Potential Throughput Improvement of FD MIMO in Practical Systems
|
|
- Marvin Craig
- 5 years ago
- Views:
Transcription
1 2014 UKSim-AMSS 8th European Modelling Symposium Potential Throughput Improvement of FD MIMO in Practical Systems Fangze Tu, Yuan Zhu, Hongwen Yang Mobile and Communications Group, Intel Corporation Beijing University of Posts and Telecommunications Abstract Installing large number of antennas at the transmitter or/and the receiver can increase the channel capacity significantly in theory, however, practical implementations need to consider different system aspects, e.g. compatibility with existing beamforming scheme and reference signal overhead, in order to realize the system level throughput gain in practice. In this paper, we first use an idealized full dimensional multiple input multiple output (FD-MIMO) system to quantize the potential system throughput gain of FD-MIMO systems. We then introduce the reference signal overhead limitation by virtualizing large number of antennas to a limited number of antenna ports. The simulation result shows that through proper antenna virtualization the system throughput gain of one idealized FD-MIMO system can be achieved within reference signal overhead limitation. Keywords FD-MIMO; antenna virtualization; I. INTRODUCTION Beamforming technology is incorporated into LTE standards since Rel.8, and it is proven to be a practical technology to increase the received strength of the signal-of-interest and reduce the inter/intra-cell interference, hence improve the overall system capacity [1]. However, in conventional system the antennas are typically equipped in one dimension array with few antennas. In order to meet the demanding of ever growing data usage, advanced MIMO technologies are highly desired for future LTE-A system. Recently, full dimensional multiple input multiple output (FD-MIMO) has been discussed in the academy and industry [2]. FD-MIMO can leverage the significantly increased freedom offered by the advanced antenna arrays with large number of antenna elements which can be adjusted individually [3]. When one planar antenna array is used, the transmitter can adjust the beam direction in elevation dimension in addition to azimuth dimension to concentrate the energy to the user of interests while minimizing the interference leakage to co-scheduled users in the same cell or users in the neighboring cells. In a traditional closed loop beamforming technology, the transmitter uses the beamforming matrix, which is designed by the receiver, to match the property of the channel matrix. When the number of transmit antenna increases, the dimension of the channel matrix also increases. The straightforward method to fully explore the increased freedom of the channel matrix is to enable the receiver to measure the full dimensional channel matrix and design the beamforming matrix correspondingly. However, this is often intractable in practical systems due to the prohibitive reference signal overhead when the number of transmit antennas are large. In order to design a closed loop FD-MIMO system with limited reference signal overhead, one feasible method is to design the large beamforming matrix as a product of two much smaller matrices. The left matrix virtualizes large amount of antennas to a limited number of antenna ports for channel state information (CSI) measurement and the right matrix further virtualizes the reduced dimension virtual channel matrix to one or more layers for data transmission. The transmission of left virtualization matrix is similar to channel state information reference signal (CSI-RS) transmission which was introduced to LTE Rel.10. One design option is to use block diagonal matrix for the left matrix to realize one electrical downtilt and existing LTE codebooks for the right matrix to beam-form in azimuth dimension [4]. Although this design option is simple for implementation, we believe other non-block diagonal structure, if well designed, will further improve the system performance. In [5], subspace tracking was proposed as a different way to design the two precoding matrices. In subspace tracking scheme, the left channel virtualization matrix is initialized and further trained over time while the right matrix is quantized using a codebook. By using multiple iterations, the left matrix would approach the subspace which covers the major directions of the intended users. In this paper, we first define the FD-MIMO baseline system which uses fixed block diagonal matrix as the left matrix. In order to quantize the performance upper bound of FD-MIMO systems, we then define one ideal system in which the full channel matrix is ideally known by the transmitter. We derive the performance upper bound based on the ideal system by using the principle eigen-beam of the full channel matrix. We then define a FD-MIMO system based on ideal subspace. In the ideal subspace system, we assume the channel covariance matrix is known by the transmitter. And the enb calculates ideal subspace by using the principal eigen-beams from the average channel covariance matrix of all active users and uses it to construct the left channel virtualization matrix. Compared with the ideal system which we use to derive the performance upper bound, the major difference in the ideal subspace system is that we introduced the limitation of number of reference /14 $ IEEE DOI /EMS
