高能效认知自适应两阶段协作频谱感知算法

Energy-efficient cognitive adaptive two-phase sensing for cooperative spectrum sensing algorithm

针对协作频谱感知技术存在产生多余的传输开销和次用户能量消耗的不足,提出了结合双阈值能量检测模型和两步检测模型的自适应协作频谱感知(CATS)算法,在保证检测性能的前提下降低能量消耗.首先给出了CATS的数学模型,然后对CATS的检测概率和能量消耗进行了理论分析.CATS在第一阶段的检测结果不准确的情况下,才执行第二阶段的感知,并根据第一阶段的检测结果以及网络状况自适应调整两个阶段参与协作的次用户数目.仿真结果表明:与现有的两步检测协作模型相比,CATS算法在保证系统检测精度的同时能够有效减少系统的能量消耗.

Cooperative spectrum sensing（CSS）will lead to transmission overhead and additional energy consumption of secondary users,and in order to alleviate energy consumption and make full use of the advantages of CSS,an energy-efficient cognitive adaptive two-phase sensing algorithm（CATS）was proposed.The algorithm was a combination of dual threshold energy detection model and two step detection model,and decreased energy consumption under the constraint of detection performance.The mathematical model of CSS based on CATS was presented,and theoretical analysis of the detection probability and energy consumption was given.The proposed CATS would perform the second-phase sensing only when the detection result of the first phase in CATS was inaccurate.According to the detection results of the first-phase and the network status,the proposed CATS would adaptive configure the number of SU participating in cooperation in each stage to achieve the goal of reducing energy consumption and ensuring detection probability.Simulation results show that compared with the traditional two-step CSS,the proposed CATS can reduce energy consumption while ensuring the detection probability.