Precoding and Massive MIMO

Size: px
Start display at page:

Download "Precoding and Massive MIMO"

Transcription

1 Precoding and Massive MIMO Jinho Choi School of Information and Communications GIST October / 64

2 1. Introduction 2. Overview of Beamforming Techniques 3. Cooperative (Network) MIMO 3.1 Multicell with a common group of users 3.2 Multicell with different groups of users 4. Massive MIMO 4.1 Partial cooperation 4.2 Pilot contamination in massive MIMO 4.3 Pilot contamination precoding 5. Conclusions 2 / 64

3 1. Introduction 1. Introduction Beamforming with antenna arrays has been studied for wireless communications since early 90s. Transmit and receive beamforming have been considered for cellular systems to improve the signal-to-noise ratio (SNR) or extend the coverage. A better beamforming gain can be achieved if the number of antennas in an array is large. In cellular systems, as base stations (BSs) can have a number of antennas, beamforming can be easily employed at BSs. In this case, transmit beamforming becomes downlink beamforming and receive beamforming becomes uplink beamforming. 3 / 64

4 44/60 / 64 Precoding Beamforming and and Massive Massive MIMO MIMO 1. Introduction Downlink beamforming = transmit beamforming at BS Beamformer w 1 Data symbols w 2 Dynamic or static Beampattern w L

5 1. Introduction Unfortunately, due to various problems, the number of antenna elements in an array for downlink beamforming is limited. However, in 5G, transmit (or downlink) beamforming has been considered seriously to extend the coverage. In this tutorial, we review existing downlink beamforming approaches and focus on cooperative and noncooperative approaches in multi-cell MIMO systems. In the end, we wil attempt to highlight the differences between network MIMO and massive MIMO in multi-cell systems. 5 / 64

6 1. Introduction How transmit beamforming works: TX 1 h 1 TX 2 h 2 Let h 1 and h 2 denote the channel coefficients from TX antennas 1 and 2, respectively. If TX antenna k transmits w k s, where w k is the weight and s is the signal to be transmitted, the received signal becomes r = h 1 w 1 s + h 2 w 2 s + n = (h 1 w 1 + h 2 w 2 )s + n, where n is the background noise. 6 / 64

7 1. Introduction The SNR at the receiver becomes SNR = h 1w 1 + h 2 w 2 2 E s N 0 h 2 w 2 E s N 0, where the equality holds if w k h k. A full diversity order is equal to the number of antennas. The more transmit antennas, the better performance. However, the channel state information (CSI) is required for transmit beamforming to achieve the maximum SNR. If the transmitter does not know the CSI, the receiver has to feed back it to the transmitter. With limited feedback, the number of antennas cannot be large, which has been one of the major drawbacks of transmit beamforming, which may not be a drawback in TDD mode (using the channel reciprocity). 7 / 64

8 2. Overview of Beamforming Techniques 2. Overview of Beamforming Techniques There are three different systems where beamforming can be employed: A. Point-to-point MIMO (single-user beamforming) B. Multiuser (point-to-multipoint) MIMO (multiuser beamforming) C. Multipoint-to-multipoint (or network) MIMO (cooperative beamforming) We can also consider a different system in which beamforming plays a crucial role: D. Massive MIMO: this is the case where single-user beamforming is employed in a multi-cell system For cellular systems, single-cell: point-to-point or multiuser MIMO multi-cell: network MIMO (there is inter-cell interference problem), massive MIMO 8 / 64

9 2. Overview of Beamforming Techniques A. Point-to-point MIMO To achieve diversity gain: beamforming with CSI at transmitter space-time coding (STC) without CSI at transmitter To achieve multiplexing gain: SVD with CSI at transmitter BLAST techniques without CSI at transmitter There are also other techniques that enjoy the trade-off between diversity and multiplexing gains Ref. Zheng and Tse, Diversity and Multiplexing: A Fundamental Tradeoff in Multiple Antenna Channels, IEEE TIT, May / 64

10 2. Overview of Beamforming Techniques Beamforming for point-to-point MIMO It is often known as single-user beamforming To maximize the SNR, the principle of matched filtering (MF) can be employed when CSI is available, which is known as maximal ratio transmission (MRT) scheme in the context of MIMO. There are also other approaches without CSI or partial CSI: Blind beamforming (or long-term transmit beamforming) with statistical properties of channels Semi-blind beamforming (with partial CSI) Diversity beamforming (with channel coding): compared to blind beamforming, it can achieve a diversity gain Ref. J. Choi, Diversity eigenbeamforming for coded signals, IEEE TCOM, June / 64

11 2. Overview of Beamforming Techniques 10 1 Coded D eigen Eigen 1 Optimized 10 2 BER SNR (db) Performance of blind beamforming (Eigen-1), diversity beamforming (D-eigen and Optimized) 11 / 64

12 2. Overview of Beamforming Techniques B. Multiuser MIMO There exists interference due to the presence of multiple signals to be transmitted to multiple users. Dirty paper coding (DPC) can achieve the channel capacity by suppresing known interference. However, its implementation is not easy. Multiuser beamforming can provide a reasonable performance with low-complexity. A better performance can be achieved with multiuser diversity & user selection CSI at BS is required to mitigate the (intra-cell) interference: No feedback in TDD: channel reciprocity can be used Feedback in FDD: excessive overhead Resource allocation (including power control) becomes a crucial. 12 / 64

13 2. Overview of Beamforming Techniques Capacity of multiuser MIMO Dirty paper coding: it can achieve the capacity, but difficult to implement C sum = E max P k log det(i + HPH H ) E log det(i + P total K HHH )(equal power) Multiuser diversity: it is simple, but overall throughput is low C md = E log(1 + P total max h k 2 ) k Multiuser beamforming: if user selection is combined, it its upper bound is proportional to the number of selected users (scaling law is applied): C mb E log(1 + P k h k 2 ) k U orthogonal 13 / 64

14 2. Overview of Beamforming Techniques 30 C sum (equal power) C md 25 C mbeam (approx.) Achievable Rate 60 users, 4 users are selected in multiuser downlink beamforming 14 / 64