2 signal ports. In order to focus on the design of the left matrix, we use ideal sounding to feedback the right matrix. Thus the effective channel matrix for both the baseline and the ideal subspace systems are known to the transmitter. The main motivation to introduce the ideal subspace system is to verify whether the performance upper bound can be achieved in practical systems when there is limitation on number of reference signal ports. The remainder of this paper is organized as follows. Section II introduces the system model. Section III briefly reviews the 3D MIMO channel model designed by 3GPP. Section IV defines the baseline, ideal and ideal subspace system using the system model in Section II. Section V provides the simulation results using LTE-A simulation methodology. Finally, conclusions are presented in section VI. Notation: We use ( ) H to denote Hermitian transpose operation, to denote the matrix norm, det( ) to denote the determinant of a matrix, CN (0, 1) to denote zero-mean and unit-variance circulary-symmetric complex-gaussian random variable, N t to denote number of transmit antennas, N c to denote the number of transmit antenna ports, N p to denote the number of transmission layer, N r to denote the number of receive antennas, N v to denote the number of antennas in vertical domain of one planar array, N a to denote the number of antennas in azimuth domain of one planar array, h user to denote the user height in meters, n fl to denote the number of floor of user stands, N fl to denote the number of floor of one building, λ to denote the wave length, d v to denote the spacing between two consecutive vertical antenna elements, K to denote dimensional reduction factor as described in (6). II. SYSTEM MODEL We consider one generalized MIMO system. The enb is equipped with N t antennas and each user is equipped with N r receiver antennas. enb transmits N p layer data symbols. The system model is given by: y = ρhpx + n (1) where y is N r 1 receive signal, ρ is per data symbol signal to noise ratio (SNR), H is N r N t channel, P is N t N p precoding matrix, x is N p 1 transmit data symbol vector, n is N r 1 additive white Gaussian noise (AWGN), whose entries are independently and identically distributed i.i.d. CN (0, 1). In FD-MIMO systems, N t is usually much larger than that of the conventional MIMO systems. For example, one planar antenna array may have 40 antenna elements. For single user transmission, the channel capacity is: where I Nr C = log 2 det(i Nr + ρ N t HH H ) (2) is N r N r identity matrix. III. 3D MIMO CHANNEL In MIMO communication, the system performance is highly related to the propagation channel. Therefore, the channel modelling is vital to MIMO related studies. Although radio electromagnetic wave is 3D in nature, the elevation angles are absent for simplicity in 2D SCM [6]. In order to study FD-MIMO systems, the elevation dimension is added to the propagation model. 3D channel model was first presented in the WINNER2 project [7] and 3GPP further studied the 3D channel model in TR [8]. Other than introducing elevation angles, user distribution is also not always on the ground in 3D MIMO channel model. The user distribution on the vertical dimension can better model the traffic in the real world. The indoor user distribution in elevation domain is modelled as (3), assuming indoor users are evenly distributed in low rise buildings in the elevation dimension: h user = 3(n fl 1) (3) where n fl is uniformly distributed from 1 to N fl, N fl is uniformly distributed from 4 to 8. The main difference between the azimuth and elevation dimension in 3D-MIMO channel is that the angle spread of the elevation dimension is much narrower than in the azimuth dimension due to lack of scatters. Other than the angle spread, the distribution of the line-of-sight angle is also much narrower in the elevation dimension than azimuth dimension due to the low building height considered in the model. IV. FD-MIMO BEAMFORMING SCHEMES This section discusses the FD-MIMO beamforming schemes including the baseline, ideal system and ideal subspace system. We first present a general FD-MIMO beamforming scheme in the subsection A. Then we give the baseline and ideal FD-MIMO beamforming scheme in subsection B. At last, the ideal subspace beamforming scheme is given in subsection C. A. General FD-MIMO Beamfoming Scheme In conventional LTE-A system, one reference signal is transmitted for one antenna port in order to enable the user to measure the CSI of that antenna port. If we use one reference signal for one antenna in FD-MIMO systems, the overall reference signal overhead can be prohibitive. Thus one feasible way is to virtualize the total number of N t antennas to a limited number of N c antenna ports and user measures CSI from N c antenna ports. This can be represented as: y = HP c P d + n = ĤP d + n (4) where P c is N t N c matrix and P H c P c is identity matrix, P d is N c N p matrix, and Ĥ = HP c is the effective 421
3 channel matrix with dimension of N r N c. While P c can be cell-specific, P d is usually user-specific. Other symbols have same meanings as in (1). After the antenna to antenna port mapping, the channel capacity is: C = logdet(i Nr + ρ N c ĤĤH ) = logdet(i Nr + ρ N c HP c (HP c ) H ) system performance is 102 degree. Thus P c can be written as equation (7): P c = W(θ) W(θ) (7) logdet(i Nr + ρ N t HH H ) Virtualizing the large number of antennas to a limited number of antenna ports will affect the channel capacity to some extent. If the P c is not well designed to match the channel property, the channel capacity will be highly reduced no matter how P d is designed. In a practical system, it means that the formed beamforming directions cannot cover all the users of interests. Otherwise, the system capacity can be mostly achieved. If we define dimensional reduction factor as equation (6), K