INTERFERENCE MANAGEMENT FOR FEMTOCELL NETWORKS

Size: px
Start display at page:

Download "INTERFERENCE MANAGEMENT FOR FEMTOCELL NETWORKS"

Transcription

1 The Pennsylvania State University The Graduate School Department of Electrical Engineering INTERFERENCE MANAGEMENT FOR FEMTOCELL NETWORKS A Thesis in Electrical Engineering by Basak Guler c 2012 Basak Guler Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science May 2012

2 The thesis of Basak Guler was reviewed and approved* by the following: Aylin Yener Professor of Electrical Engineering Thesis Adviser Vishal Monga Assistant Professor of Electrical Engineering Kultegin Aydin Professor of Electrical Engineering *Signatures are on file in the Graduate School.

3 iii Abstract This thesis proposes methods for applying the idea of Interference Alignment (IA) in femtocell networks. In the first method, in order to manage the uplink interference caused by macrocell users (MU) at the femtocell base stations (FBS), cooperation between macrocell users with the closest femtocell base stations is used to align the received signals of macrocell users in the same subspace at multiple FBSs simultaneously. The proposed method achieves IA while providing the QoS requirements of macrocell users, in terms of minimum received SINR at the macrocell base station (MBS). With this approach, the BER performance of femtocell users is shown to improve, while maintaining the quality of the communication channel of macrocell users. In the second method, an interference limited multi-tier multiuser MIMO cellular uplink is considered. Specifically, an interference management scheme is proposed where interference from subsets of macrocell users is aligned at the femtocell base stations in order to ensure acceptable service for the femtocell users. The scheme employs interference alignment at each femtocell base station, to the set of macrocell users that are causing the high interference specifically at that FBS, and is termed selective IA. The proposed IA algorithm determines the interference subspaces at each FBS and precoders for each macrocell user in a distributed fashion.

4 iv Table of Contents List of Figures vi Acknowledgments viii Chapter 1. Introduction Chapter 2. Background Femtocells: Home Base Stations Interference Alignment Minimum Leakage Interference IA Max SINR Alternating Minimization Minimum Mean Squared Error IA Least Squares Approach for IA Chapter 3. Interference Alignment for Cooperative MIMO Femtocell Networks Introduction System Model Interference Alignment with Successive SDP Relaxations Minimum sum MSE with Coordinated Zero-Forcing Minimum sum MSE without Zero Forcing Simulation Results

5 v Chapter 4. Distributed Multiuser MIMO Interference Alignment Introduction Distributed Interference Alignment for the K-user Interference Channel Distributed Interference Alignment with Imperfect Channel Information Distributed Interference Alignment for Tiered Networks Chapter 5. Selective Interference Alignment for MIMO Femtocell Networks Introduction System Model Macrocell User Selection for Interference Alignment Selective Distributed Interference Alignment for Tiered Networks Convergence Analysis and Discussion Simulation Results Chapter 6. Conclusion

6 vi List of Figures 2.1 A basic femtocell network Comparison of coverage areas of various cell sizes Spectrum access for femtocells Sources of Interference for a Tiered Network Dominant macrocell interferer K user interference channel Interference Alignment in a 3 user interference channel Alternating Minimization System model with a single MBS and 3 femtocell groups Model for a case of 2 macrocell users and 2 FBSs, each with 2 users Convergence results of the SDP-IA Algorithm Average BER of the femtocell users with and without SDP-IA Algorithm Number of macrocell users that can be aligned subject to min SINR requirement at the MBS Average BER of the femtocell users with SDP-IA Algorithm with MMSE precoding/decoding for femtocell users Convergence of the Distributed IA Algorithm Convergence of the Distributed IA Algorithm for Imperfect Channel Information

7 vii 5.1 System model for a single MBS and multiple FBSs Channel Model for 3 FBSs, with 2 FUs in each femtocell and 2 MUs Convergence results of the Selective-IA Algorithm Percentage of FUs with a particular BER with the proposed algorithm Average BER of the femtocell users wrt. number of macrocell interferers 84

8 viii Acknowledgments First, I would like to thank my advisor Dr. Aylin Yener for her guidance throughout my Master s studies. I want to thank her for introducing me to the exciting field of wireless communications. Her knowledge, experience and patience have been invaluable for the completion of this thesis. I would like to thank Dr. Vishal Monga for taking the time to serve on my thesis committee. I would also like to express my gratitude to the members of the Wireless Communications and Networking Laboratory for their help and their friendship, and for the valuable discussions. Thanks to all my friends who have been with me during the good and the difficult times, and for becoming my family away from home. I would like to thank Damien for his loving support. Many thanks to Peter Dinklage for turning the short breaks from work into an epic experience. I would like to thank my grandmothers, my grandfather and my brother. Lastly, I would like to say special thanks to my parents, Fatih and Hidayet Guler, for their love and support during my entire life.

9 1 Chapter 1 Introduction Next generation wireless networks are designed to provide high data rates to meet subscriber demands. Femtocells are a promising direction to increase the data rate for home users while reducing the load on the cellular (macrocell) network [1]. They require no infrastructure as they are plug and play devices that are connected to the conventional internet backhaul [2]. Femtocells operate in the licenced band, and consequently have to share the radio resources and coexist with the cellular network. Solutions proposed to guarantee coexistence range from partitioning the frequency resources between the two networks, to allowing cellular (macrocell) users to be served by femtocell base stations [1]. Management of cross interference in this two-tier network is of utmost importance. In the uplink, in particular, a macrocell user operating in the same band as femtocell users may cause unacceptably high interference levels, if it is close to the femtocell base station supporting the aforementioned femtocell users, and far away from its own macrocell base station. Additionally, the fact that femtocells can be deployed in an ad hoc fashion anywhere within a macrocell (and can be removed as easily) adds to the challenge of interference management and renders jointly optimal design of the two networks impractical. In order to manage the uplink interference caused by the macrocell users at the femtocell base stations (FBS), joint detection or interference cancellation may be used.

10 2 Joint detection may not be preferred due to privacy issues and the limited backhaul provided by the Internet service provider (ISP) to the femtocells. Interference cancellation methods such as zero forcing requires as many antennas at the FBS as the number of signals to be cancelled, which may be impractical in dense urban areas since only a limited number of antennas can be employed at the FBSs. We posit that a more viable alternative is by means of coordination between multiple FBS and the macrocell users that are causing high interference to this group of FBSs. Specifically, using the principle of interference alignment (IA), we can align the received signals from macrocell users in a lower dimensional subspace at multiple FBSs simultaneously, and use the remaining degree of freedoms to improve the performance of the femtocell users. While interference alignment helps the femtocell users to eliminate macrocell interference, this should not be at the expense quality of service (QoS) for the macrocell users. Our approach is that macrocell users apply interference alignment with individual SINR constraints at the MBS, thus making sure their QoS requirements are met. To solve this problem, in the first section, we propose an algorithm that uses successive semidefinite programming (SDP) relaxations, which will be referred as SDP-IA algorithm. After interference alignment, a precoding-decoding scheme is used at the FBSs which minimizes the sum MSE of the femtocell users with coordinated zero forcing to eliminate macrocell interference. Consequently, the quality of service/performance of the femtocell users is improved without diminishing the quality of service of the macrocell users. The numerical results demonstrate that the benefits of the proposed IA algorithm, and that these benefits increase as the number of interfering macrocell users increase. The number of macrocell users that can be aligned simultaneously depends on the minimum

11 3 SINR requirements at the MBS, more users can be aligned when the minimum SINR requirements are decreased. In the first algorithm, we used beamformers as precoders of mobile users to reduce the complexity of the interference alignment problem, in which all the precoders of the macrocell users are determined by solving a centralized problem. As a result, as the number of FBSs and the macrocell interferers in the network increased, the process time required for determining the precoders increased tremendously, and caused feasibility problems. In order to solve the centralized algorithm, the channel information from all the macrocell users to the MBS and to the FBSs they are interfering, has to be gathered by a central processor, and after solving the problem, the determined precoders should be sent back to the macrocell users, which is not preferred due to the excessive load it will cause on the macrocell network, as one of the main reasons for introducing femtocells was to reduce to load on the macrocell network. In order to reduce the complexity of the problem, in the second section we propose a distributed algorithm that is applicable for interference alignment in tiered networks. We do not have unitary assumptions on the precoders or the interference subspaces, and therefore the proposed IA problem can eaxily be turned into a QCQP, and applicable for adding extra constraints, such as minimum SINR constraints for the macrocell users. This algorthm is distributed in the sense that, the users will decide on their precoders individually and only partial amount of information exchange is necessary between macrocell users and the FBSs. In the last chapter, a selective interference alignment method is proposed. In this new method we again consider the uplink of a femtocell network. However, the area considered in this case is the whole macrocell coverage area with all the femtocell and

12 4 macrocell users, instead of a small group of femtocell base stations and the macrocell users close to them. The reason for this new approach is the fact that, in a real scenario where femtocell and macrocell users are distributed randomly around the macrocell coverage area, the set of high interferers at each FBS is different, and choosing a set of FBSs and macrocell users and applying IA at only this small group is suboptimal, as the macrocell users that are at the edge of the femtocell group may actually be causing higher interference to another neighboring FBS that is close to that macrocell user but not in the femtocell group. In order to solve this problem, at a specific FBS, we align the macrocell users that are causing very high interference at that FBS, which may be different then other femtocells. For this purpose, we have set two different thresholds, one is the minimum interference threshold defined for each FBS, and the second one is the maximum number of users that can be aligned at a FBS, which is limited by the number of dimensions, such as antennas. At each FBS, we choose the set of macrocell users that are causing higher interference then the predefined interference threshold, and call this the set of high interfering MUs for that femtocell. If the number of users to be aligned exceeds the number of dimensions available for interference alignment, we drop the user that is causing the least interference out of the set, which is continued until the number of MUs in the set is decreased to the maximum number of interferers allowed for interference alignment. Then the proposed distributed algorithm is applied to the macrocell users. The advantage of using the proposed algorithm in the tiered scheme is that, the proposed algorithm uses only the precoders of the macrocell users for achieving interference alignment, which in fact helps to increase the performance of the femtocell users, and the decoders of the macrocell users can be used to increase their

13 5 own performance. The results show that the proposed method helps the femtocell users to achieve better performance then they would have without the interference alignment, and there is no significant degrade on macrocell users performance. The performance criteria considered in this thesis is average bit error rate (BER).