15 2. Overview of Beamforming Techniques Conventional Multiuser Beamforming Problem (without user selection) Base station Mobile terminal Mobile terminal Mobile terminal w k : beamforming vector to user k; h k : channel vector to user k, P k = E[ s k 2 ]: TX power to user k multiuser beamforming, where s k is the signal to user k TX signal: K k=1 w ks k 15 / 64

16 2. Overview of Beamforming Techniques Multiuser joint beamforming and power control: { ˆP k, ŵ k } = arg min { k P k P q h H q w q 2 subject to k q P γ k h H q w k 2 +σq 2 q w q 2 = 1 This is an optimization problem to minimize the total transmission power subject to SINR constraints. Based on the uplink-downlink duality, this problem has been solved. 16 / 64

17 2. Overview of Beamforming Techniques C. Network (or Cooperative) MIMO Highly sophisticated systems to deal with intra-cell as well as inter-cell interference (ICI) Known techniques Cooperative transmissions (e.g., CoMP) Interference alignment Distributed nature Backhaul transmissions Interference channel models There should be the trade-off between performance and complexity (or overhead) 17 / 64

18 2. Overview of Beamforming Techniques TX RX TX RX TX RX 18 / 64

19 2. Overview of Beamforming Techniques Remarks on beamforming in network MIMO: Advantages Easy to implement Optimal solutions to most beamforming optimization problems are known (QoS can be guaranteed) Spatial multiplexing gain with reasonable performances Disadvantages Not capacity-achieving schemes Most (downlink) beamforming methods require CSI at BS. Furthermore, in network MIMO, BSs in cooperation need to share their CSI. Dilemma in FDD mode Due to limited CSI feedback, it is prohibitive to use large antenna arrays. However, with arrays of a few antenna elements, a significant performance improvement may not be achieved. 19 / 64

20 2. Overview of Beamforming Techniques D. Massive MIMO TDD mode to exploit the channel reciprocity for the CSI at BS and make use of large antenna arrays Multi-cell, but noncooperative systems Due to noncooperative transmissions, no backhaul communications between BSs are required. But, systems can suffer from ICI. Using massive antenna arrays, ICI can be mitigated. Key issues: pilot contamination, array calibration, etc. There are other advantages. One of them is that the short-term fading disappears by the law of large numbers. This makes resource allocation easy and reduces the burden of channel coding. 20 / 64

21 3. Cooperative (Network) MIMO 3. Cooperative (Network) MIMO Network MIMO is to fully exploit the spatial gain in a multi-cell system In network MIMO, intercell interference can be mitigated by cooperation between BSs through backhaul links: Ref. 1 Karakayali, et al., Network coordination for spectrally efficient communications in cellular systems, IEEE Comm. Mag., Ref. 2 Gesbert, et al., Multi-cell MIMO cooperative networks: a new look at interference, IEEE JSAC, Provided that each BS in cooperation is equipped with an antenna array, cooperative beamforming can mitigate intercell as well as intracell interference in downlink transmissions. Regardless of its implementability, it may provide performance bounds. 21 / 64

22 3. Cooperative (Network) MIMO BS BS BS BSs in cooperation need to share users data and/or CSI to their users through a backhaul network. 22 / 64

23 3. Cooperative (Network) MIMO Levels of cooperation between BSs through backhaul links: full cooperation: BSs share all CSI and signals (this is equivalent to the case of a big BS with distributed arrays or a single big cell) partial cooperation 1: BSs share all CSI, but not signals partial cooperation 2: BSs share all signals, but not CSI partial cooperation 3: each BS has its local CSI and signals (but limited information can be exchanged between BSs for such as user allocation) The cooperation would be limited by backhaul overhead. Thus, effective partial cooperation, not full cooperation, is desirable in practice. 23 / 64

24 3. Cooperative (Network) MIMO 3.1 Multicell with a common group of users 3.1 Multicell with a common group of users 1. All BSs in cooperation support a common group of users (these users would be cell-edge users). 2. The cooperation between BSs is limited (if there is no limitation, this scenario is equivalent to a single-cell). 3. Each BS has local CSI, which is the channel vectors from the BS to all users in a common group, while other BSs CSI is unknown. 4. There could be some cooperation between BSs such as user allocation. 24 / 64

25 3. Cooperative (Network) MIMO 3.1 Multicell with a common group of users ZF-DPC precoding in a multicell system Ref. Ho, et al., Decentralized precoding for multicell MIMO downlink, IEEE TWC, Assumptions: There are Q BSs in cooperation and N users per cell (there are K = QN users in total). Each BS is equipped with an antenna array of L elements. The CSI from each BS to all K users (not just N in the cell) is known by each BS, while the CSI from the other BSs to users is not known. That is, at BS q, the CSI to K users are known, but not the CSI from BS q q to K users. This partial CSI can allow block diagonalization to suppress intercell interference Within a cell, then DPC is used to suppress intracell interference 25 / 64

26 3. Cooperative (Network) MIMO 3.1 Multicell with a common group of users Key variables: H q : L K downlink channel matrix from BS q to all K users G q : L N submatrix of H q (channel vectors to users in cell q from B W q : L N beamforming matrix at BS q s q : N 1 signal vector at BS q r q : N 1 signal vector received by users in cell q n q : N 1 noise vector received by users in cell q Let H q = [G q H q ]. The received signal vector at users in cell q is given by Q r q = G H q W p s p + n q p=1 Block diagonalization to avoid intercell interference: H H qw q = / 64

27 3. Cooperative (Network) MIMO 3.1 Multicell with a common group of users For block diagonalization, orthogonal projection matrix can be used: P q = I H q ( H H q H q ) 1 H H q. The beamforming matrix can be W q = ζ q P q G q, where ζ q is the normalization factor that is given by ζ q = N P q G q F The average power per beamforming vector is unity. 27 / 64

