Decentralized Waveform Design for MIMO Cognitive Radio under Interference Temperature Constraint

This paper addresses the information rate maximization problem under interference temperature constraint for a multiple-in multiple-out cognitive radio (MIMO-CR) network in which each CR user is equipped with multiple antennas. We formulate this as a Nash equilibrium problem where each CR tries to maximize its own utility by choosing appropriate wave-form. A decentralized iterative water-filling algorithm (IWFA) with pricing is proposed to solve it, the pricing mechanism is used to satisfy the interference temperature constraint while achieving the Nash equilibrium. Simulation results show that our algorithm can satisfy the interference temperature constraint perfectly and is fast convergent, we also show that the proposed IWFA with pricing can achieve a performance improvement in terms of sum-rate over the existed IWFA with power shaping.