认知无线网络中多信道频谱感知周期优化算法

Spectrum Sensing Period Optimization Algorithm in Multi-channel Environment for Cognitive Radio Networks

为了使认知无线电次用户发现并使用更多的空闲频谱资源,提出了一种多信道频谱感知周期优化算法。针对实际网络中各授权信道使用规律的不同,本文基于交替更新理论建立了多信道状态转移模型,将各信道感知周期的选取建模为一个带约束条件的多目标优化问题,并采用遗传算法对其求解,获得了相对较优的多信道感知周期向量。仿真结果表明,本文提出的目标函数能够有效衡量多信道空闲频谱资源检测率。当目标网络中包含8个授权信道时,所提算法可发现的空闲频谱资源占实际空闲频谱资源的68.23%,相对于以相同周期对各信道进行频谱感知的OFDM机制提高17.68%。

In order to discover and employ more spectrum opportunities for the secondary users in cogni- tive radio networks, a multi-channel spectrum sensing period optimization algorithm is proposed. The main idea of this method is to set unequal sensing period for each licensed channel, which has different usage pattern. We first construct the multi-channel states transition model by alternating renewal theory. Secondly, based on the continuous-time Markov chain, the choice of the multi-channel sensing period is modeled as a constrained multi-objective optimization problem. Finally, the multi-objective optimization problem is solved by genetic algorithms. The simulation results validate the performance of the derived objective function. When there are eight licensed channels in the target network, the proposed algorithm discovers 68.23% of free spectrum opportunities, which brings up to 17.68% more opportunities than the OFDM sensing method.