antennas are mapped to one antenna port on average. (5) K = N t N c (6) If we assume that P c is cell-specific, reference signal overhead can be reduced to the maximum. The ideal system can then be viewed as a special case of the ideal subspace system with K equals to one. It is then of interests how large K can practically be if the users are realistically distributed in three-dimensional space for a given number of N t transmit antennas. In practical network, it is often observed that the traffic distribution over geographical area has strong correlation, because the traffic can come from the same building or even the same floor and still served by one macro cell. Thus it is not uncommon that several users share similar eigen-beam directions. This physical phenomenon can be explored to reduce reference signal overhead and simplify the overall system design. B. Baseline and Ideal Beamforming Schemes In this section, we define the baseline and ideal systems of FD-MIMO. In the baseline FD-MIMO system, we design P c as block-diagonal matrix which realized one single electrical downtilt using DFT vector. Each column of N v antenna elements are mapped to one port to realize one electrical downtilt, so the dimensional reduction factor K equals N v in the baseline system. We assume that all cells apply the same electrical downtilt. We choose the downtilt to minimize interference to the neighbor cells from 90 degrees zenith angle which points to horizon. When number of antenna elements is 10 and DFT vectors are used to virtualize each column of vertical antenna elements, the best cell common downtilt to maximize the overall where W(θ) = [w 1 (θ), w 2 (θ),..., w Nv (θ)] T, w m (θ) = 1 Nv e j 2π λ dv(m 1)cos(θ), m = (1, 2,..., N v ). We use this system to derive the baseline performance of FD-MIMO. In the ideal system, which is used to derive the performance upper bound, the precoding matrix P of each user is derived from the single value decomposition of each user s uplink sounding channel matrix H s. H sh Hs H s = U(s) (s)v(s)h (8) where V(s) = [v(s),, v Ntx (s)],and v i (s)(1 i N tx ) is the i th eigen beam of user s with size N t 1, (s) is N t N t diagonal matrix of eigenvalues. enb would use v 1 (s) as the optimal rank one precoder for user s. Note that in ideal system, each antenna is mapped to one antenna port, therefore the dimensional reduction factor K equals to one. C. Ideal Subspace Beamforming Scheme As described in subsection IV.A, the precoding matrix P can be represented by the product of P c and P d in order to save reference signal overhead and simplify the system design. In this subsection, we propose a beamforming method based on ideal subspace for FD-MIMO, assuming the channel covariance matrix is ideally known by the transmitter. The proposed beamforming method is described as follows, Step 1: enb estimates each user s UL channel H s from UL sounding; Step 2: enb calculates the average channel covariance matrix R ave : R ave = 1 S H sh Hs (9) S s=1 where S denotes total number of users in one cell. Step 3: enb constructs the matrix P c which is formed by the N c largest principal eigen beams from the average channel covariance matrix R ave. R ave = U V H (10) where is N t N t diagonal matrix of eigenvalues λ 1, λ 2, λ 3... λ Nt, V = [v 1, v 2, v Nt ] and v i (s)(1 i N tx ) is the i th eigen beam of user s with size N t 1. Suppose that λ 1 λ 2 λ 3 λ Nc λ Nt, P c can be designed as: P c = [v 1, v 2, v Nc ] (11) 422
4 We assume the ideal effective channel HP c is known to the transmitter thus the performance of the idealized subspace system is solely dependent on the design of P c. This avoids introducing quantization errors to the design of P d and simplifies the comparison of the systems in this paper. The eigenvalues tend to be equal, if the spatial property of users channel matrices H s is of little correlation. In order to maintain the overall system performance, we still need to do the beamforming in the full dimensional space as the ideal beamforming scheme. But in real networks, the spatial properties of users of the same cell, especially the vertical component of the channel, can be correlated. The spatially correlated eigen beams are enhanced more than those isolated eigen beams. This could result in unbalanced eigenvalues of the average channel covariance matrix. Compared with the 3D beamforming which employs block diagonal matrix for P c, the subspace based design of P c employs a nonblock diagonal structure. Note that, P c can create narrow 3D beams and point those beams to all the active users within one enb s coverage. V. SIMULATION RESULTS In this section, we compare the performances of the baseline, ideal system and the ideal subspace system performance in LTE-A system. The system level simulation is operated using the 3GPP LTE-A evaluation methodologies, and the 3D channel model in TR [8].The main simulation parameters are given in Table I. We compare the system performance using a greedy multi-user MIMO scheduler. The scheduler keeps pairing more users until the proportional fairness metrics for one sub-band does not increase. The Table II [9] shows the performance of the baseline and ideal system performance of FD-MIMO. The potential system throughput gain in FD-MIMO system is significant for both 3D-UMa and 3D-UMi environments. Note that the potential performance gain is relatively higher in 3D-UMi than 3D-UMa due to that the distribution of the vertical lineof-sight angle is larger in 3D-UMi environments. Then we discuss how the dimensional reduction factor K and system design affect the overall performance of FD-MIMO. Figures 1 