Comparison of Distributed Beamforming Algorithms for MIMO Interference Networks

Schmidt, David A.; Shi, Changgui; Berry, Randall A.; Honig, Michael L.; Utschick, Wolfgang

doi:10.1109/tsp.2013.2257761

Cited by 68 publications

(84 citation statements)

References 37 publications

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“…Similar to the observation in Fig. 2, the slower convergence of the WMMSE-based algorithm at high SINR [30] is another reason for this performance gap. It is to be noted that the ILA, DPC-WMMSE, WMMSE algorithms, and the competitive design may require several iterations to fully converge.…”

Section: Theorem 4 the Multicell Mimo-bc Wsr Maximization Problem (2supporting

confidence: 79%

“…This appears to be due to the much slower convergence rate of the DPC-WMMSE algorithm at high SINR region. As noted in a recent work [30], at high SINR, the WMMSEbased solution approach does not sufficiently suppress small interference sources. Thus, this approach only provides small sum-rate improvement after each iteration, compared to the ILA approach.…”

Section: Theorem 4 the Multicell Mimo-bc Wsr Maximization Problem (2mentioning

confidence: 86%

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Sum-rate maximization in the multicell MIMO broadcast channel with interference coordination

Nguyen

Le‐Ngoc

2013

2013 IEEE International Conference on Communications (ICC)

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Abstract-This paper studies the precoding designs to maximize the weighted sum-rate (WSR) in a multicell multiple-input multiple-output (MIMO) broadcast channel (BC). We consider a multicell network under universal frequency reuse with multiple mobile stations (MS) per cell. With interference coordination (IC) between the multiple cells, the base-station (BS) at each cell only transmits information signals to the MSs within its cell using the dirty paper coding (DPC) technique, while coordinating the inter-cell interference (ICI) induced to other cells. The main focus of this work is to jointly optimize the encoding covariance matrices across the BSs in order to maximize the network-wide WSR. Since this optimization problem is shown to be nonconvex, obtaining its globally optimal solution is highly complicated. To address this problem, we consider two low-complexity solution approaches with distributed implementation to obtain at least locally optimal solutions. In the first approach, by applying a successive convex approximation technique, the original nonconvex problem is decomposed into a sequence of simpler problems, which can be solved optimally and separately at each BS. In the second approach, the WSR problem is solved via an equivalent problem of weighted sum mean squared error minimization. Both solution approaches will unfold the control signaling among the coordinated BSs to allow their distributed implementation. Simulation results confirm the convergence of the proposed algorithms, as well as their superior performances over schemes with linear precoding or no interference coordination among the BSs.

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Section: Theorem 4 the Multicell Mimo-bc Wsr Maximization Problem (2supporting

confidence: 79%

Section: Theorem 4 the Multicell Mimo-bc Wsr Maximization Problem (2mentioning

confidence: 86%

Sum-rate maximization in the multicell MIMO broadcast channel with interference coordination

Nguyen

Le‐Ngoc

2013

2013 IEEE International Conference on Communications (ICC)

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“…For the multicell MIMO downlink without hardware-impaired transceivers, several iterative and distributed methods for coordinated beamforming exist [3,13,14]. In particular, [13] stands out as a constructive way of finding a local optimum to the non-convex weighted sum rate optimization problem, by reformulating the problem as a weighted minimum mean squared error (MMSE) problem.…”

Section: Previous Work and Contributionsmentioning

confidence: 99%

“…As a baseline, we apply the popular MaxSINR method [3]. This ad-hoc method iteratively maximizes the SINRs of all the data streams in the network, and although it has not been proven to converge, it often performs excellently in numerical studies without impairments [4,5,14]. We modify the method slightly, to account for hardware impairments.…”

Section: Performance Evaluationmentioning

confidence: 99%

Weighted sum rate optimization for multicell MIMO systems with hardware-impaired transceivers

Brandt

Björnson

Bengtsson

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Physical transceivers exhibit distortions from hardware impairments, of which traces remain even after compensation and calibration. Multicell MIMO coordinated beamforming methods that ignore these residual impairments may suffer from severely degraded performance. In this work, we consider a general model for the aggregate effect of the residual hardware impairments, and propose an iterative algorithm for finding locally optimal points to a weighted sum rate optimization problem. The importance of accounting for the residual hardware impairments is verified by numerical simulation, and a substantial gain over traditional time-division multiple access with impairments-aware resource allocation is observed.Index Terms-Weighted sum rate optimization, transceiver hardware impairments, interference alignment.

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“…E-mails: ghauch@kth.se, mats.bengtsson@ee.kth.se, skoglund@kth.se T. Kim is with Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong. E-mail: taejokim@cityu.edu.hk such metrics mainly include (weighted) interference leakage [3], [4], (weighted) mean-squared error [5], [6], signal to interference plus noise ratio [3], [7], and (weighted) sum-rate [8], [6] [9] (an insightful and comprehensive comparison of such schemes was done in [10]). Note that other approaches such as [11] tackled the problem using a "CoMP-like" setup where both CSI and data are assumed to be known globally.…”

Section: Introductionmentioning

confidence: 99%

Distributed Low-Overhead Schemes for Multi-Stream MIMO Interference Channels

Ghauch

Kim

Bengtsson

et al. 2015

IEEE Trans. Signal Process.

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Abstract-Our aim in this work is to propose fully distributed schemes for transmit and receive filter optimization. The novelty of the proposed schemes is that they only require a few forwardbackward iterations, thus causing minimal communication overhead. For that purpose, we relax the well-known leakage minimization problem, and then propose two different filter update structures to solve the resulting non-convex problem: though one leads to conventional full-rank filters, the other results in rankdeficient filters, that we exploit to gradually reduce the transmit and receive filter rank, and greatly speed up the convergence. Furthermore, inspired from the decoding of turbo codes, we propose a turbo-like structure to the algorithms, where a separate inner optimization loop is run at each receiver (in addition to the main forward-backward iteration). In that sense, the introduction of this turbo-like structure converts the communication overhead required by conventional methods to computational overhead at each receiver (a cheap resource), allowing us to achieve the desired performance, under a minimal overhead constraint. Finally, we show through comprehensive simulations that both proposed schemes hugely outperform the relevant benchmarks, especially for large system dimensions.

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Comparison of Distributed Beamforming Algorithms for MIMO Interference Networks

Cited by 68 publications

References 37 publications

Sum-rate maximization in the multicell MIMO broadcast channel with interference coordination

Sum-rate maximization in the multicell MIMO broadcast channel with interference coordination

Weighted sum rate optimization for multicell MIMO systems with hardware-impaired transceivers

Distributed Low-Overhead Schemes for Multi-Stream MIMO Interference Channels

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