14 6 Chapter 2 Background Interference management has been an important design element for multiuser systems in the past two decades. Judicious receiver design for CDMA systems provides effective interference cancellation [3]. Besides multiuser detection, power control [4], and joint design of transmitters and receivers [6, 5, 15] offer optimal interference mitigation in interference limited systems. While the aforementioned techniques have been primarily designed for multi-transmitter single receiver (multiple access) systems, interference alignment has recently been proposed for multi-transmitter multi-receiver (interference) networks and has been shown to achieve the maximum degrees of freedom for the K- user interference channel [7]. For practical scenarios, distributed algorithms have been proposed for interference alignment; these include minimizing the leakage interference and using channel reciprocity [8], minimizing MSE [9], or alternating minimization [10]. In this thesis, we take the viewpoint of managing the interference caused by the macrocell users to the uplinks of femtocells in their vicinity by aligning their signals. We leverage the recent advances in interference alignment and base station cooperation (for the femtocells) in order to put forward a practically relevant yet close to optimal design of this two-tier network.

15 7 2.1 Femtocells: Home Base Stations Femtocells are small base stations designed mainly for indoor use, to provide high data rates for next generation wireless cellular networks [1]. They emerged from the fact that next generation wireless networks should be designed to provide very high data rates, as data applications require higher data rates then the voice applications. They are low cost plug and play devices purchased by the subscribers, providing coverage to a small area where they are installed [2]. Decreasing the cell size will have the effect of increasing the capacity of the wireless network, and the load on the macrocell network will be reduced, and fewer macrocell base stations will be required in the wireless network, as the femtocell users will now be served by their femtocell base stations. In a basic femtocell network as given in Fig.2.1, the femtocell base station is connected to the internet broadband router. The fact that femtocell users (FU) utilize the internet backhaul reduces the load on the macrocell network, enabling the resources to be allocated to the truly mobile users. Another reason for employing femtocells is to increase the coverage, due to the poor indoor coverage experienced with current wireless standards and even no coverage in rural areas. As the femtocells are designed mainly for indoor use, and are connected to the internet backhaul instead of the macrocell network, they can operate and provide cellular coverage even in areas that has no cellular backhaul, but only the internet backhaul. Another reason for femtocells becoming popular among the wireless operators is that they require no infrastructure, as they are purchased and installed by the end user.

16 8 Fig A basic femtocell network

17 9 Fig Comparison of coverage areas of various cell sizes This fact reduces the construction and maintenance costs. The comparison of the coverage areas for different cell types [25] is given in Fig.2.2. The difference between the femtocells and other cell types is the fact that picocells, microcells and macrocells are constructed and maintained by the network operator, which makes it possible to employ centralized interference management and scheduling methods. The femtocells are installed by their own users, and the randomness of their locations require more sophisticated interference management methods to be employed, which should be adaptable to their environment. Femtocells are low power devices, and are designed to operate close to the mobile user they are serving. As a result the battery life of the mobile devices are higher when they are using femtocells for communication. It is preferred for the femtocells to share the frequency band with the existing macrocell network, as the licensed band is highly populated, and frequency is a scarce

18 10 Fig Spectrum access for femtocells resource. The spectrum access types for femtocells are shown in Fig.2.3. There are mainly three access types; dedicated, co-channel and hybrid [25]. In the dedicated access type, the femtocells and the macrocell have separate frequency bands, which increases the interference management performance, but it not preferred due to the inefficient use of the frequency spectrum. In the co-channel access type, the femtocells and the macrocell operate in the same frequencies, which increases the frequency reuse, but requires advanced interference management techniques to be employed due to co-channel interference. In the hybrid spectrum access, separate frequency bands are allocated to the femtocells and the macrocells as long as the load on the macrocell network is not very high. When there is excessive load in the macrocell network, some macrocell users are allowed to use the frequency bands of the femtocells. This notion brings another

19 11 idea for the access permissions for the femtocell and macrocell users, which is called the open and closed access. In the open access, all subscribers registered with an operator can access all base stations, whether it is a femtocell base station or a macrocell base station. In the closed access, only a limited number of users are permitted to access the access point. The performance of femtocell open and closed access from both femtocell owner and network operator s point of view is evaluated in [29]. The importance of sharing the frequency resources between the two tiers, combined with the ad hoc nature of femtocells, make cross tier interference management challenging, and the centralized solutions impractical. In this thesis, we consider this interference management problem, concentrating on the uplink interference caused by the macrocell users (MU) at the femtocell base stations (FBS), which may be destructive when the MU is far from the macrocell base station (MBS) and close to the FBS, thereby transmitting with high power as shown in Fig.2.4. MU close to a FBS is called a dominant interferer, as shown in Fig Interference Alignment The capacity characterizations of many distributed wireless channel models such as the interference channel in Fig.2.6 are still open problems in the literature. As a result, in order to approximate the capacity of these networks a notion called degrees of freedom is defined, which is also referred to as multiplexing gain [26]. In [28] it was shown that the sum capacity of the K-user interference channel with frequency-selective

20 12 Fig Sources of Interference for a Tiered Network

21 13 Fig Dominant macrocell interferer (or time varying) channel coefficients is as follows: C(SN R) = (K/2)log(SN R) + o(log(sn R)) (2.1) where K/2 denotes the degrees of freedom and SNR is defined as the total transmit power of all the transmitters in the network when the local noise power at each node is normalized to unity, and the achievable scheme is based on the idea of interference alignment [7]. The K user interference channel is as shown in Fig.2.6. In this channel model, each user is communicating with its intended receiver while interfering with K 1 other users. Each transmitter has N t transmit antennas and each receiver has N r receive antennas. The N r N t matrix H ij denotes the matrix of individual channel gains from transmitter j to receiver i. The aim at each receiver is to find a way to eliminate the effects of interfering users by sacrificing minimum number of dimensions

22 14 Fig K user interference channel so that it can correctly decode the data streams sent from the intended receiver. This is done by aligning all the interfering users signals in a lower dimensional subspace at each receiver simultaneously, as shown in Fig.2.7 for a 3 user interference channel. The importance of interference alignment lies in the fact that, as shown in [7], it enables to achieve the maximum degrees of freedom that can be achieved in a K user interference channel, which was shown to be K/2 in [28]. In the proposed interference alignment scheme, each transmitter uses a precoding matrix V j to enable interference alignment of its own transmitted signal at the nonintended receivers simultaneously. Each receiver uses a decoding matrix U i in order to eliminate the unintended signals received in the lower dimensional subspace (i.e. zero forcing) and allowing enough degrees of freedom to decode all of the data streams from the intended transmitter. The received signal at

23 15 Fig Interference Alignment in a 3 user interference channel

24 16 the i th receiver is as given in the following: K y i = H ij V j s j + n i (2.2) j=1 where V j denotes the N t d j precoding matrix, s j is the (d j 1) vector of independently encoded symbols, d j is the number of information bits transmitted by the j th user. The noise received at the i th receiver is represented by n i, which consists of independent zero mean Gaussian random variables with E{n i n H i } = σ 2 I, and ni H denotes the Hermitian transpose of the vector n i. The conditions at the receivers for interference alignment are given as: H 12 V 2 = H 13 V 3 =... = H 1K V K H 21 V 1 = H 23 V 3 =... = H 2K V K (2.3). H K1 V 1 = H K2 V 2 =... = H K(K 1) V (K 1) The signal at the i th receiver after the decoding matrix is applied is given as: Y i = U i Yi (2.4)

25 where U i denotes the conjugate transpose of the matrix Ui. For perfect interference alignment, the resulting system should ensure the following conditions: 17 U i Hij V j = 0 j i rank(u i Hii V i ) = d i (2.5) From these conditions it can be seen that, at each receiver the interference should be aligned into the null space of the decoding matrix and the rank of the resulting matrix should be equal to the number of symbols to be detected, in order to detect them properly. As a result the effective channel for user i can be represented as: Ỹ i = U i Hii V i s i + U i ni (2.6) The exact interference alignment scheme for a 3 user interference channel was proposed in [7]. However, the exact closed form solutions for channels with number of users K > 3 are not known. As a result, many distributed algorithms have emerged to find the approximate precoding and decoding matrices that take into account different objective functions, including minimizing the leakage interference [8], alternating minimization [10], maximizing the SINR [8], or minimizing MSE [9]. The common point of these algorithms is that they update the precoding/decoding matrices for a given decoding/precoding matrix set iteratively, and they are not jointly convex over all precoding and decoding matrices. As a result they cannot guarantee convergence to the global

26 18 optima, and may end up at a local optima. Some of these algorithms are discussed in the following sections. Recently it was shown that also the real-world performance of interference alignment outperforms conventional multiuser communication methods such as TDMA [27]. The measurements in [27] are done using practical MIMO channels and the exact interference alignment scheme for a 3 user interference channel and distributed algorithms were implemented using a 2 2 MIMO testbed. Since the distributed algorithms converge to the local optima due to their nonconvex nature, the algorithms were implemented for a number of different starting points and the one giving the best local minima was chosen Minimum Leakage Interference IA This algorithm seeks the perfect interference alignment by minimizing the leakage interference [8], calculated as the trace of the interference covariance matrix, given in the following equation: min (U k ) U k =I trace(u k Qk U k ) (2.7) where Q k = K j=1 P s H kj V j V j Hkj and Ps is the transmitted symbol power. j k denotes the identity matrix. I The algorithm aims to align all the interfering signals in a lower dimensional subspace at each receiver simultaneously. At each iteration the coding matrices are updated in such a way that the signal is transmitted along the n smallest eigenvectors, i.e. in the directions of the n smallest eigenvalues of the leakage interference matrix. Then the roles of the transmitters and receivers are changed by exploiting channel reciprocity,

27 the precoders now become decoders and the decoders now become precoders, and the same procedure is applied to the new precoder/decoders. 19 This algorithm was shown to converge in [8]. However, due to the nonconvex nature of the problem, one cannot assure the algorithm converges to the global optima, it will possibly converge only to a local optima. Although the algorithm provides good performance in high SINR (Signal to Interference plus Noise Ratio), for low to moderate SNR values, it was shown in [8] that the performance is poor since the objective function does not aim to maximize the received SINR at the intended receiver Max SINR Max SINR algorithm was developed in [8] due to the fact that Minimum Leakage Interference algorithm only seeks perfect interference alignment and does not consider about the received SINR values. Max SINR algorithm aims to maximize the received SINR at each receiver, by find the unit vector U l k that maximizes the SINR in the lth stream of the k th user, which is given as: max SINR kl = (Ul k ) H kk V l k (Vl k ) H kk U l k P s U l k B kl Ul k (2.8) where B kl = K j=1 P s d j d=1 H kj Vd j (Vd j ) H kj P s H kk + I N t and I Nt is the N t N t identity matrix. P s is the transmitted symbol power. Again the precoder and decoders are updated iteratively and the role of the precoder and decoders are changed at each iteration.

