混合模式下认知无线电网络的能效优化算法

认知无线电网络中，协作频谱感知利用多个节点同时感知可提高频谱感知检测性能。然而随着感知的次用户(SU)个数增加，导致能耗增高、能效(EE)降低。为解决这一问题，本文结合机会频谱接入和衬垫式频谱共享2种共享模式，构造基于混合频谱共享模式的能效模型，同时考虑3种不同的融合规则、主用户(PU)的再占据概率和报告信道误差，以最大化SU系统的EE为目标，使用拉格朗日乘子法与次梯度下降算法对感知时间、参与感知个数、次用户发射功率进行迭代优化求解。仿真结果表明，在最低服务质量要求(QoS)和发射功率的约束下，该能效优化算法能够实现更高的吞吐量和更高的能量效率。

Energy-Efficient Resource Allocation for Hybrid Spectrum Sharing Cognitive Radio Networks

In cognitive radio networks, cooperative spectrum sensing can improve spectrum sensing detection performance by using multiple nodes sensing simultaneously. However, with the increase of the number of secondary users (SU), energy consumption increases and energy efficiency (EE) decreases. In order to solve this problem, an energy efficiency model based on hybrid spectrum sharing mode is constructed by combining two sharing modes of opportunistic spectrum access and underlay spectrum sharing. Meanwhile, three different fusion rules, reoccupation probability of primary user (PU) and reporting channel error are considered. Aiming at maximizing EE of the secondary system, Lagrange multiplier method and sub gradient descent algorithm are used for sensing time, the number of participants and the transmission power of users to be solved by iterative optimization. The simulation results show that the algorithm can achieve higher throughput and energy efficiency under the constraints of the minimum quality of service (QoS) and transmission power.