and 2 illustrate the FD-MIMO performance of the baseline, ideal and ideal subspace system in 3D-UMa and 3D-UMi scenarios respectively. The 40 ports case means that enb has N t = 40 antenna ports to transmit thus there is no dimensional reduction, which is the ideal system performance of FD-MIMO as mentioned in subsection IV.B. The baseline system, as mentioned in Section IV.B, maps N v = 10 antennas to one antenna port, thus the dimensional reduction factor K equals 10. When number of antenna ports equals 4 or 8, it is for ideal subspace system as mentioned in Section IV.C. It can be seen that when the number of antenna ports equals to 8 and the dimensional reduction factor K is 5, the ideal Parameters Scenarios enb antenna configuration user antenna configurations Channel acknowledge at enb Duplex Network sync TABLE I SIMULATION PARAMETERS Values 3D-UMa,3D-UMi N v = 10, N a = 4, X-pol(+/45),0.5λ H/V 2R x X-pol(0/+90) Ideal sounding feedback FDD Synchronized user per cell 10 user speed user dropping user antenna pattern Traffic model Scheduler Receiver Hybrid ARQ 3 kmph three dimensional Isotropic antenna gain pattern Full-buffer PF Ideal channel estimation Ideal interference modeling MMSE-IRC receiver Maximum 4 transmissions TABLE II BASELINE AND UPPER BOUND PERFORMANCE OF FD-MIMO Cell-edge(bps/Hz) Cell Avg(bps/Hz) 3D-UMa Baseline 0.045(100%) 1.84(100%) 3D-UMa Ideal 0.062(139%) 2.75(149%) 3D-UMi Baseline 0.039(100%) 1.75(100%) 3D-UMa Ideal 0.082(158%) 3.92(224%) subspace system performance approaches the ideal system performance. If the dimensional reduction factor is too large in one cell such as K equals 10 and the number of antenna ports is 4, the overall system performance is jeopardized due to that some UEs may fall out of the subspace. Thus we observe that the number of virtualization antenna ports N c should approach the number of active users in one cell while overall performance as approaches that of the ideal system. Also it is possible to achieve the performance of ideal systems with limited number of antenna ports. VI. CONCLUSION In this paper, we have discussed different beamforming method in the FD-MIMO system. A proposed beamforming method based on ideal subspace is presented to analyze the design of antenna port virtualization matrix and the overall system performance. From the simulation results, we observed that if FD-MIMO system is properly implemented, the ideal system performance can be achieved with limited number of reference signal antenna ports. REFERENCES [1] Q. Li, G. Li, W. Lee, M. il Lee, D. Mazzarese, B. Clerckx, & Z. Li, MIMO techniques in WiMAX and LTE: a feature overview, IEEE Communications Magazine, vol. 48, no. 5, pp , May
5 Figure 1. 3D-UMa FD-MIMO Performance Figure 2. 3D-UMi FD-MIMO Performance [2] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, & F. Tufvesson. Scaling up MIMO: Opportunities and challenges with very large arrays IEEE Signal Processing Magazine, vol. 30, no. 1, pp , Jan [3] Y. H. Nam, B. L. Ng, K. Sayana, Y. Li, J. Zhang, Y. Kim, & J. Lee, Full-dimension MIMO (FD-MIMO) for next generation cellular technology, IEEE Communications Magazine, vol. 51 no.6, pp.40-60, Jan [4] T. A. Thomas & F. W. Vook, Transparent user-specific 3D MIMO in FDD using beamspace methods, IEEE Global Communications Conference (GLOBECOM), Dec [5] S. M. Hooman, Q. Li & S. Masoud, Practical Downlink Transmission Schemes for Future LTE Systems with Many Base-Station Antennas, IEEE Global Communications Conference (GLOBECOM), Dec. 2013, in press. [6] 3GPP TR (V11.0.0) Spatial channel model for Multiple Input Multiple Output (MIMO) simulations, Sep [7] IST-WINNER, I. I. (2007). Deliverable v. 1.2, WINNER II Channel Models, IST-WINNER2. Tech. Rep., [8] 3GPP TR (V2.0.0) Study on 3D channel model for LTE, Mar
Analysis of Massive MIMO With Hardware Impairments and Different Channel Models
Analysis of Massive MIMO With Hardware Impairments and Different Channel Models Fredrik Athley, Giuseppe Durisi 2, Ulf Gustavsson Ericsson Research, Ericsson AB, Gothenburg, Sweden 2 Dept. of Signals and
More informationA Novel 3D Beamforming Scheme for LTE-Advanced System
A Novel 3D Beamforming Scheme for LTE-Advanced System Yu-Shin Cheng 1, Chih-Hsuan Chen 2 Wireless Communications Lab, Chunghwa Telecom Co, Ltd No 99, Dianyan Rd, Yangmei City, Taoyuan County 32601, Taiwan
More informationPerformance Evaluation of Limited Feedback Schemes for 3D Beamforming in LTE-Advanced System
Performance Evaluation of Limited Feedback Scemes for 3D Beamforming in LTE-Advanced System Sang-Lim Ju, Young-Jae Kim, and Won-Ho Jeong Department of Radio and Communication Engineering Cungbuk National
More informationMeasured propagation characteristics for very-large MIMO at 2.6 GHz
Measured propagation characteristics for very-large MIMO at 2.6 GHz Gao, Xiang; Tufvesson, Fredrik; Edfors, Ove; Rusek, Fredrik Published in: [Host publication title missing] Published: 2012-01-01 Link
More informationInvestigation on Multiple Antenna Transmission Techniques in Evolved UTRA. OFDM-Based Radio Access in Downlink. Features of Evolved UTRA and UTRAN
Evolved UTRA and UTRAN Investigation on Multiple Antenna Transmission Techniques in Evolved UTRA Evolved UTRA (E-UTRA) and UTRAN represent long-term evolution (LTE) of technology to maintain continuous
More informationAnalysis of RF requirements for Active Antenna System
212 7th International ICST Conference on Communications and Networking in China (CHINACOM) Analysis of RF requirements for Active Antenna System Rong Zhou Department of Wireless Research Huawei Technology
More informationHybrid ARQ Scheme with Antenna Permutation for MIMO Systems in Slow Fading Channels
