Bargaining and the MISO interference channel

Authors:
Matthew Nokleby;A. Lee Swindlehurst
Affiliations:
Department of Electrical and Computer Engineering, Rice University, Houston, TX;Department of Electrical Engineering and Computer Science, University of California at Irvine, Irvine, CA
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on game theory in signal processing and communications
Year:
2009

Citing 10
Cited 0

Data networks

Data networks
Convex Optimization

Convex Optimization
Fundamentals of WiMAX: Understanding Broadband Wireless Networking (Prentice Hall Communications Engineering and Emerging Technologies Series)

Fundamentals of WiMAX: Understanding Broadband Wireless Networking (Prentice Hall Communications Engineering and Emerging Technologies Series)
Complete Characterization of the Pareto Boundary for the MISO Interference Channel

IEEE Transactions on Signal Processing - Part II
Optimized signaling for MIMO interference systems with feedback

IEEE Transactions on Signal Processing
IEEE 802.11n: enhancements for higher throughput in wireless LANs

IEEE Wireless Communications
Equilibrium Efficiency Improvement in MIMO Interference Systems: A Decentralized Stream Control Approach

IEEE Transactions on Wireless Communications
Weighted max-min fair beamforming, power control, and scheduling for a MISO downlink

IEEE Transactions on Wireless Communications
Optimal Power Schedule for Distributed MIMO Links

IEEE Transactions on Wireless Communications
Competition Versus Cooperation on the MISO Interference Channel

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We examine the MISO interference channel under cooperative bargaining theory. Bargaining approaches such as the Nash and Kalai-Smorodinsky solutions have previously been used in wireless networks to strike a balance between max-sum efficiency and max-min equity in users' rates. However, cooperative bargaining for the MISO interference channel has only been studied extensively for the two-user case. We present an algorithm that finds the optimal Kalai-Smorodinsky beamformers for an arbitrary number of users. We also consider joint scheduling and beamformer selection, using gradient ascent to find a stationary point of the Kalai-Smorodinsky objective function. When interference is strong, the flexibility allowed by scheduling compensates for the performance loss due to local optimization. Finally, we explore the benefits of power control, showing that power control provides nontrivial throughput gains when the number of transmitter/receiver pairs is greater than the number of transmit antennas.