认知中继网络中的功率分配算法

无人机（Unmanned aerial vehicle, UAV）通信是当前无线通信领域的研究热点。为了保证地面移动端与UAV通信的可靠性，提出了基于空时块码（Space-time block code, STBC）的协作中继传输方案。为了提升频谱效率，本文利用认知无线电技术，于协作中继处分别采用放大转发(Amplify-and-forward, AF)和解码转发(Decode-and-forward, DF)两种协议进行传输，在主用户通信服务质量得到保证和认知用户传输功率受限的条件下，建立以认知中继网络的吞吐量最大化为目标函数的优化问题。采取拉格朗日乘子法与Karush-Kuhn-Tucker (KKT)条件相结合的方案来实现优化问题中最优功率的分配。最后，仿真结果不仅验证了所提方案的有效性，还表明了DF中继协议下认知中继网络的吞吐量优于AF中继协议。

Power Allocation Algorithm in Cognitive Relay Networks

Unmanned aerial vehicle (UAV) communication is a research hotspot in the field of wireless communication. In order to ensure the reliability of communication between ground mobile terminal and UAV, a cooperative relay transmission scheme based on space-time coding (STBC) is proposed in this paper. Then, by using cognitive radio technology to improve the spectral efficiency, we formulate an optimization problem to maximize the throughput of cognitive relay network under the constraints of the quality-of-service (QoS) requirement of the primary users and the transmission power limitation of cognitive users with amplify-and-forward (AF) and decode-and-forward (DF) protocols. The Lagrange multiplier function and Karush-Kuhn-Tucker (KKT) condition are combined to solve the optimal power allocation. Finally, the simulation results not only verify the effectiveness of the proposed scheme, but also show that the throughput of cognitive relay network with DF protocol is better than that of AF protocol.