28 Alternating Minimization Alternating minimization is the technique to tackle the optimization problems in which finding an exact solution over two variables is difficult, but optimizing over one variable while fixing the others is relatively easy. In this method the aim is to find the minimum of d(a, B) where d denotes the distance in any metric space. And A and B denote the sets of optimization variables. Here the sequence {A k } (k=0) and {B k } (k=0) are obtained by an iterative algorithm, that is first fixing B k and optimizing over A and then fixing A k and optimizing over B. The algorithm, illustrated in Fig.2.8, is given as follows: A k+1 = argmin A A B k+1 = argmin B B d(a, B k ) d(b k+1, A) (2.9) A A, B B k (2.10) The idea of using alternating minimization for interference alignment was proposed in [10]. The received signals are projected onto a subspace which is called the interference subspace. The objective of the algorithm is to minimize the sum of the distances between the projected signals to the interference subspace, in which the sum is done over the interfering users. The precoding and orthogonal projection matrices F l and C k for the l th transmitter and k th receiver are find via alternating minimization, which is shown to converge, but whether it converges to the global optima is unknown. The received

29 21 Fig Alternating Minimization signal at the k th receiver is given as follows: y k = H kk F k s k + l k H kl F l s l + n k (2.11) The objective function is represented as: min F l F l =I, l C k C k =I, k K K H kl F l C k C k H kl F l 2 F k=1 l=1 l k (2.12) In this approach the optimization is done over 2K variables, where 2K 1 variables are temporarily fixed and the optimization is done over the remaining variable.

30 Minimum Mean Squared Error IA Another distributed algorithm [9] aims to minimize the sum mean squared-error by using precoding/decoding matrices at each transmitter/receiver, which is given as: min v 1,...,v k g 1,...,g k K ϵ k (2.13) k=1 where ϵ k = E{ ŝ k s k 2 }, v k is the precoding vector of the k th user, g k is the decoding vector for the k th user, and ŝ k is the estimated symbol of the k th user Least Squares Approach for IA Least squares approach [11] uses an alternative representation for interference alignment given as: C(H 12 w 2 ) = C(H 13 w 23 ) = = C(H 1K w K ) (2.14) C(H 21 w 1 ) = C(H 23 w 3 ) = = C(H 2K w K ) (2.15). C(H K1 w 1 ) = C(H K2 w 2 ) = = C(H K(K 1) w (K 1) ) (2.16) where C(.) represents that the interfering signals span the same subspace. For each specific receiver, each interfering signal is represented by a linear combination of the

31 23 remaining interfering signals using scalar coefficients, which is given as: H 12 w 2 = α 13 H 13 w 23 = = α 1K H 1K w M (2.17) H 21 w 1 = α 23 H 23 w 3 = = α 2K H 2K w K (2.18). H K1 w 1 = α K2 H K2 w 2 = = α K(K 1) H K(K 1) w (K 1) (2.19) Using the precoders and the associated coefficients, the interference alignment expressions can be combined in a single matrix representation as: Hw = 0 (2.20) where w = [w T 1 w T 2... wt K ]T and 0 H 12 α 13 H H 12 0 α 14 H H α 1K H 1K H = H K1 α K2 H K H K α K(K 1) H K(K 1)

32 24 The proposed approach for finding the precoding matrices is making the norm of this expression as close to zero as possible, from which follows the notion of least squares approach for interference alignment [11]: min w w=1 Hw (2.21) As a result of the unitary assumption on w, the solution for w is the eigenvector of Hw that corresponds to its smallest eigenvalue.

33 25 Chapter 3 Interference Alignment for Cooperative MIMO Femtocell Networks 3.1 Introduction In this chapter, we propose a method for mitigating the uplink interference caused by the macrocell users (MU) at the femtocell base stations (FBS). The proposed method uses interference alignment for restricting the received interference from MUs to a lower dimensional subspace at multiple FBSs simultaneously. Our approach considers improving the performance of femtocell users by aligning the macrocell interference, while satisfying the QoS requirements of the macrocell users, in terms of the minimum SINR required at the macrocell base station. As a solution, we propose to use SDP relaxations with eigenvector approximation for interference alignment in tiered networks with SINR constraints. The remainder of the chapter is organized as follows: In Section II, we introduce the system model. Interference alignment for macrocell users is presented in Section III. Section IV describes the precoding and decoding scheme for femtocell users. In Section V, the numerical results and simulations are discussed. We conclude the chapter in Section VI. The notation used in this chapter is as follows: We use lower (upper) bold case letters for vectors (matrices). X H is used to denote the Hermitian transpose, X

34 as the pseudo-inverse of matrix X, and for the Kronecker product. Finally, trace(x) represents the trace of matrix X System Model We consider an uplink femtocell network as shown in Fig. 3.1 consisting of a macrocell base station (MBS) at the center with N o receive antennas. The macrocell coverage area is partitioned into smaller cells of fixed radius in which the mobile users and base stations can cooperate with each other. Suppose such a group consists of F femtocell base stations (FBS), with U f users in the f th femtocell (FU) and M macrocell users (MU). We have N t transmit antennas at each mobile device and N f receive antennas at the f th FBS. Then the signal received at the k th FBS is given by U k y k = H k ki wk i sk i i=1 U F f + H f ku wf u sf u + M H o km wo m so m + n k (3.1) f=1 u=1 m=1 f k where H f ku denotes the channel from the uth user of the f th femtocell to the k th FBS, H o km is the channel from the mth MU to the k th FBS, w f u and sf u are the precoding vector and the message bit of the u th user of the f th femtocell, w o m and so m are the precoding vector and message bit of the m th MU, n k is a vector of independent zero mean Gaussian random variables with E{n k n H k } = σ 2 I. The channels considered are Rayleigh fading channels and the path loss is modeled using the ITU-R channel model [23]. We used rank 1 precoders to reduce the complexity of the algorithm and to avoid

35 27 Macrocell User Femtocell User Macrocell Base Station Femtocell Base Station Fig System model with a single MBS and 3 femtocell groups Fig Model for a case of 2 macrocell users and 2 FBSs, each with 2 users

36 28 feasibility problems due to the large number of users. We assume s f u and so m = ±1 for u = 1,..., U f, f = 1,..., F, and m = 1,..., M. An example model is given in Fig.3.2 for 2 macrocell users and 2 FBSs, each with 2 users. 3.3 Interference Alignment with Successive SDP Relaxations For simplicity, we will neglect the uplink interference caused at a FBS by the users of other femtocells, and consider only the (dominant) interference caused by the macrocell users. We will use the condition for interference alignment proposed in [11]: H o 11 wo 1 = α 12 Ho 12 wo 2 = = α 1M Ho 1M wo M (3.2) H o 21 wo 1 = α 22 Ho 22 wo 2 = = α 2M Ho 2M wo M (3.3). H o F 1 wo 1 = α F 2 Ho F 2 wo 2 = = α F M Ho F M wo M (3.4) where α fm is a constant and the equations denote that all interfering users span the same column space at each FBS. That is, each interfering signal is represented by a linear combination of other interfering signals, represented by different coefficients. Using the precoders and the associated coefficients, expressions (3.2)-(3.4) can be combined in a single matrix representation as in (3.5), as proposed in [11]. Then the condition of perfect interference alignment is equal to the expression being equal to zero (3.5). Therefore, one approach for finding the interference aligning precoding matrices is to make the norm of this expression as close to zero as possible as in (3.6), from which follows the notion of

37 29 least squares approach for interference alignment, proposed in [11]. Hw = 0 (3.5) where H o 11 H o 11. H o 11 H =. H o F 1 H o F 1. H o F 1 α 12 H o α 13 H o α 1M H o 1M α F 2 H o F 2 0 α F 3 H o... 0 F α F M H o F M ] T w = [w o1 T w o2 T w o3 T... w om 1 T w om T We will follow this definition for interference alignment, however, our solution follows a SDP relaxation method to solve the norm minimization problem that satisfies the individual minimum SINR requirements for each macrocell user, which incorporates successive SDP relaxations [20] and rank-one approximation. The interference alignment problem in (3.5) can be regarded as a least squares (LS) problem [11]. In fact, (3.5)

38 30 denotes a set of linear equations and the LS approach is a conventional method to approximate the solution. In order to satisfy QoS requirements, we define an individual SINR constraint for each macrocell user. The problem is thus given by: minimize w o 1,...,wo M Hw subject to SINR i γ i (3.6) (w o i )H w o i Po i i = 1,..., M where P o i denotes the maximum transmit power of the ith macrocell user, γ i denotes the minimum SINR threshold of the i th macrocell user, and SINR i is given as in (3.8). SINR i = (w i o )H (H o oi )H H o oi wo i Mn=1 (w n o )H (H o on )H H o on wo n + B + (3.7) σ2 n i where U F f B = (w f u )H (H f ou )H H f ou wf u f=1 u=1 (3.8) where H o on denotes the channel from the nth macrocell user to the MBS. Then the equivalent problem can be written as: minimize w o 1,...,wo M subject to trace(rw) trace(( R oi γ i R on )W) γ i σ 2 n i