Hybrid ARQ Scheme with Antenna Permutation for MIMO Systems in Slow Fading Channels Jianfeng Wang, Meizhen Tu, Kan Zheng, and Wenbo Wang School of Telecommunication Engineering, Beijing University of Posts
More information3D Beamforming for Capacity Boosting in LTE-Advanced System
3D Beamforming for Capacity Boosting in LTE-Advanced System Hyoungju Ji, Byungju Lee and Byonghyo Shim Seoul National University, Seoul, Korea Email: {hyoungjuji, bjlee}@islabsnuackr, bshim@snuackr Young-Han
More informationClosed-loop MIMO performance with 8 Tx antennas
Closed-loop MIMO performance with 8 Tx antennas Document Number: IEEE C802.16m-08/623 Date Submitted: 2008-07-14 Source: Jerry Pi, Jay Tsai Voice: +1-972-761-7944, +1-972-761-7424 Samsung Telecommunications
More informationSystem Performance of Cooperative Massive MIMO Downlink 5G Cellular Systems
IEEE WAMICON 2016 April 11-13, 2016 Clearwater Beach, FL System Performance of Massive MIMO Downlink 5G Cellular Systems Chao He and Richard D. Gitlin Department of Electrical Engineering University of
More informationCanadian Evaluation Group
IEEE L802.16-10/0061 Canadian Evaluation Group Raouia Nasri, Shiguang Guo, Ven Sampath Canadian Evaluation Group (CEG) www.imt-advanced.ca Overview What the CEG evaluated Compliance tables Services Spectrum
More informationTest strategy towards Massive MIMO
Test strategy towards Massive MIMO Using LTE-Advanced Pro efd-mimo Shatrughan Singh, Technical Leader Subramaniam H, Senior Technical Leader Jaison John Puliyathu Mathew, Senior Engg. Project Manager Abstract
More informationBeamforming for 4.9G/5G Networks
Beamforming for 4.9G/5G Networks Exploiting Massive MIMO and Active Antenna Technologies White Paper Contents 1. Executive summary 3 2. Introduction 3 3. Beamforming benefits below 6 GHz 5 4. Field performance
More informationMassive MIMO a overview. Chandrasekaran CEWiT
Massive MIMO a overview Chandrasekaran CEWiT Outline Introduction Ways to Achieve higher spectral efficiency Massive MIMO basics Challenges and expectations from Massive MIMO Network MIMO features Summary
More informationNR Physical Layer Design: NR MIMO
NR Physical Layer Design: NR MIMO Younsun Kim 3GPP TSG RAN WG1 Vice-Chairman (Samsung) 3GPP 2018 1 Considerations for NR-MIMO Specification Design NR-MIMO Specification Features 3GPP 2018 2 Key Features
More informationExperimental evaluation of massive MIMO at 20 GHz band in indoor environment
This article has been accepted and published on J-STAGE in advance of copyediting. Content is final as presented. IEICE Communications Express, Vol., 1 6 Experimental evaluation of massive MIMO at GHz
More informationUplink Closed Loop Transmit Diversity for HSPA Yibo Jiang, Haitong Sun, Sharad Sambhwani, Jilei Hou Qualcomm Inc
Uplink Closed Loop Transmit Diversity for HSPA Yibo Jiang, Haitong Sun, Sharad Sambhwani, Jilei Hou Qualcomm Inc Abstract The closed loop transmit diversity scheme is a promising technique to improve the
More informationPerformance Evaluation of Massive MIMO in terms of capacity
IJSRD National Conference on Advances in Computer Science Engineering & Technology May 2017 ISSN: 2321-0613 Performance Evaluation of Massive MIMO in terms of capacity Nikhil Chauhan 1 Dr. Kiran Parmar
More informationLTE-Advanced research in 3GPP
LTE-Advanced research in 3GPP GIGA seminar 8 4.12.28 Tommi Koivisto tommi.koivisto@nokia.com Outline Background and LTE-Advanced schedule LTE-Advanced requirements set by 3GPP Technologies under investigation
More informationChannel Modelling ETI 085. Antennas Multiple antenna systems. Antennas in real channels. Lecture no: Important antenna parameters
Channel Modelling ETI 085 Lecture no: 8 Antennas Multiple antenna systems Antennas in real channels One important aspect is how the channel and antenna interact The antenna pattern determines what the
More informationON PILOT CONTAMINATION IN MASSIVE MULTIPLE-INPUT MULTIPLE- OUTPUT SYSTEM WITH LEAST SQUARE METHOD AND ZERO FORCING RECEIVER
ISSN: 2229-6948(ONLINE) ICTACT JOURNAL ON COMMUNICATION TECHNOLOGY, SEPTEM 2017, VOLUME: 08, ISSUE: 03 DOI: 10.21917/ijct.2017.0228 ON PILOT CONTAMINATION IN MASSIVE MULTIPLE-INPUT MULTIPLE- OUTPUT SYSTEM
More informationBeamforming with Finite Rate Feedback for LOS MIMO Downlink Channels
Beamforming with Finite Rate Feedback for LOS IO Downlink Channels Niranjay Ravindran University of innesota inneapolis, N, 55455 USA Nihar Jindal University of innesota inneapolis, N, 55455 USA Howard
More informationE7220: Radio Resource and Spectrum Management. Lecture 4: MIMO
E7220: Radio Resource and Spectrum Management Lecture 4: MIMO 1 Timeline: Radio Resource and Spectrum Management (5cr) L1: Random Access L2: Scheduling and Fairness L3: Energy Efficiency L4: MIMO L5: UDN
More informationBackward Compatible MIMO Techniques in a Massive MIMO Test-bed for Long Term Evolution (LTE) Mobile Systems
Backward Compatible MIMO Techniques in a Massive MIMO Test-bed for Long Term Evolution (LTE) Mobile Systems Seok Ho Won, Saeyoung Cho, and Jaewook Shin Mobile Communication Division, ETRI (Electronics
More informationAntennas Multiple antenna systems
Channel Modelling ETIM10 Lecture no: 8 Antennas Multiple antenna systems Fredrik Tufvesson Department of Electrical and Information Technology Lund University, Sweden Fredrik.Tufvesson@eit.lth.se 2012-02-13
More informationPerformance Analysis of Massive MIMO Downlink System with Imperfect Channel State Information
International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 Volume 3 Issue 12 ǁ December. 2015 ǁ PP.14-19 Performance Analysis of Massive MIMO
