TY - JOUR

T1 - Comparison of distributed beamforming algorithms for MIMO interference networks

AU - Schmidt, David A.

AU - Shi, Changxin

AU - Berry, Randall A.

AU - Honig, Michael L.

AU - Utschick, Wolfgang

PY - 2013

Y1 - 2013

N2 - This paper presents a comparative study of algorithms for jointly optimizing beamformers and receive filters in an interference network, where each node may have multiple antennas, each user transmits at most one data stream, and interference is treated as noise. We focus on techniques that seek good suboptimal solutions by means of iterative and distributed updates. Those include forward-backward iterative algorithms (max-signal-to-interference-plus- noise ratio (SINR) and interference leakage), weighted sum mean-squared error (MSE) algorithms, and interference pricing with incremental signal-to-noise ratio (SNR) adjustments. We compare their properties in terms of convergence and information exchange requirements, and then numerically evaluate their sum rate performance averaged over random (stationary) channel realizations. The numerical results show that the max-SINR algorithm achieves the maximum degrees of freedom (i.e., supports the maximum number of users with near-zero interference) and exhibits better convergence behavior at high SNRs than the weighted sum MSE algorithms. However, it assumes fixed power per user and achieves only a single point in the rate region whereas the weighted sum MSE criterion gives different points. In contrast, the incremental SNR algorithm adjusts the beam powers and deactivates users when interference alignment is infeasible. Furthermore, that algorithm can provide a slight increase in sum rate, relative to max-SINR, at the cost of additional iterations.

AB - This paper presents a comparative study of algorithms for jointly optimizing beamformers and receive filters in an interference network, where each node may have multiple antennas, each user transmits at most one data stream, and interference is treated as noise. We focus on techniques that seek good suboptimal solutions by means of iterative and distributed updates. Those include forward-backward iterative algorithms (max-signal-to-interference-plus- noise ratio (SINR) and interference leakage), weighted sum mean-squared error (MSE) algorithms, and interference pricing with incremental signal-to-noise ratio (SNR) adjustments. We compare their properties in terms of convergence and information exchange requirements, and then numerically evaluate their sum rate performance averaged over random (stationary) channel realizations. The numerical results show that the max-SINR algorithm achieves the maximum degrees of freedom (i.e., supports the maximum number of users with near-zero interference) and exhibits better convergence behavior at high SNRs than the weighted sum MSE algorithms. However, it assumes fixed power per user and achieves only a single point in the rate region whereas the weighted sum MSE criterion gives different points. In contrast, the incremental SNR algorithm adjusts the beam powers and deactivates users when interference alignment is infeasible. Furthermore, that algorithm can provide a slight increase in sum rate, relative to max-SINR, at the cost of additional iterations.

KW - MIMO interference channel

KW - beamforming

KW - interference alignment

KW - precoder optimization

KW - sum-rate maximization

UR - http://www.scopus.com/inward/record.url?scp=84879232611&partnerID=8YFLogxK

U2 - 10.1109/TSP.2013.2257761

DO - 10.1109/TSP.2013.2257761

M3 - Article

AN - SCOPUS:84879232611

SN - 1053-587X

VL - 61

SP - 3476

EP - 3489

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 13

M1 - 6497666

ER -