28 3. Cooperative (Network) MIMO 3.1 Multicell with a common group of users BS1 W1 G1 H 1 user 1 H 2 BS2 user 2 W2 G2 Then, the received signal vector becomes Q r q = G H q ζ q P p G p s p + n q p=1 = ζ q G H q P q G q s q + n q. 28 / 64

29 3. Cooperative (Network) MIMO 3.1 Multicell with a common group of users Within r q, there exists intracell interference. Thus, s q has to be DPCed signals. The sum rate becomes C = Q E [ log 2 det ( I + Ω ζ q 2 Ψ q Ψ H )] q q=1 where E[n q n H q ] = I, E[s q s H q ] = ΩI, and Ψ q = G H q P q G q. 29 / 64

30 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users 3.2 Multicell with different groups of users Each BS has its own group of users. There are Q BSs and each BS has N users. Each BS is equipped with L antennas for beamforming. In this case, local CSI at BS is not sufficient to suppress the intercell interference. In order to migitate both intercell and intracell interference by beamforming, SINR could be considered. 30 / 64

31 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Key variables: H q,k : W q : s q : the L N channel matrix from BS k to the users in cell q the L N beamforming matrix the N 1 signal vector from BS q Local CSI at BS k is {H 1,k, H 2,k,..., H Q,k }. W q = [w 1;q... w N;q ] decides the transmission power if E[s q s H q ] = I, i.e., P i;q = w i;q / 64

32 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users The received signal at cell q is given by Q r q = H H q,k W ks k + n q. k=1 At user i in cell q, the received signal becomes Q N r i;q = w u;k s u;k + n i;q h H i;q,k k=1 u=1 = h H i;q,qw i;q s i;q + u i h H i;q,qw u;q s u;q } {{ } =intracell + N h H i;q,k w u;ks u;k +n i;q k q u=1 }{{} =intercell 32 / 64

33 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users The SINR becomes SINR i;q = h H i;q,q w i;q 2 u i hh i;q,q w u;q 2 + N k q u=1 hh i;q,k w u;k 2 + σi;q 2 In general, any beamforming algorithm that minimizes the SINR requires full CSI, which requires significant backhaul transmissions. Define the intercell interference at cell q as Z i;q = k q N h H i;q,k w u;k 2. u=1 Note that this interference is not a function of W q. 33 / 64

34 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Well-known multiuser beamforming problems: Maximization of SINR with power constraints max Wq SINR i;q subject to W q 2 F P q Minimization of power with SINR constraints min q W q 2 F subject to SINR i;q Γ i;q See SINR-based Joint Power Control and Beamforming to solve the above problem 34 / 64

35 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Interference leakage based approaches are also popular: Define the interference leakage from BS q to cell k as I k (W q ) = H H k,q W q 2 F. To minimize the leakage to the other cells, we can have the following constraint: I k (W q ) = tr W q H H k,q H H k,q W q I q,leak. k q k q 35 / 64

36 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Distributed optimization approaches: Forming a global optimization problem Decompose it into local optimization problems Each local optimization problem is solved at a BS and update variables through backhaul links Do iterations until a satisfactory performance is achieved Key references: Palomar and Chiang, A tutorial on decomposition methods for network utility maximization, IEEE JSAC, Chiang, et al., Layering as optimization decomposition: a mathematical theory of network architectures, Proc. IEEE, / 64

37 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Example: Dual decomposition The minimization of power with SINR constraints: where C q (W q ) = W q 2 F. Using Lagrangian multipliers, min = min Q q=1 C q(w q ) subject to SINR i;q (W q ) Γ i;q Q C q (W q ) q=1 Q q=1 ( Q q=1 i=1 N λ i;q (SINR i;q (W q ) Γ i;q ) N min C q (W q ) λ i;q (SINR i;q (W q ) Γ i;q ) W q i=1 }{{} =local optimization ) 37 / 64

38 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users At each BS, the local optimiztion is carried out as follows: Ŵ q = arg min C q (W q ) W q N λ i;q SINR i;q (W q ) i=1 At a higher level (i.e., at a central unit), the master dual problem is to update λ s: where min λ Q g q (λ) q=1 N λ i;q Γ i;q, λ 0, i=1 g q (λ) = N λ i;q SINR i;q (Ŵq) C q (Ŵq). i=1 38 / 64

39 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users To update λ s at a higher level, the following gradient method can be used: [ ( )] + λ i;q (t + 1) = λ i;q (t) α SINR i;q (Ŵq) Γ i;q, where [x] + = max{0, x} and α > 0 is the step-size. Thus, BSs need to send their SINRs to a central unit and this central unit sends λ s to BSs. This approach would have a slow convergence rate, but it does not require excessive backhaul transmissions between BSs and a central unit. 39 / 64

40 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Example Minimization interference leakage problem with SINR min Q q=1 Q k=1 I k(w q ) subject to SINR i;q (W q ) Γ i;q A direct decomposition can be considered. At each BS, min i wh i Xw i h subject to H i w i 2 Γ q i hh i wq 2 +Z i i X k q H k,qh H k,q w i w i;q h i h i;q Z i Z i;q + σi;q 2 Γ i Γ i;q 40 / 64

41 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Since the ICI Z i;q (W q ) in the SINR depends on the other beamforming matrices, an iterative algorithm is required to solve the NML beamforming problem, which consists of the following two key steps. Solving the local minimization problem (at BS q): subject to W q (l) = arg min C q (W q ) h H i;q,q w i;q 2 u i hh i;q,q wu;q 2 +Z i;q (W (l 1) q )+σi;q 2 Γ i;q updating the ICI: Z i;q (W (l) q ) = N h H i;q,k w(l) u;k 2, k q u=1 which is fed back by the users (not by other BSs) to BS q. 41 / 64

42 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Low-Dimensional Beamforming Consider the following eigendecomposition: X = k q H k,q H H k,q + αi = EΛEH, where E = [e 1... e L ] and Λ = diag(λ 1,..., λ L ). The local cost function becomes C q (W) = I k (W) = tr ( W H XW ) k q The complexity can be reduced if a subspace of E used as w i w i = Ẽ Λ 1/2 v i, where Ẽ = [e 1... e M ] and M < L. 42 / 64

