Convex Optimization
Opportunistic spectrum sharing in cognitive MIMO wireless networks
IEEE Transactions on Wireless Communications
Robust cognitive beamforming with partial channel state information
IEEE Transactions on Wireless Communications
Robust cognitive beamforming with bounded channel uncertainties
IEEE Transactions on Signal Processing
MIMO cognitive radio: a game theoretical approach
IEEE Transactions on Signal Processing
Robust MIMO Cognitive Radio Via Game Theory
IEEE Transactions on Signal Processing
Joint power control and beamforming for cognitive radio networks
IEEE Transactions on Wireless Communications
Competitive spectrum sharing in cognitive radio networks: a dynamic game approach
IEEE Transactions on Wireless Communications
Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks
IEEE Journal on Selected Areas in Communications
On the Design of Linear Transceivers for Multiuser Systems with Channel Uncertainty
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Signal Processing
Robust Distributed Power Control in Cognitive Radio Networks
IEEE Transactions on Mobile Computing
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The problem of joint beamforming and power allocation for cognitive multi-input multi-output systems is studied via game theory. The objective is to maximize the sum utility of secondary users (SUs) subject to the primary user (PU) interference constraint, the transmission power constraint of SUs, and the signal-to-interference-plus-noise ratio (SINR) constraint of each SU. In our earlier work, the problem was formulated as a non-cooperative game under the assumption of perfect channel state information (CSI). Nash equilibrium (NE) is considered as the solution of this game. A distributed algorithm is proposed which can converge to the NE. Due to the limited cooperation between the secondary base station (SBS) and the PU, imperfect CSI between the SBS and the PU is further considered in this work. The problem is formulated as a robust game. As it is difficult to solve the optimization problem in this case, existence of the NE cannot be analyzed. Therefore, convergence property of the sum utility of SUs will be illustrated numerically. Simulation results show that under perfect CSI the proposed algorithm can converge to a locally optimal pair of transmission power vector and beamforming vector, while under imperfect CSI the sum utility of SUs converges with the increase of the transmission power constraint of SUs.