认知无线电中继协助网络资源分层优化算法

Hierarchical optimization algorithm for resource allocation in relay-assisted cognitive radio network

在认知多用户中继网络系统场景下,针对时变信道完全DF中继两跳传输的差异性问题,采用纳什议价公平性准则效用函数,提出了联合用户传输模式选择、子载波配对、信道分配和功率分配的分层优化模型。利用拉格朗日对偶理论实现模型分层求解,在降低算法复杂度的同时提升了系统效用及吞吐量。仿真结果表明:相对于完全中继、直传网络和非载波配对的中继网络,所提算法在兼顾用户速率需求和公平性的同时可使系统吞吐量获得较大提高。

In order to diversity problem of two-hop complete Decode-and-Forward （DF） relay transmission in time-varying channels, a hierarchical optimization model is proposed. This model is based on Nash bargaining fairness criterion, and it combines transmission mode selection, subcarrier pairing, channel assignment and power allocation. Taking the advantage of Lagrange Duality, the proposed model reduces the algorithm complexity, improves the system utility and enhances the overall throughput. Simulation results show that, compared with complete relay transmission models, direct transmission models and non-carrier-pairing transmission models, the proposed algorithm achieves great improvement in system throughput and ensures the users＇ rate requirement and fairness at the same time.