43 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users With v i of size M 1, we have C q (W) N v i 2 The local optimization at BS q is carried out to minimize C q (W) through v i of size M 1. The subspace beamforming with optimized v i (not w i ) can reduce the complexity by a factor of ( L M ) 2. For highly correlated channels, this approximation may not result in a significant loss. The complexity of the eigendecomposition of X may not be significant if second order statistics are used: i=1 X E[X] = k q E[H k,q H H k,q ] + αi = EΛEH. 43 / 64

44 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Simulation Results Three different approaches are used: Approach A: The cooperative min-power beamforming with target SINR Approach B: The NML beamforming with target SINR Approach C: The noncooprative minimum total power (NMP) beamforming with the following cost function: C q (W q ) = W q 2 F, which is to reduce the transmission power rather than interference leakage (an egoistic approach). Approach A is cooperative, while Approaches B and C are noncooperative (i.e., no backhaul communications between BSs) The elements of channel matrices are independent Gaussian (no spatial correlation is considered) 44 / 64

45 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Feasibility results: The NMP beamforming (Approach C) cannot achieve target SINR, but both cooperative min-power beamforming and NML beamforming (Approaches A and B) can achieve target SINR Cooperative Noncoop. (min power) Noncoop. (min leakage) Achieved SINR (db) Target SINR (db) L = 30, Q = 3, N = 5, and M = L = / 64

46 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Approach A performs better than Approaches B and C, but requires backhaul communications between BSs Cooperative Noncoop. (min power) Noncoop. (min leakage) 60 Total transmission power (db) Target SINR (db) L = 30, Q = 3, N = 5, and M = L = / 64

47 3. Cooperative (Network) MIMO 3.2 Multicell with different groups of users Low-complexity versions of Approach B (NML): tradeoff between complexity and performance If M 20, no performance degradation is observed (note that uncorrelated channels are used in simulations) Total transmission power (db) M L = 30, Q = 3, N = 5, and Γ = 10 db 47 / 64

48 4. Massive MIMO 4. Massive MIMO Proposed by Marzetta in 2010 to effectively mitigate ICI without any cooperation between BSs A massive MIMO system consists of BSs with large antenna arrays. The number of antenna elements is about 100. Frequency reuse factor is 1 and orthogonal channels within a cell (no intra-cell interference) TDD to exploit the channel reciprocity. The channel vector, h, from a BS to a user can be factorized as h = βu, where β is a parameter for large-scale fading and u CN ( 0, 1 L I) is a random vector for small-scale fading, where L / 64

49 4. Massive MIMO Base Station Large Array narrow beam user user In each cell, each user has an orthogonal channel (i.e., no intra-cell interference) and h k,q denotes the channel vector from BS q to the user in cell k. 49 / 64

50 4. Massive MIMO For a large L, the matched-filter (MF) beamforming can be used. The beamforming vector is given by w k = h k,k h k,k. Since the received signal at the user in cell k is r k = h H k,k w ks k + q k h H k,q w qs q + n k, the SINR becomes SINR k = P k h k,k 2 q k P q h H k,q w q 2 + σk / 64

51 4. Massive MIMO ICI can be mitigated as L as the inner product of two random vectors, x = h k,k and y = h k,q, k q, approaches <x,y> L: number of antennas This is the key idea of massive MIMO. That is, without any cooperation between BSs, it is possible to mitigate ICI by increasing L (similar to CDMA). 51 / 64

52 4. Massive MIMO Approximation for a large L: h k,q 2 β k,q u H k,q w q CN (0, 1/L) P q h H k,q w q 2 +σk 2 = q k q k The approximate SINR is P q β k u H k,q w q 2 +σk 2 1 P q β k,q +σk 2 L. q k SINR k = LP k β k,k q k P qβ k,q + Lσk 2 In SINR, small-scale fading terms disappear the SINR is much less fluctuated over time. 52 / 64

53 4. Massive MIMO 4.1 Partial cooperation 4.1 Partial cooperation Since the frequency reuse factor is 1, there exists ICI although it may not be significant for a large L (as shown in the SINR expression). ICI can be further mitigated if ZF or MMSE beamforming can be used in cooperation with adjacent BSs as in network MIMO. However, it requires massive CSI exchange, which would provide a marginal gain at the expense of excessive backhaul communications. There might be a tradeoff between ICI mitigation (high ICI for a small L) performance and the backhaul overhead (a large overhead for a large L) in the case of cooperation. 53 / 64

54 4. Massive MIMO 4.1 Partial cooperation In order to improve the performance further, partial cooperation can be considered. Not exchange the CSI of fast-fading, but the CSI of slow-fading, which is β k for large-scale fading. Then, the joint power control is carried out to minimize the total power of BSs in cooperation with SINR constraints. This partial cooperation can guarantee target SINR in massive MIMO. 54 / 64

55 4. Massive MIMO 4.1 Partial cooperation average/minimum SINR (db) Average SINR (equal power) Average SINR (opt. power) Minimum SINR (equal power) Minimum SINR (opt. power) target SINR (db) Arrays of L = 100 elements, 3-cell, 4 users per cell 55 / 64

56 4. Massive MIMO 4.2 Pilot contamination in massive MIMO 4.2 Pilot contamination in massive MIMO A main issue of massive MIMO is pilot contamination. As the coherence time is limited and the number of users per cell can be large, the same set of orthogonal pilot sequences can be used in all cells, which results in interference from adjacent cell during the uplink training. The estimated uplink channel can be contaminated by the interference and the performance of beamforming can be degraded. This problem is called the pilot contamination. To mitigate this problem, we can take into account interfering pilot signals from adjacent cells in estimating uplink channels; adjust precoding vectors to reduce the interference. 56 / 64

57 4. Massive MIMO 4.2 Pilot contamination in massive MIMO Two-cell example (TDD: channel reciprocity) Assume that MRT precoding vectors are used from estimated channels. 57 / 64