39 31 trace((diag(e i ) I (Nt N t ) )W) Po i rank(w) = 1 (3.9) W 0, i = 1,..., M where R = H H H, W = ww H, R on = (H o on )H H o on, Ron = diag(e n ) R on, and e n = [ ] T is an (M 1) unit vector with 1 as the n th element and zeros elsewhere. I (Nt N t ) denotes the (N t N t ) identity matrix. By relaxing the rank 1 constraint, we obtain the semidefinite relaxation [19] of the problem: minimize w o 1,...,wo M subject to trace(rw) trace(( R oi γ i R on )W) γ i σ 2 n i (3.10) trace((diag(e i ) I (Nt N t ) )W) Po i W 0, i = 1,..., M The SDP in (3.9) can be solved efficiently by software such as SeDuMi[13]. In case the resulting solution has a higher rank than one, we can use eigenvector approximation [12], in which the vector w is approximated as the eigenvector q 1 corresponding to the largest eigenvalue of W, scaled by the square root of the largest eigenvalue of W, λ 1, i.e., W = ww H = i λ i q i q H i (3.11) w = λ 1 q 1 (3.12)

40 After this step, the coefficients are determined using the conditions in (3.2)-(3.4) [11], as given by: 32 α km = (H o km wo m ) (H o k1 wo 1 ) (3.13) (H o km wo m ) = ((H o km wo m )H (H o km wo m )) 1 (H o km wo m )H (3.14) 3.4 Minimum sum MSE with Coordinated Zero-Forcing Femtocell users can either cooperate and contribute interference alignment, which may increase the load on the backhaul or they can try to improve their own performance. As a suitable precoding-decoding scheme for the second case, each FBS may try to minimize the sum MSE of its own users, by zero-forcing the aligned macrocell users. A coordinated zero-forcing beamforming for SINR maximization was proposed in [14], which uses the ideas from [15] and [16]. We will use a precoding-decoding scheme that minimizes the sum MSE at the FBSs while zero-forcing the aligned interference from the macrocell users. The estimated bit of the j th user of the k th femtocell is given as: U k ŝ k j = (g k j )H H k F U ki wk i sk i + f (g k j )H H f ku wf u sf u i=1 f=1 u=1 f k M + (g k j )H H o km wo m so m + (gk j )H n k (3.15) m=1 where g k j is the decoding vector for the jth user of the k th femtocell. Since the interference caused by other femtocells are very small compared to the intracell interference,

41 for simplicity we will regard intercell femtocell interference as noise, which is given as: 33 U F f ñ k = H f ku wf u sf u + n k (3.16) f=1 u=1 f k Using the conditions in (3.2)-(3.4) and (3.15), the minimum sum MSE at the FBS problem can be formulated as: minimize w k 1,...,wk U k U k ŝ k j sk j 2 j=1 g k 1,...,gk U k (3.17) subject to (g k j )H H o k1 wo 1 = 0 (w k j )H w k j Pk j j = 1,..., U k or equivalently minimize w k 1,...,wk U k U k j=1 [ (g k j )H H k kj wk j 1 2 g k 1,...,gk U k U k ] + (g k j )H H k ki wk i 2 + g k j 2 2 σ2 i=1 i j (3.18) subject to (g k j )H H o k1 wo 1 = 0 (w k j )H w k j Pk j j = 1,..., U k

42 where P k j is the maximum transmit power of the jth user of the k th femtocell, and 34 E{ñ k (ñ k ) H } = σ 2 I. The zero forcing constraint in (3.18) implies that g k j should be in the null space of (H o k1 wo ) [17], from which we can define a decoding vector such as: 1 g k j = Uo k vk j (3.19) where [U 0 k U1 k ]Λ k V k is obtained from the SVD of Ho k1 wo 1 and the columns of Uo k is a nullspace basis of H o k1 wo 1. If we let (U0 k )H H k kj = H k, the problem in (3.18) is equal kj to: minimize w k 1,...,wk U k U k j=1 [ (v k j )H Hk kj wk j 1 2 v k 1,...,vk U k U k + i=1 i j (v k j )H Hk ki wk i 2 + v k j 2 2 σ2 ] (3.20) subject to (w k j )H w k j Pk j j = 1,..., U k The problem in (3.20) is convex in w k j if the all other vk j are fixed, and convex in vk j if all other w k j are fixed. Using this property, we can use an iterative algorithm by first fixing the decoding matrices and obtaining the precoding matrices, then fixing the precoding matrices to obtain the decoding matrices. An iterative procedure for obtaining the optimal coding vectors is used in [18] where the transmit precoding vector had unit norm. After writing the Lagrangian for the problem in (3.20), from the KKT conditions

43 35 we have the optimal precoding and decoding vectors as: ( U k ) 1 v k j = ( H k ki wk i )( H k ki wk i )H + σ 2 I Hk kj wk j i=1 ( U k ) 1 w k j = ( H k kj )H v k i (vk i )H Hk kj + µk j I ( H k kj )H v k j i=1 (3.21) (3.22) for j = 1,..., U k. We determine µ k j such that (wk j )H w k j = Pk j. 3.5 Minimum sum MSE without Zero Forcing In this section we apply MMSE precoding/decoding for the femtocell users, without zero forcing the aligned interference from the macrocell users first. For the new approach, the problem in (3.17) becomes: Using the conditions in (3.2)-(3.4) and (3.15), the minimum sum MSE at the FBS problem can be formulated as: minimize w k 1,...,wk U k g k 1,...,gk U k U k ŝ k j sk j 2 j=1 (3.23) subject to (w k j )H w k j Pk j j = 1,..., U k

44 where the zero forcing requirement for the macrocell users is removed from the problem in (3.17). The problem can also be represented in the following form: 36 minimize w k 1,...,wk U k g k 1,...,gk U k U k [ U k (g k j )H H k kj wk j (g k j )H H k ki wk i 2 j=1 i=1 i j M ] + (g k j )H H o km wo m 2 + g k j 2 2 σ2 m=1 (3.24) subject to (w k j )H w k j Pk j j = 1,..., U k where the problem in (3.24) is convex in w k j if the all other gk j are fixed, and convex in g k j if all other wk j are fixed. We can again make use of this to obtain an iterative algorithm by first fixing the decoding matrices and determining the precoding matrices, then fixing the precoding matrices determining the decoding matrices. An iterative procedure is used in [18] to obtain the coding vectors where the transmit precoding vector had unit norm. After writing the Lagrangian and using the KKT conditions, the resulting expressions for the optimal precoders and decoders of the femtocell users are found to be as follows: ( U k g k j = (H k M ) 1 ki wk i )(Hk ki wk i )H + (H o km wo m )(Ho km wo m )H + σ 2 I H k kj wk j i=1 m=1 (3.25) ( U k ) 1 w k j = (H k kj )H g k i (gk i )H H k kj + µk j I (H k kj )H g k j i=1 (3.26)

45 37 for j = 1,..., U k. We determine µ k j such that (wk j )H w k j = Pk j. 3.6 Simulation Results Simulations are performed to compare the performance of the proposed macrocell interference alignment with that of the setting where macrocell users (MU) minimize their sum MSE at the MBS, without regard to femtocell users (FU). The MBS has a coverage radius of 2km, the group of FBSs close to each other is denoted by an area of 150m radius, placed randomly according to a uniform distribution within the coverage radius of the MBS, and the MUs within this area apply interference alignment jointly. We consider 3 FBSs each with a radius of 30m coverage. Each FBS has 3 users, and each mobile user has 4 transmit antennas. FBSs have 4 receive antennas. Noise power is assumed to be 110dB. Power control at both FBS and MBS is used to compensate for the path loss. The maximum transmit power of each user is 1W. The convergence of the SDP-IA algorithm for 10 macrocell users and a minimum SINR requirement of 0.1 at the MBS is presented in Fig.3.3. The comparison of the SDP- IA with coordinated zero forcing scheme with the one with no interference alignment in terms of average BER versus the number of MUs interfering to the femtocell group is given in Fig.3.4. For the case when no interference alignment is applied, the only objective for the MUs is to minimize the sum MSE at the MBS. The number of MUs that can be aligned for different minimum SINR requirements at the MBS is depicted in Fig.3.5 for the SDP-IA with coordinated zero forcing algorithm. The results show that the performance of the FUs in terms of average BER is significantly better when compared to the case when the interfering MUs only consider

46 x leaked interference iteration Fig Convergence results of the SDP-IA Algorithm their own performance and minimize the sum MSE at the MBS. It was observed in the simulations that, the received SINR constraints of the MUs in the second case do not satisfy a minimum and may cause an outage in voice applications. The feasibility of the minimum SINR constraints is a main limitation in this system: as the minimum SINR constraints of MUs are increased, the maximum number of MUs that can be aligned simultaneously decreases significantly. The average BER of the femtocell users with respect to the number of interfering macrocell users for the SDP-IA without zero forcing algorithm is given in Fig.3.6 for a single femtocell group. From Fig.3.6 it can be seen that the average BER of the femtocell users have decreased, correspondingly their performances have improved.