More informationPerformance Evaluation of Adaptive MIMO Switching in Long Term Evolution
Performance Evaluation of Adaptive MIMO Switching in Long Term Evolution Muhammad Usman Sheikh, Rafał Jagusz,2, Jukka Lempiäinen Department of Communication Engineering, Tampere University of Technology,
More informationEnergy 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 informationFeedback Compression Schemes for Downlink Carrier Aggregation in LTE-Advanced. Nguyen, Hung Tuan; Kovac, Istvan; Wang, Yuanye; Pedersen, Klaus
Downloaded from vbn.aau.dk on: marts, 19 Aalborg Universitet Feedback Compression Schemes for Downlink Carrier Aggregation in LTE-Advanced Nguyen, Hung Tuan; Kovac, Istvan; Wang, Yuanye; Pedersen, Klaus
More informationAWGN Channel Performance Analysis of QO-STB Coded MIMO- OFDM System
AWGN Channel Performance Analysis of QO-STB Coded MIMO- OFDM System Pranil Mengane 1, Ajitsinh Jadhav 2 12 Department of Electronics & Telecommunication Engg, D.Y. Patil College of Engg & Tech, Kolhapur
More informationMIMO Wireless Communications
MIMO Wireless Communications Speaker: Sau-Hsuan Wu Date: 2008 / 07 / 15 Department of Communication Engineering, NCTU Outline 2 2 MIMO wireless channels MIMO transceiver MIMO precoder Outline 3 3 MIMO
More informationA Practical Channel Estimation Scheme for Indoor 60GHz Massive MIMO System. Arumugam Nallanathan King s College London
A Practical Channel Estimation Scheme for Indoor 60GHz Massive MIMO System Arumugam Nallanathan King s College London Performance and Efficiency of 5G Performance Requirements 0.1~1Gbps user rates Tens
More information3GPP TR V ( )
TR 36.871 V11.0.0 (2011-12) Technical Report 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Downlink Multiple
More informationOn the Trade-Off Between Transmit and Leakage Power for Rate Optimal MIMO Precoding
On the Trade-Off Between Transmit and Leakage Power for Rate Optimal MIMO Precoding Tim Rüegg, Aditya U.T. Amah, Armin Wittneben Swiss Federal Institute of Technology (ETH) Zurich, Communication Technology
More informationELEC 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 informationJoint Antenna Selection and Grouping in Massive MIMO Systems
Joint Antenna Selection and Grouping in Massive MIMO Systems Mouncef Benmimoune, Elmahdi Driouch, Wessam Ajib Department of Computer Science, Université du Québec à Montréal, CANADA Email:{benmimoune.moncef,
More informationMassive MIMO for the New Radio Overview and Performance
Massive MIMO for the New Radio Overview and Performance Dr. Amitabha Ghosh Nokia Bell Labs IEEE 5G Summit June 5 th, 2017 What is Massive MIMO ANTENNA ARRAYS large number (>>8) of controllable antennas
More informationCHAPTER 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 informationPROGRESSIVE CHANNEL ESTIMATION FOR ULTRA LOW LATENCY MILLIMETER WAVE COMMUNICATIONS
PROGRESSIVECHANNELESTIMATIONFOR ULTRA LOWLATENCYMILLIMETER WAVECOMMUNICATIONS Hung YiCheng,Ching ChunLiao,andAn Yeu(Andy)Wu,Fellow,IEEE Graduate Institute of Electronics Engineering, National Taiwan University
More informationWideband Hybrid Precoder for Massive MIMO Systems
Wideband Hybrid Precoder for Massive MIMO Systems Lingxiao Kong, Shengqian Han, and Chenyang Yang School of Electronics and Information Engineering, Beihang University, Beijing 100191, China Email: {konglingxiao,
More informationMIMO 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 informationPrecoding 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 informationMU-MIMO in LTE/LTE-A Performance Analysis. Rizwan GHAFFAR, Biljana BADIC
MU-MIMO in LTE/LTE-A Performance Analysis Rizwan GHAFFAR, Biljana BADIC Outline 1 Introduction to Multi-user MIMO Multi-user MIMO in LTE and LTE-A 3 Transceiver Structures for Multi-user MIMO Rizwan GHAFFAR
More informationFull-Dimension MIMO Arrays with Large Spacings Between Elements. Xavier Artiga Researcher Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Full-Dimension MIMO Arrays with Large Spacings Between Elements Xavier Artiga Researcher Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) APS/URSI 2015, 22/07/2015 1 Outline Introduction to Massive
More informationProviding Extreme Mobile Broadband Using Higher Frequency Bands, Beamforming, and Carrier Aggregation
Providing Extreme Mobile Broadband Using Higher Frequency Bands, Beamforming, and Carrier Aggregation Fredrik Athley, Sibel Tombaz, Eliane Semaan, Claes Tidestav, and Anders Furuskär Ericsson Research,
More informationA 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 informationLecture 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 informationMultiple 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 informationSystem-Level Performance of Downlink Non-orthogonal Multiple Access (NOMA) Under Various Environments
System-Level Permance of Downlink n-orthogonal Multiple Access (N) Under Various Environments Yuya Saito, Anass Benjebbour, Yoshihisa Kishiyama, and Takehiro Nakamura 5G Radio Access Network Research Group,
More informationAnalysis of massive MIMO networks using stochastic geometry
Analysis of massive MIMO networks using stochastic geometry Tianyang Bai and Robert W. Heath Jr. Wireless Networking and Communications Group Department of Electrical and Computer Engineering The University
More informationAnalysis and Improvements of Linear Multi-user user MIMO Precoding Techniques
1 Analysis and Improvements of Linear Multi-user user MIMO Precoding Techniques Bin Song and Martin Haardt Outline 2 Multi-user user MIMO System (main topic in phase I and phase II) critical problem Downlink
More informationMultiple 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 informationMassive MIMO: Signal Structure, Efficient Processing, and Open Problems I