58 4. Massive MIMO 4.3 Pilot contamination precoding 4.3 Pilot contamination precoding For a large L, statistical precoding can be used to mitigate the intercell interference resulting from pilot contamination. This scheme is called the pilot contamination precoding (PCP). Assume that there are K cells. The estimated channel vector at the BS in cell k is ĥ k = K h q,k + n k. q=1 The MF beamforming vector is used as: ĥk w k = ĥk. 58 / 64

59 4. Massive MIMO 4.3 Pilot contamination precoding Received signal at the user in cell k: x k = = K h H k,q w qs q + v k q=1 K q=1 s q ˆα q K h H k,q h t,q + v k, t=1 where ˆα k = ĥk. Since h k,q = β k,q u k,q and u k,q are iid, we have Thus, as L, we have K x k = u H k,q u t,q δ k,q as L w.p. 1 q=1 s q ˆα q β k,q + v k. 59 / 64

60 4. Massive MIMO 4.3 Pilot contamination precoding Staking x k s: x = [x 1... x K ] T [ ] βk,q = [s 1... s K ] T + [v 1... v K ] T α q = As + v, where [A] k,q = β k,q α q. To avoid the interference, s can be replaced with s Bs, where B = A 1. This means that the BSs need to exchange the channel information (elements in A) for precoding. The resulting received signal is x = s + v. 60 / 64

61 4. Massive MIMO 4.3 Pilot contamination precoding Since each BS needs to estimate β k,q and ˆα q = ĥq, which are related to slow fading coefficients only, their estimation can be done precisely, and their exchange through a backhaul network may not result in a heavy overhead. Note that the transmission power depends on B = A 1. DPC or VP technique can help reduce the total transmission power when precoding is used. 61 / 64

62 4. Massive MIMO 4.3 Pilot contamination precoding Improvement by partial cooperation against pilot contamination without PCP with PCP 62 / 64

63 As a remedy, partial cooperation could be considered. 63 / 64 Precoding and Massive MIMO 5. Conclusions 5. Conclusions Beamforming is a simple and effective means to increase SNR or coverage, which can be employed in more complicated systems as follows: Network MIMO Cooperation between BSs with not too big arrays More controls (for better performance) Backhaul overhead for cooperation might be a critical issue Distributed optimization will play a key role in reducing backhaul overhead Massive MIMO Noncooperation, each BS with a big array might be robust enough against ICI Less controls (for implementation) There exists ICI, and due to it, there is difficulty to guarantee certain performance in terms of SINR.

64 5. Conclusions Network MIMO versus Massive MIMO: Network MIMO is to reduce cooperation (full to partial cooperation) Massive MIMO is to introduce cooperation (no cooperation to partial cooperation) In the end, network MIMO meets massive MIMO. 64 / 64

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

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

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

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

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

Decentralized Resource Allocation and Effective CSI Signaling in Dense TDD Networks

Decentralized Resource Allocation and Effective CSI Signaling in Dense TDD Networks Decentralized Resource Allocation and Effective CSI Signaling in Dense TDD Networks 1 Decentralized Resource Allocation and Effective CSI Signaling in Dense TDD Networks Antti Tölli with Praneeth Jayasinghe,

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

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

Channel Estimation and Multiple Access in Massive MIMO Systems. Junjie Ma, Chongbin Xu and Li Ping City University of Hong Kong, Hong Kong

Channel Estimation and Multiple Access in Massive MIMO Systems. Junjie Ma, Chongbin Xu and Li Ping City University of Hong Kong, Hong Kong Channel Estimation and Multiple Access in Massive MIMO Systems Junjie Ma, Chongbin Xu and Li Ping City University of Hong Kong, Hong Kong 1 Main references Li Ping, Lihai Liu, Keying Wu, and W. K. Leung,

More information

Analysis of massive MIMO networks using stochastic geometry

Analysis 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 information

MU-MIMO in LTE/LTE-A Performance Analysis. Rizwan GHAFFAR, Biljana BADIC

MU-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 information

742 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 8, NO. 5, OCTOBER An Overview of Massive MIMO: Benefits and Challenges

742 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 8, NO. 5, OCTOBER An Overview of Massive MIMO: Benefits and Challenges 742 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 8, NO. 5, OCTOBER 2014 An Overview of Massive MIMO: Benefits and Challenges Lu Lu, Student Member, IEEE, Geoffrey Ye Li, Fellow, IEEE, A.

More information

Massive MIMO a overview. Chandrasekaran CEWiT

Massive 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 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

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

Analysis and Improvements of Linear Multi-user user MIMO Precoding Techniques

Analysis 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 information

ON PILOT CONTAMINATION IN MASSIVE MULTIPLE-INPUT MULTIPLE- OUTPUT SYSTEM WITH LEAST SQUARE METHOD AND ZERO FORCING RECEIVER

ON 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 information

System Performance of Cooperative Massive MIMO Downlink 5G Cellular Systems

System 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 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

Blind Pilot Decontamination

Blind Pilot Decontamination Blind Pilot Decontamination Ralf R. Müller Professor for Digital Communications Friedrich-Alexander University Erlangen-Nuremberg Adjunct Professor for Wireless Networks Norwegian University of Science

More information

Optimized Data Symbol Allocation in Multicell MIMO Channels

Optimized Data Symbol Allocation in Multicell MIMO Channels Optimized Data Symbol Allocation in Multicell MIMO Channels Rajeev Gangula, Paul de Kerret, David Gesbert and Maha Al Odeh Mobile Communications Department, Eurecom 9 route des Crêtes, 06560 Sophia Antipolis,

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

A Large-Scale MIMO Precoding Algorithm Based on Iterative Interference Alignment

A Large-Scale MIMO Precoding Algorithm Based on Iterative Interference Alignment BUGARAN ACADEMY OF SCENCES CYBERNETCS AND NFORMATON TECNOOGES Volume 14, No 3 Sofia 014 Print SSN: 1311-970; Online SSN: 1314-4081 DO: 10478/cait-014-0033 A arge-scale MMO Precoding Algorithm Based on

More information

On 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 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 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

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

Distributed Multi- Cell Downlink Transmission based on Local CSI

Distributed Multi- Cell Downlink Transmission based on Local CSI Distributed Multi- Cell Downlink Transmission based on Local CSI Mario Castañeda, Nikola Vučić (Huawei Technologies Düsseldorf GmbH, Munich, Germany), Antti Tölli (University of Oulu, Oulu, Finland), Eeva

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

What is the Role of MIMO in Future Cellular Networks: Massive? Coordinated? mmwave?

