认知自组网分布式频谱感知最优协作算法

The optimal spectrum sensing algorithm in distributed cognitive radio Ad-hoc networks

认知无线电技术使得自组织网络节点能够充分利用空闲频谱资源,提高了传输性能。通过协作频谱感知,可有效解决由于无线信道存在阴影、噪声和衰落等情况导致的单节点感知准确性偏低。为了解决梯度算法随着协作节点数量增大后计算复杂度变高,文中提出部分梯度算法ψ-GBCS,该模型通过基于SNR的动态阈值保证了感知准确性,同时通过最佳协作节点数提高了感知效率。仿真结果表明,该模型下,综合评估系统效率和性能的J函数值提高37%,能耗降低50%,有效保证大规模认知自组网频谱感知的鲁棒性,降低了对主用户的干扰及设备功耗。

In Ad-hoc networks,the unused spectrum resources can be effectively sensed by cognitive radio technology, and transmission performance will be improved. Cooperative spectrum sensing is necessary because a single node cannot detect the existence accurately due to shadowing,noise and fading in wireless channels. Gradient based cooperative sensing（ GBCS） is a widely used scheme in distributed cooperative spectrum sensing. However,as the nodes of cognitive radio Ad-hoc networks increasing,the efficiency of GBCS degrades because the complexity rises. To solve the problem,a partial-GBCS（ ψ-GBCS） scheme is proposed in this paper. The roc performance and energy consumption are balanced by SNR-based adaptive threshold and optimal cooperative numbers. The simulation results show that under the proposed scheme,the value of object function,which evaluates the performance and energy efficiency of the system,is larged improved by 37%,and the robustness of cooperative spectrum sensing is guaranteed in large scale cognitive Ad-hoc networks. Meanwhile,the interference to primary users is suppressed and equipment power consumption is reduced.