Abstract
Multi-input multioutput (MIMO) technique provides a promising solution to enhance the performance of wireless communication systems. In this paper, we consider antenna correlation at the transmitter in practical cognitive MIMO systems. What is more, a game-theoretic framework is conducted to analyze the optimum beamforming and power allocation such that each user maximizes its own rate selfishly under the transmitting power constraint and the primary user (PU) interference constraint. The design of the cognitive MIMO system is formulated as a noncooperative game, where the secondary users (SUs) compete with each other over the resources made available by the PUs. Interestingly, as the correlation parameter grows, the utility degrades. Nash equilibrium is considered as the solution of this game. Simulation results show that the proposed algorithm can converge quickly and clearly outperforms the strategy without game.
Highlights
The explosive expansion in wireless communications over the past several years has given rise to severe technical challenges which include the demand of transmitting multimedia date at high rates in an environment rich in scattering
The secondary base station (SBS) is equipped with four antennas, and primary user (PU) and each secondary users (SUs) are equipped with only one antenna
The background noise power at each user is set to σ2 = 0.01 W, the period of block fading is 1 s, the maximum transmission power of SBS is set to pT = 10 W, the PU’s transmitting power is set to pp = 1 W, the iterative threshold is set to ε = 10−3, the initial transmission power is p(1) = (0.1, 0.1, 0.1) W, the beamforming vector is fk, that is, β = 0, iterative step is β = 0 : 0.01 : 1, and the number of power iterations is 20
Summary
The explosive expansion in wireless communications over the past several years has given rise to severe technical challenges which include the demand of transmitting multimedia date at high rates in an environment rich in scattering. In [5], power allocation and beamforming techniques were introduced to CR systems, where SUs cooperated with PUs under different constraints It proposed a new iterative algorithm and found an optimum solution by the principle of duality transformation model . In [18], with consideration of antenna correlation, a power allocation game for uplink MIMO access channels was provided to maximize the mutual information rate under power constraint. Inspired by the above preceding works, with consideration of antenna correlation, this paper addresses the joint optimization of beamforming and power allocation in cognitive MIMO systems.
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