What is the Role of MIMO in Future Cellular Networks: Massive? Coordinated? mmwave? What is the Role of MIMO in Future Cellular Networks: Massive? Coordinated? mmwave? Robert W. Heath Jr. The University of Texas at Austin Wireless Networking and Communications Group www.profheath.org

More information

Pilot-Decontamination in Massive MIMO Systems via Network Pilot Data Alignment

Pilot-Decontamination in Massive MIMO Systems via Network Pilot Data Alignment Pilot-Decontamination in Massive MIMO Systems via Network Pilot Data Alignment Majid Nasiri Khormuji Huawei Technologies Sweden AB, Stockholm Email: majid.n.k@ieee.org Abstract We propose a pilot decontamination

More information

LIMITED DOWNLINK NETWORK COORDINATION IN CELLULAR NETWORKS

LIMITED DOWNLINK NETWORK COORDINATION IN CELLULAR NETWORKS LIMITED DOWNLINK NETWORK COORDINATION IN CELLULAR NETWORKS ABSTRACT Federico Boccardi Bell Labs, Alcatel-Lucent Swindon, UK We investigate the downlink throughput of cellular systems where groups of M

More information

Massive MIMO: Signal Structure, Efficient Processing, and Open Problems I

Massive 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 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

Interference Mitigation by MIMO Cooperation and Coordination - Theory and Implementation Challenges

Interference Mitigation by MIMO Cooperation and Coordination - Theory and Implementation Challenges Interference Mitigation by MIMO Cooperation and Coordination - Theory and Implementation Challenges Vincent Lau Dept of ECE, Hong Kong University of Science and Technology Background 2 Traditional Interference

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

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

Tuning the Receiver Structure and the Pilot-to-Data Power Ratio in Multiple Input Multiple Output Systems

Tuning the Receiver Structure and the Pilot-to-Data Power Ratio in Multiple Input Multiple Output Systems Tuning the Receiver Structure and the Pilot-to-Data Power Ratio in Multiple Input Multiple Output Systems Gabor Fodor Ericsson Research Royal Institute of Technology 5G: Scenarios & Requirements Traffic

More information

Opportunistic Communication in Wireless Networks

Opportunistic Communication in Wireless Networks Opportunistic Communication in Wireless Networks David Tse Department of EECS, U.C. Berkeley October 10, 2001 Networking, Communications and DSP Seminar Communication over Wireless Channels Fundamental

More information

Beamforming with Imperfect CSI

Beamforming with Imperfect CSI This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 007 proceedings Beamforming with Imperfect CSI Ye (Geoffrey) Li

More information

arxiv: v2 [eess.sp] 31 Dec 2018

arxiv: v2 [eess.sp] 31 Dec 2018 Cooperative Energy Efficient Power Allocation Algorithm for Downlink Massive MIMO Saeed Sadeghi Vilni Abstract arxiv:1804.03932v2 [eess.sp] 31 Dec 2018 Massive multiple input multiple output (MIMO) is

More information

Random Beamforming with Multi-beam Selection for MIMO Broadcast Channels

Random Beamforming with Multi-beam Selection for MIMO Broadcast Channels Random Beamforming with Multi-beam Selection for MIMO Broadcast Channels Kai Zhang and Zhisheng Niu Dept. of Electronic Engineering, Tsinghua University Beijing 84, China zhangkai98@mails.tsinghua.e.cn,

More information

S. Mohammad Razavizadeh. Mobile Broadband Network Research Group (MBNRG) Iran University of Science and Technology (IUST)

S. Mohammad Razavizadeh. Mobile Broadband Network Research Group (MBNRG) Iran University of Science and Technology (IUST) S. Mohammad Razavizadeh Mobile Broadband Network Research Group (MBNRG) Iran University of Science and Technology (IUST) 2 Evolution of Wireless Networks AMPS GSM GPRS EDGE UMTS HSDPA HSUPA HSPA+ LTE LTE-A

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

Channel Modelling ETI 085. Antennas Multiple antenna systems. Antennas in real channels. Lecture no: Important antenna parameters

Channel 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 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

Joint User Selection and Beamforming Schemes for Inter-Operator Spectrum Sharing

Joint User Selection and Beamforming Schemes for Inter-Operator Spectrum Sharing Joint User Selection and Beamforming Schemes for Inter-Operator Spectrum Sharing Johannes Lindblom, Erik G. Larsson and Eleftherios Karipidis Linköping University Post Print N.B.: When citing this work,

More information

Performance Analysis of Massive MIMO Downlink System with Imperfect Channel State Information

Performance 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 information

Optimizing Multi-Cell Massive MIMO for Spectral Efficiency

Optimizing Multi-Cell Massive MIMO for Spectral Efficiency Optimizing Multi-Cell Massive MIMO for Spectral Efficiency How Many Users Should Be Scheduled? Emil Björnson 1, Erik G. Larsson 1, Mérouane Debbah 2 1 Linköping University, Linköping, Sweden 2 Supélec,

More information

Ten Things You Should Know About MIMO

Ten 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 information

Performance Evaluation of the VBLAST Algorithm in W-CDMA Systems

Performance 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 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

Unquantized and Uncoded Channel State Information Feedback on Wireless Channels

Unquantized 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 information

Beamforming and Transmission Power Optimization

Beamforming and Transmission Power Optimization Beamforming and Transmission Power Optimization Reeta Chhatani 1, Alice Cheeran 2 PhD Scholar, Victoria Jubilee Technical Institute, Mumbai, India 1 Professor, Victoria Jubilee Technical Institute, Mumbai,