47 with SDP IA Algorithm without SDP IA Algorithm 0.5 Average BER Number of interfering macrocell users Fig Average BER of the femtocell users with and without SDP-IA Algorithm Min SINR required at the MBS Number of aligned macrocell users Fig Number of macrocell users that can be aligned subject to min SINR requirement at the MBS

48 x Average BER Number of interfering macrocell users Fig Average BER of the femtocell users with SDP-IA Algorithm with MMSE precoding/decoding for femtocell users

49 41 Chapter 4 Distributed Multiuser MIMO Interference Alignment 4.1 Introduction In the previous chapter, a method for dealing with large number of macrocell users in a femtocell network was proposed. This method combined the ideas of interference alignment and semidefinite relaxation in order to restrict the macrocell interference in a lower dimensional subspace, simultaneously at multiple base stations, so that the macrocell interference could be cancelled at each femtocell base station, using a relatively small number of antennas compared to the number of interfering macrocell users. Since the femtocell devices have an ad-hoc nature, unlike the microcell and picocell networks, the interference management and scheduling cannot be done in a centralized manner. Therefore adaptive schemes should be proposed that can adjust to the current specifications of the tiered cellular network. For this purpose, in this chapter, we first define a new interference alignment algorithm that determines the precoders and the aligned subspaces iteratively. This is because using one dimensional precoders or beamformers will cause feasibility problems if we want to align a larger number of interferers. Another reason for introducing the new distributed algorithm is that the interference alignment algorithm with semidefinite relaxation operated in a centralized manner, which requires a processing unit to gather information from both tiers, and to send the resulting information back to the users, which may not be desirable in a tiered network, due to the load

50 42 it will add to the network and for security and privacy reasons. Therefore, a distributed algorithm is highly desirable in these networks, especially when we are designing schemes that consider the whole femtocell network, instead of a smaller part of it, as we have considered in the previous chapters. This chapter is organized as follows. In the next section the iterative interference alignment algorithm is described for a K-user interference channel. In Section 4.3, this algorithm is considered for the case when the channel information available at the transmitters is imperfect. We generalize the distributed interference alignment algorithm for the tiered networks in Section Distributed Interference Alignment for the K-user Interference Channel In this section, the proposed multiantenna distributed interference alignment algorithm is described for a K-user interference channel as shown in Fig.2.6, with K transmitters and K receivers. The transmitter and receivers are assumed to have perfect Channel State Information (CSI). The aim is to determine the precoder of each transmitter and the interference subspace at each receiver such that the received signals from all the interfering users are restricted in a lower dimensional subspace simultaneously at each receiver. The channel considered in this section is a K-user interference channel, but the results can be extended to the two-tier systems such as femtocell networks, as will be done in Section 4.4.

Multiple Antenna Processing for WiMAX

Multiple Antenna Processing for WiMAX Multiple Antenna Processing for WiMAX Overview Wireless operators face a myriad of obstacles, but fundamental to the performance of any system are the propagation characteristics that restrict delivery

More information

Communication over MIMO X Channel: Signalling and Performance Analysis

Communication over MIMO X Channel: Signalling and Performance Analysis Communication over MIMO X Channel: Signalling and Performance Analysis Mohammad Ali Maddah-Ali, Abolfazl S. Motahari, and Amir K. Khandani Coding & Signal Transmission Laboratory Department of Electrical

More information

Lecture 8 Multi- User MIMO

Lecture 8 Multi- User MIMO Lecture 8 Multi- User MIMO I-Hsiang Wang ihwang@ntu.edu.tw 5/7, 014 Multi- User MIMO System So far we discussed how multiple antennas increase the capacity and reliability in point-to-point channels Question:

More information

Multiple Antennas. Mats Bengtsson, Björn Ottersten. Basic Transmission Schemes 1 September 8, Presentation Outline

Multiple Antennas. Mats Bengtsson, Björn Ottersten. Basic Transmission Schemes 1 September 8, Presentation Outline Multiple Antennas Capacity and Basic Transmission Schemes Mats Bengtsson, Björn Ottersten Basic Transmission Schemes 1 September 8, 2005 Presentation Outline Channel capacity Some fine details and misconceptions

More information

Dynamic Fair Channel Allocation for Wideband Systems

Dynamic Fair Channel Allocation for Wideband Systems Outlines Introduction and Motivation Dynamic Fair Channel Allocation for Wideband Systems Department of Mobile Communications Eurecom Institute Sophia Antipolis 19/10/2006 Outline of Part I Outlines Introduction

More information

NEXT generation wireless networks are expected to provide

NEXT generation wireless networks are expected to provide 2246 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL 13, NO 4, APRIL 2014 Uplin Interference Management for Coexisting MIMO Femtocell and Macrocell Networs: An Interference Alignment Approach Basa Guler,

More information

MIMO III: Channel Capacity, Interference Alignment

MIMO III: Channel Capacity, Interference Alignment MIMO III: Channel Capacity, Interference Alignment COS 463: Wireless Networks Lecture 18 Kyle Jamieson [Parts adapted from D. Tse] Today 1. MIMO Channel Degrees of Freedom 2. MIMO Channel Capacity 3. Interference

More information

EE360: Lecture 6 Outline MUD/MIMO in Cellular Systems

EE360: Lecture 6 Outline MUD/MIMO in Cellular Systems EE360: Lecture 6 Outline MUD/MIMO in Cellular Systems Announcements Project proposals due today Makeup lecture tomorrow Feb 2, 5-6:15, Gates 100 Multiuser Detection in cellular MIMO in Cellular Multiuser

More information

THE emergence of multiuser transmission techniques for

THE emergence of multiuser transmission techniques for IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 54, NO. 10, OCTOBER 2006 1747 Degrees of Freedom in Wireless Multiuser Spatial Multiplex Systems With Multiple Antennas Wei Yu, Member, IEEE, and Wonjong Rhee,

More information

A Performance Comparison of Interference Alignment and Opportunistic Transmission with Channel Estimation Errors

A Performance Comparison of Interference Alignment and Opportunistic Transmission with Channel Estimation Errors A Performance Comparison of Interference Alignment and Opportunistic Transmission with Channel Estimation Errors Min Ni, D. Richard Brown III Department of Electrical and Computer Engineering Worcester

More information

Novel Transmission Schemes for Multicell Downlink MC/DS-CDMA Systems Employing Time- and Frequency-Domain Spreading

Novel Transmission Schemes for Multicell Downlink MC/DS-CDMA Systems Employing Time- and Frequency-Domain Spreading Novel Transmission Schemes for Multicell Downlink MC/DS-CDMA Systems Employing Time- and Frequency-Domain Spreading Jia Shi and Lie-Liang Yang School of ECS, University of Southampton, SO7 BJ, United Kingdom

More information

Hype, Myths, Fundamental Limits and New Directions in Wireless Systems

Hype, Myths, Fundamental Limits and New Directions in Wireless Systems Hype, Myths, Fundamental Limits and New Directions in Wireless Systems Reinaldo A. Valenzuela, Director, Wireless Communications Research Dept., Bell Laboratories Rutgers, December, 2007 Need to greatly

More information

MIMO Channel Capacity in Co-Channel Interference

MIMO Channel Capacity in Co-Channel Interference MIMO Channel Capacity in Co-Channel Interference Yi Song and Steven D. Blostein Department of Electrical and Computer Engineering Queen s University Kingston, Ontario, Canada, K7L 3N6 E-mail: {songy, sdb}@ee.queensu.ca

More information

3432 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 53, NO. 10, OCTOBER 2007

3432 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 53, NO. 10, OCTOBER 2007 3432 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 53, NO 10, OCTOBER 2007 Resource Allocation for Wireless Fading Relay Channels: Max-Min Solution Yingbin Liang, Member, IEEE, Venugopal V Veeravalli, Fellow,

More information

Interference Alignment for Heterogeneous Full-duplex Cellular Networks

Interference Alignment for Heterogeneous Full-duplex Cellular Networks Interference Alignment for Heterogeneous ull-duplex Cellular Networks Amr El-Keyi and Halim Yanikomeroglu Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada. Email:

More information

ISSN Vol.03,Issue.17 August-2014, Pages:

ISSN Vol.03,Issue.17 August-2014, Pages: www.semargroup.org, www.ijsetr.com ISSN 2319-8885 Vol.03,Issue.17 August-2014, Pages:3542-3548 Implementation of MIMO Multi-Cell Broadcast Channels Based on Interference Alignment Techniques B.SANTHOSHA

More information

On the Value of Coherent and Coordinated Multi-point Transmission

On the Value of Coherent and Coordinated Multi-point Transmission On the Value of Coherent and Coordinated Multi-point Transmission Antti Tölli, Harri Pennanen and Petri Komulainen atolli@ee.oulu.fi Centre for Wireless Communications University of Oulu December 4, 2008

More information

Precoding and Massive MIMO

Precoding and Massive MIMO Precoding and Massive MIMO Jinho Choi School of Information and Communications GIST October 2013 1 / 64 1. Introduction 2. Overview of Beamforming Techniques 3. Cooperative (Network) MIMO 3.1 Multicell

More information

Modeling and Analysis of User-Centric and Disjoint Cooperation in Network MIMO Systems. Caiyi Zhu

Modeling and Analysis of User-Centric and Disjoint Cooperation in Network MIMO Systems. Caiyi Zhu Modeling and Analysis of User-Centric and Disjoint Cooperation in Network MIMO Systems by Caiyi Zhu A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Graduate

More information

MIMO Systems and Applications

MIMO Systems and Applications MIMO Systems and Applications Mário Marques da Silva marques.silva@ieee.org 1 Outline Introduction System Characterization for MIMO types Space-Time Block Coding (open loop) Selective Transmit Diversity

More information

REMOTE CONTROL OF TRANSMIT BEAMFORMING IN TDD/MIMO SYSTEMS

REMOTE CONTROL OF TRANSMIT BEAMFORMING IN TDD/MIMO SYSTEMS The 7th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 6) REMOTE CONTROL OF TRANSMIT BEAMFORMING IN TDD/MIMO SYSTEMS Yoshitaa Hara Kazuyoshi Oshima Mitsubishi

More information

Block Processing Linear Equalizer for MIMO CDMA Downlinks in STTD Mode

Block Processing Linear Equalizer for MIMO CDMA Downlinks in STTD Mode Block Processing Linear Equalizer for MIMO CDMA Downlinks in STTD Mode Yan Li Yingxue Li Abstract In this study, an enhanced chip-level linear equalizer is proposed for multiple-input multiple-out (MIMO)

More information

An Efficient Linear Precoding Scheme Based on Block Diagonalization for Multiuser MIMO Downlink System

An Efficient Linear Precoding Scheme Based on Block Diagonalization for Multiuser MIMO Downlink System An Efficient Linear Precoding Scheme Based on Block Diagonalization for Multiuser MIMO Downlink System Abhishek Gupta #, Garima Saini * Dr.SBL Sachan $ # ME Student, Department of ECE, NITTTR, Chandigarh

More information

ELEC E7210: Communication Theory. Lecture 11: MIMO Systems and Space-time Communications

ELEC E7210: Communication Theory. Lecture 11: MIMO Systems and Space-time Communications ELEC E7210: Communication Theory Lecture 11: MIMO Systems and Space-time Communications Overview of the last lecture MIMO systems -parallel decomposition; - beamforming; - MIMO channel capacity MIMO Key

More information

Adaptive Wireless. Communications. gl CAMBRIDGE UNIVERSITY PRESS. MIMO Channels and Networks SIDDHARTAN GOVJNDASAMY DANIEL W.

