Space-time water-filling for composite MIMO fading channels
EURASIP Journal on Wireless Communications and Networking
Introduction to Space-Time Wireless Communications
Introduction to Space-Time Wireless Communications
Capacity bounds for the Gaussian interference channel
IEEE Transactions on Information Theory
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Downlink capacity of interference-limited MIMO systems with joint detection
IEEE Transactions on Wireless Communications
MIMO transmission over a time-varying channel using SVD
IEEE Transactions on Wireless Communications
On the capacity of MIMO broadcast channels with partial side information
IEEE Transactions on Information Theory
MIMO capacity with interference
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
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.