Massive MIMO: Signal Structure, Efficient Processing, and Open Problems I Saeid Haghighatshoar Communications and Information Theory Group (CommIT) Technische Universität Berlin CoSIP Winter Retreat Berlin,
More informationWINNER+ IMT-Advanced Evaluation Group
IEEE L802.16-10/0064 WINNER+ IMT-Advanced Evaluation Group Werner Mohr, Nokia-Siemens Networks Coordinator of WINNER+ project on behalf of WINNER+ http://projects.celtic-initiative.org/winner+/winner+
More informationAn Adaptive Algorithm for MU-MIMO using Spatial Channel Model
An Adaptive Algorithm for MU-MIMO using Spatial Channel Model SW Haider Shah, Shahzad Amin, Khalid Iqbal College of Electrical and Mechanical Engineering, National University of Science and Technology,
More informationMultiple 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 informationAuxiliary Beam Pair Enabled AoD Estimation for Large-scale mmwave MIMO Systems
Auxiliary Beam Pair Enabled AoD Estimation for Large-scale mmwave MIMO Systems Dalin Zhu, Junil Choi and Robert W. Heath Jr. Wireless Networking and Communications Group Department of Electrical and Computer
More informationPower 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 informationMIMO Receiver Design in Impulsive Noise
COPYRIGHT c 007. ALL RIGHTS RESERVED. 1 MIMO Receiver Design in Impulsive Noise Aditya Chopra and Kapil Gulati Final Project Report Advanced Space Time Communications Prof. Robert Heath December 7 th,
More informationDistributed 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 informationMaximum Throughput in a C-RAN Cluster with Limited Fronthaul Capacity
Maximum Throughput in a C-RAN Cluster with Limited Fronthaul Capacity Jialong Duan, Xavier Lagrange and Frédéric Guilloud Télécom Bretagne/IRISA, France Télécom Bretagne/Lab-STICC, France Email: {jialong.duan,
More informationSIGNAL 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 informationPerformance Comparison of MIMO Systems over AWGN and Rician Channels with Zero Forcing Receivers
Performance Comparison of MIMO Systems over AWGN and Rician Channels with Zero Forcing Receivers Navjot Kaur and Lavish Kansal Lovely Professional University, Phagwara, E-mails: er.navjot21@gmail.com,
More informationImproving MU-MIMO Performance in LTE-(Advanced) by Efficiently Exploiting Feedback Resources and through Dynamic Scheduling
Improving MU-MIMO Performance in LTE-(Advanced) by Efficiently Exploiting Feedback Resources and through Dynamic Scheduling Ankit Bhamri, Florian Kaltenberger, Raymond Knopp, Jyri Hämäläinen Eurecom, France
More informationOn Using Channel Prediction in Adaptive Beamforming Systems
On Using Channel rediction in Adaptive Beamforming Systems T. R. Ramya and Srikrishna Bhashyam Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai - 600 036, India. Email:
More informationPerformance 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 informationLow-Complexity Hybrid Precoding in Massive Multiuser MIMO Systems
Low-Complexity Hybrid Precoding in Massive Multiuser MIMO Systems Le Liang, Student Member, IEEE, Wei Xu, Member, IEEE, and Xiaodai Dong, Senior Member, IEEE 1 arxiv:1410.3947v1 [cs.it] 15 Oct 014 Abstract
More informationPerformance Analysis of MRT-Based Dual-Polarized Massive MIMO System with Space-Polarization Division Multiple Access
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 12, NO. 8, Aug. 2018 4006 Copyright c 2018 KSII Performance Analysis of MRT-Based Dual-Polarized Massive MIMO System with Space-Polarization Division
More informationTen Things You Should Know About MIMO
Ten Things You Should Know About MIMO 4G World 2009 presented by: David L. Barner www/agilent.com/find/4gworld Copyright 2009 Agilent Technologies, Inc. The Full Agenda Intro System Operation 1: Cellular
More informationTransmit Antenna Selection and User Selection in Multiuser MIMO Downlink Systems
Transmit Antenna Selection and User Selection in Multiuser MIMO Downlink Systems By: Mohammed Al-Shuraifi A Thesis Submitted in Fulfilment of the Requirements for the Degree of Doctor of Philosophy (PhD)
More informationOn 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 informationChannel 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 informationHybrid Transceivers for Massive MIMO - Some Recent Results
IEEE Globecom, Dec. 2015 for Massive MIMO - Some Recent Results Andreas F. Molisch Wireless Devices and Systems (WiDeS) Group Communication Sciences Institute University of Southern California (USC) 1
More informationMIMO 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 informationCooperative versus Full-Duplex Communication in Cellular Networks: A Comparison of the Total Degrees of Freedom. Amr El-Keyi and Halim Yanikomeroglu
Cooperative versus Full-Duplex Communication in Cellular Networks: A Comparison of the Total Degrees of Freedom Amr El-Keyi and Halim Yanikomeroglu Outline Introduction Full-duplex system Cooperative system
More informationNovel Detection Scheme for LSAS Multi User Scenario with LTE-A and MMB Channels
Novel Detection Scheme for LSAS Multi User Scenario with LTE-A MMB Channels Saransh Malik, Sangmi Moon, Hun Choi, Cheolhong Kim. Daeijin Kim, Intae Hwang, Non-Member, IEEE Abstract In this paper, we analyze
More informationTechnical Aspects of LTE Part I: OFDM
Technical Aspects of LTE Part I: OFDM By Mohammad Movahhedian, Ph.D., MIET, MIEEE m.movahhedian@mci.ir ITU regional workshop on Long-Term Evolution 9-11 Dec. 2013 Outline Motivation for LTE LTE Network
More informationImpact of LTE Precoding for Fixed and Adaptive Rank Transmission in Moving Relay Node System