Adaptive Wireless. Communications. gl CAMBRIDGE UNIVERSITY PRESS. MIMO Channels and Networks SIDDHARTAN GOVJNDASAMY DANIEL W. Adaptive Wireless Communications MIMO Channels and Networks DANIEL W. BLISS Arizona State University SIDDHARTAN GOVJNDASAMY Franklin W. Olin College of Engineering, Massachusetts gl CAMBRIDGE UNIVERSITY

More information

Power allocation for Block Diagonalization Multi-user MIMO downlink with fair user scheduling and unequal average SNR users

Power allocation for Block Diagonalization Multi-user MIMO downlink with fair user scheduling and unequal average SNR users Power allocation for Block Diagonalization Multi-user MIMO downlink with fair user scheduling and unequal average SNR users Therdkiat A. (Kiak) Araki-Sakaguchi Laboratory MCRG group seminar 12 July 2012

More information

Multiple Input Multiple Output (MIMO) Operation Principles

Multiple Input Multiple Output (MIMO) Operation Principles Afriyie Abraham Kwabena Multiple Input Multiple Output (MIMO) Operation Principles Helsinki Metropolia University of Applied Sciences Bachlor of Engineering Information Technology Thesis June 0 Abstract

More information

Iterative Leakage-Based Precoding for Multiuser-MIMO Systems. Eric Sollenberger

Iterative Leakage-Based Precoding for Multiuser-MIMO Systems. Eric Sollenberger Iterative Leakage-Based Precoding for Multiuser-MIMO Systems Eric Sollenberger Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements

More information

03_57_104_final.fm Page 97 Tuesday, December 4, :17 PM. Problems Problems

03_57_104_final.fm Page 97 Tuesday, December 4, :17 PM. Problems Problems 03_57_104_final.fm Page 97 Tuesday, December 4, 2001 2:17 PM Problems 97 3.9 Problems 3.1 Prove that for a hexagonal geometry, the co-channel reuse ratio is given by Q = 3N, where N = i 2 + ij + j 2. Hint:

More information

The Feasibility of Interference Alignment over Measured MIMO-OFDM Channels

The Feasibility of Interference Alignment over Measured MIMO-OFDM Channels The Feasibility of Interference Alignment over Measured MIMO-OFDM Channels Omar El Ayach, Steven W. Peters, and Robert W. Heath, Jr. Wireless Networking and Communications Group The University of Texas

More information

Linear Precoding in MIMO Wireless Systems

Linear Precoding in MIMO Wireless Systems Linear Precoding in MIMO Wireless Systems Bhaskar Rao Center for Wireless Communications University of California, San Diego Acknowledgement: Y. Isukapalli, L. Yu, J. Zheng, J. Roh 1 / 48 Outline 1 Promise

More information

Coordinated Multi-Point Transmission for Interference Mitigation in Cellular Distributed Antenna Systems

Coordinated Multi-Point Transmission for Interference Mitigation in Cellular Distributed Antenna Systems Coordinated Multi-Point Transmission for Interference Mitigation in Cellular Distributed Antenna Systems M.A.Sc. Thesis Defence Talha Ahmad, B.Eng. Supervisor: Professor Halim Yanıkömeroḡlu July 20, 2011

More information

Joint Transmitter-Receiver Adaptive Forward-Link DS-CDMA System

Joint Transmitter-Receiver Adaptive Forward-Link DS-CDMA System # - Joint Transmitter-Receiver Adaptive orward-link D-CDMA ystem Li Gao and Tan. Wong Department of Electrical & Computer Engineering University of lorida Gainesville lorida 3-3 Abstract A joint transmitter-receiver

More information

Fair scheduling and orthogonal linear precoding/decoding. in broadcast MIMO systems

Fair scheduling and orthogonal linear precoding/decoding. in broadcast MIMO systems Fair scheduling and orthogonal linear precoding/decoding in broadcast MIMO systems R Bosisio, G Primolevo, O Simeone and U Spagnolini Dip di Elettronica e Informazione, Politecnico di Milano Pzza L da

More information

Robust MMSE Tomlinson-Harashima Precoder for Multiuser MISO Downlink with Imperfect CSI

Robust MMSE Tomlinson-Harashima Precoder for Multiuser MISO Downlink with Imperfect CSI Robust MMSE Tomlinson-Harashima Precoder for Multiuser MISO Downlink with Imperfect CSI P. Ubaidulla and A. Chockalingam Department of ECE, Indian Institute of Science, Bangalore 560012, INDIA Abstract

More information

Sum-Rate Analysis and Optimization of. Self-Backhauling Based Full-Duplex Radio Access System

Sum-Rate Analysis and Optimization of. Self-Backhauling Based Full-Duplex Radio Access System Sum-Rate Analysis and Optimization of 1 Self-Backhauling Based Full-Duplex Radio Access System Dani Korpi, Taneli Riihonen, Ashutosh Sabharwal, and Mikko Valkama arxiv:1604.06571v1 [cs.it] 22 Apr 2016

More information

RESOURCE MANAGEMENT FOR WIRELESS AD HOC NETWORKS

RESOURCE MANAGEMENT FOR WIRELESS AD HOC NETWORKS The Pennsylvania State University The Graduate School College of Engineering RESOURCE MANAGEMENT FOR WIRELESS AD HOC NETWORKS A Dissertation in Electrical Engineering by Min Chen c 2009 Min Chen Submitted

More information

CHAPTER 8 MIMO. Xijun Wang

CHAPTER 8 MIMO. Xijun Wang CHAPTER 8 MIMO Xijun Wang WEEKLY READING 1. Goldsmith, Wireless Communications, Chapters 10 2. Tse, Fundamentals of Wireless Communication, Chapter 7-10 2 MIMO 3 BENEFITS OF MIMO n Array gain The increase

More information

DOWNLINK TRANSMITTER ADAPTATION BASED ON GREEDY SINR MAXIMIZATION. Dimitrie C. Popescu, Shiny Abraham, and Otilia Popescu

DOWNLINK TRANSMITTER ADAPTATION BASED ON GREEDY SINR MAXIMIZATION. Dimitrie C. Popescu, Shiny Abraham, and Otilia Popescu DOWNLINK TRANSMITTER ADAPTATION BASED ON GREEDY SINR MAXIMIZATION Dimitrie C Popescu, Shiny Abraham, and Otilia Popescu ECE Department Old Dominion University 231 Kaufman Hall Norfol, VA 23452, USA ABSTRACT

More information

Joint Spectrum and Power Allocation for Inter-Cell Spectrum Sharing in Cognitive Radio Networks

Joint Spectrum and Power Allocation for Inter-Cell Spectrum Sharing in Cognitive Radio Networks Joint Spectrum and Power Allocation for Inter-Cell Spectrum Sharing in Cognitive Radio Networks Won-Yeol Lee and Ian F. Akyildiz Broadband Wireless Networking Laboratory School of Electrical and Computer

More information

Space-Time Interference Alignment and Degrees of Freedom Regions for the MISO Broadcast Channel with Periodic CSI Feedback

Space-Time Interference Alignment and Degrees of Freedom Regions for the MISO Broadcast Channel with Periodic CSI Feedback 1 Space-Time Interference Alignment and Degrees of Freedom Regions for the MISO Broadcast Channel with Periodic CSI Feedback Namyoon Lee and Robert W Heath Jr arxiv:13083272v1 [csit 14 Aug 2013 Abstract

More information

Dirty Paper Coding vs. TDMA for MIMO Broadcast Channels

Dirty Paper Coding vs. TDMA for MIMO Broadcast Channels 1 Dirty Paper Coding vs. TDMA for MIMO Broadcast Channels Nihar Jindal & Andrea Goldsmith Dept. of Electrical Engineering, Stanford University njindal, andrea@systems.stanford.edu Submitted to IEEE Trans.

More information

Joint Power Control and Beamforming for Interference MIMO Relay Channel

Joint Power Control and Beamforming for Interference MIMO Relay Channel 2011 17th Asia-Pacific Conference on Communications (APCC) 2nd 5th October 2011 Sutera Harbour Resort, Kota Kinabalu, Sabah, Malaysia Joint Power Control and Beamforming for Interference MIMO Relay Channel

More information

Optimal Transceiver Design for Multi-Access. Communication. Lecturer: Tom Luo

Optimal Transceiver Design for Multi-Access. Communication. Lecturer: Tom Luo Optimal Transceiver Design for Multi-Access Communication Lecturer: Tom Luo Main Points An important problem in the management of communication networks: resource allocation Frequency, transmitting power;

More information

New Uplink Opportunistic Interference Alignment: An Active Alignment Approach

New Uplink Opportunistic Interference Alignment: An Active Alignment Approach New Uplink Opportunistic Interference Alignment: An Active Alignment Approach Hui Gao, Johann Leithon, Chau Yuen, and Himal A. Suraweera Singapore University of Technology and Design, Dover Drive, Singapore

More information

IJSRD - International Journal for Scientific Research & Development Vol. 4, Issue 03, 2016 ISSN (online):

IJSRD - International Journal for Scientific Research & Development Vol. 4, Issue 03, 2016 ISSN (online): IJSRD - International Journal for Scientific Research & Development Vol. 4, Issue 03, 2016 ISSN (online): 2321-0613 Energy Efficiency of MIMO-IFBC for Green Wireless Systems Divya R PG Student Department

More information

MIMO I: Spatial Diversity

MIMO I: Spatial Diversity MIMO I: Spatial Diversity COS 463: Wireless Networks Lecture 16 Kyle Jamieson [Parts adapted from D. Halperin et al., T. Rappaport] What is MIMO, and why? Multiple-Input, Multiple-Output (MIMO) communications

More information

Eigenvalues and Eigenvectors in Array Antennas. Optimization of Array Antennas for High Performance. Self-introduction

