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中文摘要：
该文研究在主用户干扰温度约束下多个竞争的MIMO认知无线电最大化自身信息速率的波形自适应问题。该文使用非协作博弈论将其表述为Nash均衡问题，给出了Nash均衡解存在唯一的条件，并提出了一种求解Nash均衡的带惩罚价格的分布式迭代注水算法，通过对干扰进行价格惩罚使得MIMO认知无线电在达到Nash均衡时满足干扰温度约束。仿真结果表明相对于不考虑干扰温度约束的经典迭代注水算法，该文算法能够满足干扰温度约束，适用于认知无线电网络。
英文摘要：
This paper addresses the waveform adaptation issue of multiple competitive Multiple-In Multiple-Out Cognitive Radios （MIMO-CR） respectively maximizing their information rates under the interference-temperature constraint of primary users. This issue is formulated as a Nash equilibrium issue from a non-cooperative game theoretic viewpoint, conditions for the existence and uniqueness of the Nash equilibrium are provided and a decentralized Iterative Water-Filling Algorithm （IWFA） with a punishing price is proposed to solve the above Nash equilibrium issue, the punishing price is imposed on the interference generated by MIMO-CRs in order to make the interference-temperature constraint satisfied while MIMO-CRs achieve the Nash equilibrium. Simulation results show, when compared to the classical IWFA which does not consider the interference-temperature constraint, the proposed algorithm satisfies the interference-temperature constraint and hence is applicable to cognitive radio networks
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