Crystallized Rate Regions for MIMO Transmission
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Research Article Crystallized Rate Regions for MIMO Transmission Adrian Kliks (EURASIP Member),1 Pawel Sroka (EURASIP Member),1 and Merouane Debbah2 1 Poznan
University of Technology, Chair of Wireless Communications, Polanka 3, 60-965 Poznan, Poland Alcatel-Lucent Chair on Flexible Radio, 3 rue Joliot-Curie, 91192 Gif-sur-Yvette, France
2 SUPELEC,
Correspondence should be addressed to Pawel Sroka, [email protected] Received 1 February 2010; Revised 2 July 2010; Accepted 8 July 2010 Academic Editor: Osvaldo Simeone Copyright © 2010 Adrian Kliks et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. When considering the multiuser SISO interference channel, the allowable rate region is not convex and the maximization of the aggregated rate of all the users by the means of transmission power control becomes inefficient. Hence, a concept of the crystallized rate regions has been proposed, where the time-sharing approach is considered to maximize the sumrate.In this paper, we extend the concept of crystallized rate regions from the simple SISO interference channel case to the MIMO/OFDM interference channel. As a first step, we extend the time-sharing convex hull from the SISO to the MIMO channel case. We provide a non-cooperative game-theoretical approach to study the achievable rate regions, and consider the Vickrey-Clarke-Groves (VCG) mechanism design with a novel cost function. Within this analysis, we also investigate the case of OFDM channels, which can be treated as the special case of MIMO channels when the channel transfer matrices are diagonal. In the second step, we adopt the concept of correlated equilibrium into the case of two-user MIMO/OFDM, and we introduce a regret-matching learning algorithm for the system to converge to the equilibrium state. Moreover, we formulate the linear programming problem to find the aggregated rate of all users and solve it using the Simplex method. Finally, numerical results are provided to confirm our theoretical claims and show the improvement provided by this approach.
1. Introduction The future wireless systems are characterized by decreasing range of the transmitters as higher transmit frequencies are to be utilized. The decreasing cell sizes combined with the increasing number of users within a cell greatly increases the impact of interference on the overall system performance. Hence, mitigation of the interference between transmitreceive pairs is of great importance in order to improve the achievable data rates. The Multiple Input Multiple Output (MIMO) technology has become an enabler for further increase in system throughput. Moreover, the utilization of spatial diversity thanks to MIMO technology opens new possibilities of interference mitigation [1–3]. Several concepts of interference mitigation have been proposed, such as the successive interference cancellation or the treatment of interferenc
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