Impact of LTE Precoding for Fixed and Adaptive Rank Transmission in Moving Relay Node System Ayotunde O. Laiyemo, Pekka Pirinen, and Matti Latva-aho Centre for Wireless Communications University of Oulu,
More informationUtilization of Channel Reciprocity in Advanced MIMO System
Utilization of Channel Reciprocity in Advanced MIMO System Qiubin Gao, Fei Qin, Shaohui Sun System and Standard Deptartment Datang Mobile Communications Equipment Co., Ltd. Beijing, China gaoqiubin@datangmobile.cn
More informationPerformance of CSI-based Multi-User MIMO for the LTE Downlink
Performance of CSI-based Multi-User MIMO for the LTE Downlink ABSTRACT Philipp Frank Deutsche Telekom Laboratories Ernst-Reuter-Platz 7 1587 Berlin, Germany philipp.frank@telekom.de We consider the application
More informationAntenna Design and Site Planning Considerations for MIMO
Antenna Design and Site Planning Considerations for MIMO Steve Ellingson Mobile & Portable Radio Research Group (MPRG) Dept. of Electrical & Computer Engineering Virginia Polytechnic Institute & State
More informationImplementation and evaluation of FD-MIMO beamforming schemes for highway scenarios
TECHNISCHE UNIVERSITÄT WIEN DIPLOMA THESIS Implementation and evaluation of FD-MIMO beamforming schemes for highway scenarios Author: Félix Pablo CANO PAÍNO Supervisor: Fjolla ADEMAJ Martin K. MÜLLER Stefan
More informationEE 5407 Part II: Spatial Based Wireless Communications
EE 5407 Part II: Spatial Based Wireless Communications Instructor: Prof. Rui Zhang E-mail: rzhang@i2r.a-star.edu.sg Website: http://www.ece.nus.edu.sg/stfpage/elezhang/ Lecture I: Introduction March 4,
More informationResearch Article Intercell Interference Coordination through Limited Feedback
Digital Multimedia Broadcasting Volume 21, Article ID 134919, 7 pages doi:1.1155/21/134919 Research Article Intercell Interference Coordination through Limited Feedback Lingjia Liu, 1 Jianzhong (Charlie)
More informationUse of Multiple-Antenna Technology in Modern Wireless Communication Systems
Use of in Modern Wireless Communication Systems Presenter: Engr. Dr. Noor M. Khan Professor Department of Electrical Engineering, Muhammad Ali Jinnah University, Islamabad Campus, Islamabad, PAKISTAN Ph:
More informationAnalysis of Novel Eigen Beam Forming Scheme with Power Allocation in LSAS
Analysis of Novel Eigen Beam Forming Scheme with Power Allocation in LSAS Saransh Malik, Sangmi Moon, Hun Choi, Cheolhong Kim. Daeijin Kim, and Intae Hwang, Non-Member, IEEE Abstract Massive MIMO (also
More informationFair Performance Comparison between CQI- and CSI-based MU-MIMO for the LTE Downlink
Fair Performance Comparison between CQI- and CSI-based MU-MIMO for the LTE Downlink Philipp Frank, Andreas Müller and Joachim Speidel Deutsche Telekom Laboratories, Berlin, Germany Institute of Telecommunications,
More informationUniversity of Bristol - Explore Bristol Research. Peer reviewed version. Link to published version (if available): /PIMRC.2009.
Beh, K. C., Doufexi, A., & Armour, S. M. D. (2009). On the performance of SU-MIMO and MU-MIMO in 3GPP LTE downlink. In IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications,
More informationREMOTE 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 information5G New Radio Design. Fall VTC-2017, Panel September 25 th, Expanding the human possibilities of technology to make our lives better
5G New Radio Design Expanding the human possibilities of technology to make our lives better Fall VTC-2017, Panel September 25 th, 2017 Dr. Amitabha Ghosh Head of Small Cell Research, Nokia Fellow, IEEE
More informationARQ 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 informationUser Grouping and Scheduling for Joint Spatial Division and Multiplexing in FDD Massive MIMO System
Int. J. Communications, Networ and System Sciences, 2017, 10, 176-185 http://www.scirp.org/journal/ijcns ISSN Online: 1913-3723 ISSN Print: 1913-3715 User rouping and Scheduling for Joint Spatial Division
More informationINVESTIGATION OF CAPACITY GAINS IN MIMO CORRELATED RICIAN FADING CHANNELS SYSTEMS
INVESTIGATION OF CAPACITY GAINS IN MIMO CORRELATED RICIAN FADING CHANNELS SYSTEMS NIRAV D PATEL 1, VIJAY K. PATEL 2 & DHARMESH SHAH 3 1&2 UVPCE, Ganpat University, 3 LCIT,Bhandu E-mail: Nirav12_02_1988@yahoo.com
More informationPerformance Evaluation of the VBLAST Algorithm in W-CDMA Systems
erformance Evaluation of the VBLAST Algorithm in W-CDMA Systems Dragan Samardzija, eter Wolniansky, Jonathan Ling Wireless Research Laboratory, Bell Labs, Lucent Technologies, 79 Holmdel-Keyport Road,
More informationBlock 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 informationUnquantized and Uncoded Channel State Information Feedback on Wireless Channels
Unquantized and Uncoded Channel State Information Feedback on Wireless Channels Dragan Samardzija Wireless Research Laboratory Bell Labs, Lucent Technologies 79 Holmdel-Keyport Road Holmdel, NJ 07733,
More informationBase-station Antenna Pattern Design for Maximizing Average Channel Capacity in Indoor MIMO System
MIMO Capacity Expansion Antenna Pattern Base-station Antenna Pattern Design for Maximizing Average Channel Capacity in Indoor MIMO System We present an antenna-pattern design method for maximizing average
More informationAdaptive 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 informationAN EFFICIENT LINK PERFOMANCE ESTIMATION TECHNIQUE FOR MIMO-OFDM SYSTEMS
AN EFFICIENT LINK PERFOMANCE ESTIMATION TECHNIQUE FOR MIMO-OFDM SYSTEMS 1 K. A. Narayana Reddy, 2 G. Madhavi Latha, 3 P.V.Ramana 1 4 th sem, M.Tech (Digital Electronics and Communication Systems), Sree
More informationTHE 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