Eigenvalues and Eigenvectors in Array Antennas. Optimization of Array Antennas for High Performance. Self-introduction Short Course @ISAP2010 in MACAO Eigenvalues and Eigenvectors in Array Antennas Optimization of Array Antennas for High Performance Nobuyoshi Kikuma Nagoya Institute of Technology, Japan 1 Self-introduction

More information

CHAPTER 6 JOINT SUBCHANNEL POWER CONTROL AND ADAPTIVE BEAMFORMING FOR MC-CDMA SYSTEMS

CHAPTER 6 JOINT SUBCHANNEL POWER CONTROL AND ADAPTIVE BEAMFORMING FOR MC-CDMA SYSTEMS CHAPTER 6 JOINT SUBCHANNEL POWER CONTROL AND ADAPTIVE BEAMFORMING FOR MC-CDMA SYSTEMS 6.1 INTRODUCTION The increasing demand for high data rate services necessitates technology advancement and adoption

More information

Optimizing future wireless communication systems

Optimizing future wireless communication systems Optimizing future wireless communication systems "Optimization and Engineering" symposium Louvain-la-Neuve, May 24 th 2006 Jonathan Duplicy (www.tele.ucl.ac.be/digicom/duplicy) 1 Outline History Challenges

More information

On the Capacity Region of the Vector Fading Broadcast Channel with no CSIT

On the Capacity Region of the Vector Fading Broadcast Channel with no CSIT On the Capacity Region of the Vector Fading Broadcast Channel with no CSIT Syed Ali Jafar University of California Irvine Irvine, CA 92697-2625 Email: syed@uciedu Andrea Goldsmith Stanford University Stanford,

More information

Performance Enhancement of Interference Alignment Techniques for MIMO Multi Cell Networks

Performance Enhancement of Interference Alignment Techniques for MIMO Multi Cell Networks Performance Enhancement of Interference Alignment Techniques for MIMO Multi Cell Networks B.Vijayanarasimha Raju 1 PG Student, ECE Department Gokula Krishna College of Engineering Sullurpet, India e-mail:

More information

6 Multiuser capacity and

6 Multiuser capacity and CHAPTER 6 Multiuser capacity and opportunistic communication In Chapter 4, we studied several specific multiple access techniques (TDMA/FDMA, CDMA, OFDM) designed to share the channel among several users.

More information

Acentral problem in the design of wireless networks is how

Acentral problem in the design of wireless networks is how 1968 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 45, NO. 6, SEPTEMBER 1999 Optimal Sequences, Power Control, and User Capacity of Synchronous CDMA Systems with Linear MMSE Multiuser Receivers Pramod

More information

CHANNEL ESTIMATION AND SIGNAL DETECTION FOR WIRELESS RELAY

CHANNEL ESTIMATION AND SIGNAL DETECTION FOR WIRELESS RELAY CHANNEL ESTIMATION AND SIGNAL DETECTION FOR WIRELESS RELAY A Dissertation Presented to The Academic Faculty By Jun Ma In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in Electrical

More information

Demo: Non-classic Interference Alignment for Downlink Cellular Networks

Demo: Non-classic Interference Alignment for Downlink Cellular Networks Demo: Non-classic Interference Alignment for Downlink Cellular Networks Yasser Fadlallah 1,2, Leonardo S. Cardoso 1,2 and Jean-Marie Gorce 1,2 1 University of Lyon, INRIA, France 2 INSA-Lyon, CITI-INRIA,

More information

IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 39, NO. 1, JANUARY

IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 39, NO. 1, JANUARY IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 39, NO. 1, JANUARY 2014 189 Region of Feasibility of Interference Alignment in Underwater Sensor Networks Parul Pandey, Student Member, IEEE, Mohammad Hajimirsadeghi,

More information

Detection of SINR Interference in MIMO Transmission using Power Allocation

Detection of SINR Interference in MIMO Transmission using Power Allocation International Journal of Electronics and Communication Engineering. ISSN 0974-2166 Volume 5, Number 1 (2012), pp. 49-58 International Research Publication House http://www.irphouse.com Detection of SINR

More information

IMPROVED QR AIDED DETECTION UNDER CHANNEL ESTIMATION ERROR CONDITION

IMPROVED QR AIDED DETECTION UNDER CHANNEL ESTIMATION ERROR CONDITION IMPROVED QR AIDED DETECTION UNDER CHANNEL ESTIMATION ERROR CONDITION Jigyasha Shrivastava, Sanjay Khadagade, and Sumit Gupta Department of Electronics and Communications Engineering, Oriental College of

More information

SIGNAL MODEL AND PARAMETER ESTIMATION FOR COLOCATED MIMO RADAR

SIGNAL MODEL AND PARAMETER ESTIMATION FOR COLOCATED MIMO RADAR SIGNAL MODEL AND PARAMETER ESTIMATION FOR COLOCATED MIMO RADAR Moein Ahmadi*, Kamal Mohamed-pour K.N. Toosi University of Technology, Iran.*moein@ee.kntu.ac.ir, kmpour@kntu.ac.ir Keywords: Multiple-input

More information

Multiple Antennas in Wireless Communications

Multiple Antennas in Wireless Communications Multiple Antennas in Wireless Communications Luca Sanguinetti Department of Information Engineering Pisa University lucasanguinetti@ietunipiit April, 2009 Luca Sanguinetti (IET) MIMO April, 2009 1 / 46

More information

Diversity Techniques

Diversity Techniques Diversity Techniques Vasileios Papoutsis Wireless Telecommunication Laboratory Department of Electrical and Computer Engineering University of Patras Patras, Greece No.1 Outline Introduction Diversity

More information

Spatial Correlation Effects on Channel Estimation of UCA-MIMO Receivers

Spatial Correlation Effects on Channel Estimation of UCA-MIMO Receivers 11 International Conference on Communication Engineering and Networks IPCSIT vol.19 (11) (11) IACSIT Press, Singapore Spatial Correlation Effects on Channel Estimation of UCA-MIMO Receivers M. A. Mangoud

More information

Revision of Lecture Twenty-Eight

Revision of Lecture Twenty-Eight ELEC64 Advanced Wireless Communications Networks and Systems Revision of Lecture Twenty-Eight MIMO classification: roughly three classes create diversity, increase throughput, support multi-users Some

More information

ASYNCHRONOUS BI-DIRECTIONAL RELAY-ASSISTED COMMUNICATION NETWORKS

ASYNCHRONOUS BI-DIRECTIONAL RELAY-ASSISTED COMMUNICATION NETWORKS ASYNCHRONOUS BI-DIRECTIONAL RELAY-ASSISTED COMMUNICATION NETWORKS By Reza Vahidnia A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN THE FACULTY OF

More information

PERFORMANCE ANALYSIS OF BEAMFORMING FOR FEMTOCELLULAR APPLICATIONS. by Wooyoung Ryu

PERFORMANCE ANALYSIS OF BEAMFORMING FOR FEMTOCELLULAR APPLICATIONS. by Wooyoung Ryu PERFORMANCE ANALYSIS OF BEAMFORMING FOR FEMTOCELLULAR APPLICATIONS by Wooyoung Ryu A thesis submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree

More information

ARQ strategies for MIMO eigenmode transmission with adaptive modulation and coding

ARQ strategies for MIMO eigenmode transmission with adaptive modulation and coding ARQ strategies for MIMO eigenmode transmission with adaptive modulation and coding Elisabeth de Carvalho and Petar Popovski Aalborg University, Niels Jernes Vej 2 9220 Aalborg, Denmark email: {edc,petarp}@es.aau.dk

More information

Optimum Power Allocation in Cooperative Networks

Optimum Power Allocation in Cooperative Networks Optimum Power Allocation in Cooperative Networks Jaime Adeane, Miguel R.D. Rodrigues, and Ian J. Wassell Laboratory for Communication Engineering Department of Engineering University of Cambridge 5 JJ

More information

ENERGY EFFICIENT WATER-FILLING ALGORITHM FOR MIMO- OFDMA CELLULAR SYSTEM

ENERGY EFFICIENT WATER-FILLING ALGORITHM FOR MIMO- OFDMA CELLULAR SYSTEM ENERGY EFFICIENT WATER-FILLING ALGORITHM FOR MIMO- OFDMA CELLULAR SYSTEM Hailu Belay Kassa, Dereje H.Mariam Addis Ababa University, Ethiopia Farzad Moazzami, Yacob Astatke Morgan State University Baltimore,

More information

A Novel SINR Estimation Scheme for WCDMA Receivers

A Novel SINR Estimation Scheme for WCDMA Receivers 1 A Novel SINR Estimation Scheme for WCDMA Receivers Venkateswara Rao M 1 R. David Koilpillai 2 1 Flextronics Software Systems, Bangalore 2 Department of Electrical Engineering, IIT Madras, Chennai - 36.

