Equilibrium efficiency improvement via pricing in MIMO interference channels

A Nash equilibrium exists in the MIMO interference channel where users maximize their rates in a fully competitive manner. In this paper, we propose a distributed algorithm to improve the equilibrium efficiency. In the algorithm, some kind of coordination is introduced by exchanging interference prices among users, where the price function is derived from the KKT conditions of the global sum rate maximization problem. Taking the concept of price into consideration, each user will suppress interference to others while maximizing his own rate, thus enhancing the sum rate performance. Numerical results show that the proposed algorithm can achieve higher sum rate than the equilibrium which is obtained by iterative waterfilling.

[1]  Rick S. Blum,et al.  Optimized signaling for MIMO interference systems with feedback , 2003, IEEE Trans. Signal Process..

[2]  Sergio Barbarossa,et al.  Optimal Linear Precoding Strategies for Wideband Noncooperative Systems Based on Game Theory—Part I: Nash Equilibria , 2007, IEEE Transactions on Signal Processing.

[3]  R.A. Berry,et al.  Distributed interference pricing with MISO channels , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[4]  Sergio Barbarossa,et al.  Optimal Linear Precoding Strategies for Wideband Non-Cooperative Systems Based on Game Theory—Part II: Algorithms , 2007, IEEE Transactions on Signal Processing.

[5]  Sergio Barbarossa,et al.  IEEE TRANSACTIONS ON SIGNAL PROCESSING (ACCEPTED) 1 The MIMO Iterative Waterfilling Algorithm , 2022 .

[6]  Wolfgang Utschick,et al.  Distributed Interference Pricing for the MIMO Interference Channel , 2009, 2009 IEEE International Conference on Communications.

[7]  Robert W. Heath,et al.  Shifting the MIMO Paradigm , 2007, IEEE Signal Processing Magazine.

[8]  Mary Ann Ingram,et al.  Power-controlled capacity for interfering MIMO links , 2001, VTC Fall.

[9]  Yang Song,et al.  Equilibrium Efficiency Improvement in MIMO Interference Systems: A Decentralized Stream Control Approach , 2007, IEEE Transactions on Wireless Communications.

[10]  Sergio Barbarossa,et al.  Competitive Design of Multiuser MIMO Systems Based on Game Theory: A Unified View , 2008, IEEE Journal on Selected Areas in Communications.

[11]  Michael L. Honig,et al.  Distributed interference compensation for wireless networks , 2006, IEEE Journal on Selected Areas in Communications.

[12]  T. Sälzer,et al.  From Single User to Multiuser Communications : Shifting the MIMO Paradigm , 2007 .