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

Analysis of Massive MIMO With Hardware Impairments and Different Channel Models

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 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

BLOCK-DIAGONAL GEOMETRIC MEAN DECOMPOSITION (BD-GMD) FOR MULTIUSER MIMO BROADCAST CHANNELS

BLOCK-DIAGONAL GEOMETRIC MEAN DECOMPOSITION (BD-GMD) FOR MULTIUSER MIMO BROADCAST CHANNELS BLOCK-DIAGONAL GEOMETRIC MEAN DECOMPOSITION (BD-GMD) FOR MULTIUSER MIMO BROADCAST CHANNELS Shaowei Lin Winston W. L. Ho Ying-Chang Liang, Senior Member, IEEE Institute for Infocomm Research 21 Heng Mui

More information

Training in Massive MIMO Systems. Wan Amirul Wan Mohd Mahyiddin

Training in Massive MIMO Systems. Wan Amirul Wan Mohd Mahyiddin Training in Massive MIMO Systems Wan Amirul Wan Mohd Mahyiddin A thesis submitted for the degree of Doctor of Philosophy in Electrical and Electronic Engineering University of Canterbury New Zealand 2015

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

E7220: Radio Resource and Spectrum Management. Lecture 4: MIMO

E7220: 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 information

Sum Rate Maximizing Zero Interference Linear Multiuser MIMO Transmission

Sum Rate Maximizing Zero Interference Linear Multiuser MIMO Transmission Sum Rate Maximizing Zero Interference Linear Multiuser MIMO Transmission Helka-Liina Määttänen Renesas Mobile Europe Ltd. Systems Research and Standardization Helsinki, Finland Email: helka.maattanen@renesasmobile.com

More information

Pilot Contamination Reduction Scheme in Massive MIMO Multi-cell TDD Systems

Pilot Contamination Reduction Scheme in Massive MIMO Multi-cell TDD Systems Journal of Computational Information Systems 0: 5 (04) 67 679 Available at http://www.jofcis.com Pilot Contamination Reduction Scheme in Massive MIMO Multi-cell TDD Systems Cuifang ZHANG, Guigen ZENG College

More information

Transmission Strategies for Wireless Multi-user, Multiple-Input, Multiple-Output Communication Channels

Transmission Strategies for Wireless Multi-user, Multiple-Input, Multiple-Output Communication Channels Brigham Young University BYU ScholarsArchive All Theses and Dissertations 2004-03-18 Transmission Strategies for Wireless Multi-user, Multiple-Input, Multiple-Output Communication Channels Quentin H. Spencer

More information

UL/DL Mode Selection and Transceiver Design for Dynamic TDD Systems

UL/DL Mode Selection and Transceiver Design for Dynamic TDD Systems UL/DL Mode Selection and Transceiver Design for Dynamic TDD Systems 1 UL/DL Mode Selection and Transceiver Design for Dynamic TDD Systems Antti Tölli with Ganesh Venkatraman, Jarkko Kaleva and David Gesbert

More information

Performance of wireless Communication Systems with imperfect CSI

Performance of wireless Communication Systems with imperfect CSI Pedagogy lecture Performance of wireless Communication Systems with imperfect CSI Yogesh Trivedi Associate Prof. Department of Electronics and Communication Engineering Institute of Technology Nirma University

More information

Beamforming with Finite Rate Feedback for LOS MIMO Downlink Channels

Beamforming 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 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

Potential Throughput Improvement of FD MIMO in Practical Systems

Potential Throughput Improvement of FD MIMO in Practical Systems 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

More information

IN RECENT years, wireless multiple-input multiple-output

IN RECENT years, wireless multiple-input multiple-output 1936 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 3, NO. 6, NOVEMBER 2004 On Strategies of Multiuser MIMO Transmit Signal Processing Ruly Lai-U Choi, Michel T. Ivrlač, Ross D. Murch, and Wolfgang

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

Interference Management in Wireless Networks

Interference Management in Wireless Networks Interference Management in Wireless Networks Aly El Gamal Department of Electrical and Computer Engineering Purdue University Venu Veeravalli Coordinated Science Lab Department of Electrical and Computer

More information

Use of Multiple-Antenna Technology in Modern Wireless Communication Systems

Use 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 information

DATA ALLOCATION WITH MULTI-CELL SC-FDMA FOR MIMO SYSTEMS

DATA ALLOCATION WITH MULTI-CELL SC-FDMA FOR MIMO SYSTEMS DATA ALLOCATION WITH MULTI-CELL SC-FDMA FOR MIMO SYSTEMS Rajeshwari.M 1, Rasiga.M 2, Vijayalakshmi.G 3 1 Student, Electronics and communication Engineering, Prince Shri Venkateshwara Padmavathy Engineering

More information

Uplink Receiver with V-BLAST and Practical Considerations for Massive MIMO System

Uplink Receiver with V-BLAST and Practical Considerations for Massive MIMO System Uplink Receiver with V-BLAST and Practical Considerations for Massive MIMO System Li Tian 1 1 Department of Electrical and Computer Engineering, University of Auckland, Auckland, New Zealand Abstract Abstract

More information

A 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 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 information

Bringing the Magic of Asymptotic Analysis to Wireless Networks

Bringing the Magic of Asymptotic Analysis to Wireless Networks Massive MIMO Bringing the Magic of Asymptotic Analysis to Wireless Networks Dr. Emil Björnson Department of Electrical Engineering (ISY) Linköping University, Linköping, Sweden International Workshop on

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

Low-Complexity Beam Allocation for Switched-Beam Based Multiuser Massive MIMO Systems

Low-Complexity Beam Allocation for Switched-Beam Based Multiuser Massive MIMO Systems Low-Complexity Beam Allocation for Switched-Beam Based Multiuser Massive MIMO Systems Jiangzhou Wang University of Kent 1 / 31 Best Wishes to Professor Fumiyuki Adachi, Father of Wideband CDMA [1]. [1]

