CAJ | 학술논문

　　针对大规模认知无线电网络中协同频谱感存在的感知时间长、能量消耗过多、缺乏自适应能力等问题，提出了一种基于分簇协同的Q-学习频谱感知算法。该算法利用分簇机制，把大规模的环境变成小规模的簇内环境，分簇后簇内采用协同Q-学习，通过代理在与环境交互过程中不断试错来确定频谱检测的最佳门限值，使系统具有自主学习的能力。实验结果表明：大规模环境下系统的检测性能有显著提高。
　　침대대규모인지무선전망락중협동빈보감존재적감지시간장、능량소모과다、결핍자괄응능력등문제，제출료일충기우분족협동적Q-학습빈보감지산법。해산법이용분족궤제，파대규모적배경변성소규모적족내배경，분족후족내채용협동Q-학습，통과대리재여배경교호과정중불단시착래학정빈보검측적최가문한치，사계통구유자주학습적능력。실험결과표명：대규모배경하계통적검측성능유현저제고。
Due to long sensing time,excess energy consuming and incapability of adaptive, this paper proposes a algorithm of spectrum sensing based on clustering cooperative Q-learning in large-scale cognitive radios systems. The algorithm uses cluster mechanism,divided the large-scale environment into small ones, using the collaborative q-learning in the divided clusters. Obtain the optimal threshold of spectrum detection by continuous processes of “trial and error” in the interaction between agents and environment . Through this way, the system will have the ability of autonomous learning. The experimental results show that the detection performance of system improved significantly.