Comparison of Distributed Beamforming Algorithms for MIMO Interference Networks

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 goo...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on signal processing Jg. 61; H. 13; S. 3476 - 3489
Hauptverfasser: Schmidt, David A., Shi, Changxin, Berry, Randall A., Honig, Michael L., Utschick, Wolfgang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.07.2013
Institute of Electrical and Electronics Engineers
Schlagworte:
Applied sciences
Array signal processing
beamforming
Classification algorithms
Convergence
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Information, signal and communications theory
Interference
interference alignment
Iterative methods
MIMO interference channel
Power control
precoder optimization
Pricing
Resource management
Signal and communications theory
Signal processing algorithms
Signal to noise ratio
Signal, noise
sum-rate maximization
Telecommunications and information theory
Performance evaluation
MIMO system
precoder optimization
Coding circuit
Updating
Iterative method
MIMO interference channel
Beam forming
Nodes
Optimization
Mean square error
sum-rate maximization
interference alignment
Distributed algorithm
Pricing
Signal processing
Numerical simulation
Tariffication
Signal to interference plus noise ratio
beamforming
Antenna array
Information exchange
Comparative study
Signal to noise ratio
ISSN:1053-587X, 1941-0476
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2013.2257761