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

Distributed Robust Sum Rate Maximization in Cooperative Cellular Networks

Distributed Robust Sum Rate Maximization in Cooperative Cellular Networks Distributed Robust Sum Rate Maximization in Cooperative Cellular Networks Richard Fritzsche, Gerhard P. Fettweis Technische Universität Dresden, Vodafone Chair Mobile Communications Systems, Dresden, Germany

More information

Performance Analysis of Multiuser MIMO Systems with Scheduling and Antenna Selection

Performance Analysis of Multiuser MIMO Systems with Scheduling and Antenna Selection Performance Analysis of Multiuser MIMO Systems with Scheduling and Antenna Selection Mohammad Torabi Wessam Ajib David Haccoun Dept. of Electrical Engineering Dept. of Computer Science Dept. of Electrical

More information

Pilot Reuse & Sum Rate Analysis of mmwave & UHF-based Massive MIMO Systems

Pilot Reuse & Sum Rate Analysis of mmwave & UHF-based Massive MIMO Systems Pilot Reuse & Sum Rate Analysis of mmwave & UHF-based Massive MIMO Systems Syed Ahsan Raza Naqvi, Syed Ali Hassan and Zaa ul Mul School of Electrical Engineering & Computer Science (SEECS National University

More information

Novel Detection Scheme for LSAS Multi User Scenario with LTE-A and MMB Channels

Novel 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 information

1 Opportunistic Communication: A System View

1 Opportunistic Communication: A System View 1 Opportunistic Communication: A System View Pramod Viswanath Department of Electrical and Computer Engineering University of Illinois, Urbana-Champaign The wireless medium is often called a fading channel:

More information

Hybrid Compression and Message-Sharing Strategy for the Downlink Cloud Radio-Access Network

Hybrid Compression and Message-Sharing Strategy for the Downlink Cloud Radio-Access Network Hybrid Compression and Message-Sharing Strategy for the Downlink Cloud Radio-Access Network Pratik Patil and Wei Yu Department of Electrical and Computer Engineering University of Toronto, Toronto, Ontario

More information

3G Evolution. Outline. Chapter: Multi-antenna configurations. Introduction. Introduction. Multi-antenna techniques. Multiple receiver antennas, SIMO

3G Evolution. Outline. Chapter: Multi-antenna configurations. Introduction. Introduction. Multi-antenna techniques. Multiple receiver antennas, SIMO Chapter: 3G Evolution 6 Outline Introduction Multi-antenna configurations Multi-antenna t techniques Vanja Plicanic vanja.plicanic@eit.lth.se lth Multi-antenna techniques Multiple transmitter antennas,

More information

WITH the advancements in antenna technology and

WITH the advancements in antenna technology and On the Use of Channel Models and Channel Estimation Techniques for Massive MIMO Systems Martin Kuerbis, Naveen Mysore Balasubramanya, Lutz Lampe and Alexander Lampe Hochschule Mittweida - University of

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

UPLINK SPATIAL SCHEDULING WITH ADAPTIVE TRANSMIT BEAMFORMING IN MULTIUSER MIMO SYSTEMS

UPLINK SPATIAL SCHEDULING WITH ADAPTIVE TRANSMIT BEAMFORMING IN MULTIUSER MIMO SYSTEMS UPLINK SPATIAL SCHEDULING WITH ADAPTIVE TRANSMIT BEAMFORMING IN MULTIUSER MIMO SYSTEMS Yoshitaka Hara Loïc Brunel Kazuyoshi Oshima Mitsubishi Electric Information Technology Centre Europe B.V. (ITE), France

More information

Optimal user pairing for multiuser MIMO

Optimal user pairing for multiuser MIMO Optimal user pairing for multiuser MIMO Emanuele Viterbo D.E.I.S. Università della Calabria Arcavacata di Rende, Italy Email: viterbo@deis.unical.it Ari Hottinen Nokia Research Center Helsinki, Finland

More information

Antennas Multiple antenna systems

Antennas 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 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

Research Article Intercell Interference Coordination through Limited Feedback

Research 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 information

MATLAB COMMUNICATION TITLES

MATLAB COMMUNICATION TITLES MATLAB COMMUNICATION TITLES -2018 ORTHOGONAL FREQUENCY-DIVISION MULTIPLEXING(OFDM) 1 ITCM01 New PTS Schemes For PAPR Reduction Of OFDM Signals Without Side Information 2 ITCM02 Design Space-Time Trellis

More information

5G: Opportunities and Challenges Kate C.-J. Lin Academia Sinica

5G: Opportunities and Challenges Kate C.-J. Lin Academia Sinica 5G: Opportunities and Challenges Kate C.-J. Lin Academia Sinica! 2015.05.29 Key Trend (2013-2025) Exponential traffic growth! Wireless traffic dominated by video multimedia! Expectation of ubiquitous broadband

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

A Brief Review of Opportunistic Beamforming

A Brief Review of Opportunistic Beamforming A Brief Review of Opportunistic Beamforming Hani Mehrpouyan Department of Electrical and Computer Engineering Queen's University, Kingston, Ontario, K7L3N6, Canada Emails: 5hm@qlink.queensu.ca 1 Abstract

More information

University of Bristol - Explore Bristol Research. Peer reviewed version. Link to published version (if available): /PIMRC.2009.

University 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 information

LTE-Advanced and Release 10

LTE-Advanced and Release 10 LTE-Advanced and Release 10 1. Carrier Aggregation 2. Enhanced Downlink MIMO 3. Enhanced Uplink MIMO 4. Relays 5. Release 11 and Beyond Release 10 enhances the capabilities of LTE, to make the technology

More information

Downlink Performance of Cell Edge User Using Cooperation Scheme in Wireless Cellular Network

Downlink Performance of Cell Edge User Using Cooperation Scheme in Wireless Cellular Network Quest Journals Journal of Software Engineering and Simulation Volume1 ~ Issue1 (2013) pp: 07-12 ISSN(Online) :2321-3795 ISSN (Print):2321-3809 www.questjournals.org Research Paper Downlink Performance

More information