More information

Beamforming in Interference Networks for Uniform Linear Arrays

Beamforming in Interference Networks for Uniform Linear Arrays Beamforming in Interference Networks for Uniform Linear Arrays Rami Mochaourab and Eduard Jorswieck Communications Theory, Communications Laboratory Dresden University of Technology, Dresden, Germany e-mail:

More information

SEN366 (SEN374) (Introduction to) Computer Networks

SEN366 (SEN374) (Introduction to) Computer Networks SEN366 (SEN374) (Introduction to) Computer Networks Prof. Dr. Hasan Hüseyin BALIK (8 th Week) Cellular Wireless Network 8.Outline Principles of Cellular Networks Cellular Network Generations LTE-Advanced

More information

Non-Orthogonal Multiple Access (NOMA) in 5G Cellular Downlink and Uplink: Achievements and Challenges

Non-Orthogonal Multiple Access (NOMA) in 5G Cellular Downlink and Uplink: Achievements and Challenges Non-Orthogonal Multiple Access (NOMA) in 5G Cellular Downlink and Uplink: Achievements and Challenges Presented at: Huazhong University of Science and Technology (HUST), Wuhan, China S.M. Riazul Islam,

More information

Generalized Signal Alignment For MIMO Two-Way X Relay Channels

Generalized Signal Alignment For MIMO Two-Way X Relay Channels Generalized Signal Alignment For IO Two-Way X Relay Channels Kangqi Liu, eixia Tao, Zhengzheng Xiang and Xin Long Dept. of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China Emails:

More information

Energy Harvested and Achievable Rate of Massive MIMO under Channel Reciprocity Error

Energy Harvested and Achievable Rate of Massive MIMO under Channel Reciprocity Error Energy Harvested and Achievable Rate of Massive MIMO under Channel Reciprocity Error Abhishek Thakur 1 1Student, Dept. of Electronics & Communication Engineering, IIIT Manipur ---------------------------------------------------------------------***---------------------------------------------------------------------

More information

On the Complementary Benefits of Massive MIMO, Small Cells, and TDD

On the Complementary Benefits of Massive MIMO, Small Cells, and TDD On the Complementary Benefits of Massive MIMO, Small Cells, and TDD Jakob Hoydis (joint work with K. Hosseini, S. ten Brink, M. Debbah) Bell Laboratories, Alcatel-Lucent, Germany Alcatel-Lucent Chair on

More information

Modelling Small Cell Deployments within a Macrocell

Modelling Small Cell Deployments within a Macrocell Modelling Small Cell Deployments within a Macrocell Professor William Webb MBA, PhD, DSc, DTech, FREng, FIET, FIEEE 1 Abstract Small cells, or microcells, are often seen as a way to substantially enhance

More information

Channel Capacity Estimation in MIMO Systems Based on Water-Filling Algorithm

Channel Capacity Estimation in MIMO Systems Based on Water-Filling Algorithm Channel Capacity Estimation in MIMO Systems Based on Water-Filling Algorithm 1 Ch.Srikanth, 2 B.Rajanna 1 PG SCHOLAR, 2 Assistant Professor Vaagdevi college of engineering. (warangal) ABSTRACT power than

More information

Adaptive CDMA Cell Sectorization with Linear Multiuser Detection

Adaptive CDMA Cell Sectorization with Linear Multiuser Detection Adaptive CDMA Cell Sectorization with Linear Multiuser Detection Changyoon Oh Aylin Yener Electrical Engineering Department The Pennsylvania State University University Park, PA changyoon@psu.edu, yener@ee.psu.edu

More information

Cooperative Tx/Rx Caching in Interference Channels: A Storage-Latency Tradeoff Study

Cooperative Tx/Rx Caching in Interference Channels: A Storage-Latency Tradeoff Study Cooperative Tx/Rx Caching in Interference Channels: A Storage-Latency Tradeoff Study Fan Xu Kangqi Liu and Meixia Tao Dept of Electronic Engineering Shanghai Jiao Tong University Shanghai China Emails:

More information

Asynchronous Space-Time Cooperative Communications in Sensor and Robotic Networks

Asynchronous Space-Time Cooperative Communications in Sensor and Robotic Networks Proceedings of the IEEE International Conference on Mechatronics & Automation Niagara Falls, Canada July 2005 Asynchronous Space-Time Cooperative Communications in Sensor and Robotic Networks Fan Ng, Juite

More information

A Novel Uplink MIMO Transmission Scheme in a Multicell Environment

A Novel Uplink MIMO Transmission Scheme in a Multicell Environment IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL 8, NO 10, OCTOBER 2009 4981 A Novel Uplink MIMO Transmission Scheme in a Multicell Environment Byong Ok Lee, Student Member, IEEE, Hui Won Je, Member,

More information

Lecture 5: Antenna Diversity and MIMO Capacity Theoretical Foundations of Wireless Communications 1

Lecture 5: Antenna Diversity and MIMO Capacity Theoretical Foundations of Wireless Communications 1 Antenna, Antenna : Antenna and Theoretical Foundations of Wireless Communications 1 Friday, April 27, 2018 9:30-12:00, Kansliet plan 3 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication

More information

On Differential Modulation in Downlink Multiuser MIMO Systems

On Differential Modulation in Downlink Multiuser MIMO Systems On Differential Modulation in Downlin Multiuser MIMO Systems Fahad Alsifiany, Aissa Ihlef, and Jonathon Chambers ComS IP Group, School of Electrical and Electronic Engineering, Newcastle University, NE

More information

Optimization Techniques for Alphabet-Constrained Signal Design

Optimization Techniques for Alphabet-Constrained Signal Design Optimization Techniques for Alphabet-Constrained Signal Design Mojtaba Soltanalian Department of Electrical Engineering California Institute of Technology Stanford EE- ISL Mar. 2015 Optimization Techniques

More information

Distributed Coordinated Multi-Point Downlink Transmission with Over-the-Air Communication

Distributed Coordinated Multi-Point Downlink Transmission with Over-the-Air Communication Distributed Coordinated Multi-Point Downlink Transmission with Over-the-Air Communication Shengqian Han, Qian Zhang and Chenyang Yang School of Electronics and Information Engineering, Beihang University,

More information

An Energy-Division Multiple Access Scheme

An Energy-Division Multiple Access Scheme An Energy-Division Multiple Access Scheme P Salvo Rossi DIS, Università di Napoli Federico II Napoli, Italy salvoros@uninait D Mattera DIET, Università di Napoli Federico II Napoli, Italy mattera@uninait

More information

MULTICARRIER code-division multiple access (MC-

MULTICARRIER code-division multiple access (MC- 2064 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 4, NO. 5, SEPTEMBER 2005 A Novel Prefiltering Technique for Downlink Transmissions in TDD MC-CDMA Systems Michele Morelli, Member, IEEE, and L. Sanguinetti

More information

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, ACCEPTED FOR PUBLICATION 1

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, ACCEPTED FOR PUBLICATION 1 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, ACCEPTED FOR PUBLICATION 1 Multicell Coordination via Joint Scheduling, Beamforming and Power Spectrum Adaptation Wei Yu, Senior Member, IEEE, Taesoo Kwon,

More information

How (Information Theoretically) Optimal Are Distributed Decisions?

How (Information Theoretically) Optimal Are Distributed Decisions? How (Information Theoretically) Optimal Are Distributed Decisions? Vaneet Aggarwal Department of Electrical Engineering, Princeton University, Princeton, NJ 08544. vaggarwa@princeton.edu Salman Avestimehr

More information

ZERO-FORCING PRE-EQUALIZATION WITH TRANSMIT ANTENNA SELECTION IN MIMO SYSTEMS

ZERO-FORCING PRE-EQUALIZATION WITH TRANSMIT ANTENNA SELECTION IN MIMO SYSTEMS ZERO-FORCING PRE-EQUALIZATION WITH TRANSMIT ANTENNA SELECTION IN MIMO SYSTEMS Seyran Khademi, Sundeep Prabhakar Chepuri, Geert Leus, Alle-Jan van der Veen Faculty of Electrical Engineering, Mathematics

More information

Multi-user Space Time Scheduling for Wireless Systems with Multiple Antenna

Multi-user Space Time Scheduling for Wireless Systems with Multiple Antenna Multi-user Space Time Scheduling for Wireless Systems with Multiple Antenna Vincent Lau Associate Prof., University of Hong Kong Senior Manager, ASTRI Agenda Bacground Lin Level vs System Level Performance

More information

Beamforming and Binary Power Based Resource Allocation Strategies for Cognitive Radio Networks

Beamforming and Binary Power Based Resource Allocation Strategies for Cognitive Radio Networks 1 Beamforming and Binary Power Based Resource Allocation Strategies for Cognitive Radio Networks UWB Walter project Workshop, ETSI October 6th 2009, Sophia Antipolis A. Hayar EURÉCOM Institute, Mobile

More information

EasyChair Preprint. A User-Centric Cluster Resource Allocation Scheme for Ultra-Dense Network

EasyChair Preprint. A User-Centric Cluster Resource Allocation Scheme for Ultra-Dense Network EasyChair Preprint 78 A User-Centric Cluster Resource Allocation Scheme for Ultra-Dense Network Yuzhou Liu and Wuwen Lai EasyChair preprints are intended for rapid dissemination of research results and

More information

EE359 Discussion Session 8 Beamforming, Diversity-multiplexing tradeoff, MIMO receiver design, Multicarrier modulation

EE359 Discussion Session 8 Beamforming, Diversity-multiplexing tradeoff, MIMO receiver design, Multicarrier modulation EE359 Discussion Session 8 Beamforming, Diversity-multiplexing tradeoff, MIMO receiver design, Multicarrier modulation November 29, 2017 EE359 Discussion 8 November 29, 2017 1 / 33 Outline 1 MIMO concepts

More information

On the Capacity Regions of Two-Way Diamond. Channels

On the Capacity Regions of Two-Way Diamond. Channels On the Capacity Regions of Two-Way Diamond 1 Channels Mehdi Ashraphijuo, Vaneet Aggarwal and Xiaodong Wang arxiv:1410.5085v1 [cs.it] 19 Oct 2014 Abstract In this paper, we study the capacity regions of

More information

Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems

Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems MIMO Each node has multiple antennas Capable of transmitting (receiving) multiple streams

More information

Distributed Power Control in Cellular and Wireless Networks - A Comparative Study

Distributed Power Control in Cellular and Wireless Networks - A Comparative Study Distributed Power Control in Cellular and Wireless Networks - A Comparative Study Vijay Raman, ECE, UIUC 1 Why power control? Interference in communication systems restrains system capacity In cellular

More information

Antennas and Propagation. Chapter 6d: Diversity Techniques and Spatial Multiplexing

Antennas and Propagation. Chapter 6d: Diversity Techniques and Spatial Multiplexing Antennas and Propagation d: Diversity Techniques and Spatial Multiplexing Introduction: Diversity Diversity Use (or introduce) redundancy in the communications system Improve (short time) link reliability

More information

Antennas and Propagation. Chapter 6b: Path Models Rayleigh, Rician Fading, MIMO

Antennas and Propagation. Chapter 6b: Path Models Rayleigh, Rician Fading, MIMO Antennas and Propagation b: Path Models Rayleigh, Rician Fading, MIMO Introduction From last lecture How do we model H p? Discrete path model (physical, plane waves) Random matrix models (forget H p and

More information