Distributed interference pricing for the MIMO interference channel

Authors:
Changxin Shi;David A. Schmidt;Randall A. Berry;Michael L. Honig;Wolfgang Utschick
Affiliations:
Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois;Technische Universität München, Munich, Germany;Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois;Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois;Technische Universität München, Munich, Germany
Venue:
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Year:
2009

Citing 5
Cited 2

Optimized signaling for MIMO interference systems with feedback

IEEE Transactions on Signal Processing
Balancing transport and physical Layers in wireless multihop networks: jointly optimal congestion control and power control

IEEE Journal on Selected Areas in Communications
Distributed interference compensation for wireless networks

IEEE Journal on Selected Areas in Communications
Competition Versus Cooperation on the MISO Interference Channel

IEEE Journal on Selected Areas in Communications
Competitive Design of Multiuser MIMO Systems Based on Game Theory: A Unified View

IEEE Journal on Selected Areas in Communications

Minimum mean squared error interference alignment

Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Local interference pricing for distributed beamforming in MIMO networks

MILCOM'09 Proceedings of the 28th IEEE conference on Military communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We study distributed algorithms for updating transmit precoding matrices for a two-user Multi-Input/Multi-Output (MIMO) interference channel. Our objective is to maximize the sum rate with linear Minimum Mean Squared Error (MMSE) receivers, treating the interference as additive Gaussian noise. An iterative approach is considered in which given a set of precoding matrices and powers, each receiver announces an interference price (marginal decrease in rate due to an increase in interference) for each received beam, corresponding to a column of the precoding matrix. Given the interference prices from the neighboring receiver, and also knowledge of the appropriate cross-channel matrices, the transmitter can then update the beams and powers to maximize the rate minus the interference cost. Variations on this approach are presented in which beams are added sequentially (and then fixed), and in which all beams and associated powers are adjusted at each iteration. Numerical results are presented, which compare these algorithms with iterative water-filling (which requires no information exchange), and a centralized optimization algorithm, which finds locally optimal solutions. Our results show that the distributed algorithms perform close to the centralized algorithm, and by adapting the rank of the precoder matrices, achieve the optimal high-SNR slope.