认知无线电网络中次用户平均吞吐量的优化

Optimization for Average Throughput of Secondary Users in Cognitive Radio Networks

协作频谱感知的认知无线电网络中,已有研究表明增加参与协作频谱感知的次用户数量能够提高感知性能,进而提高信道吞吐量。然而,由于信道容量的限制,不断增加参与协作感知的次用户数量并不会使信道吞吐量无限提高,反而会使次用户平均可获得的吞吐量不断降低。针对上述问题,该文以次用户平均吞吐量为优化目标,证明多信道条件下,对于任意给定的融合参数,次用户的平均吞吐量是感知时间的凸函数,并提出交叉迭代算法进行2维优化。仿真结果表明,当信噪比为-10 dB时,次用户使用交叉迭代算法获得的平均吞吐量较已有算法可提高20%以上。

In cooperative spectrum sensing, previous numerical studies show that the sensing performance and the throughput of channel can be improved by increasing the secondary users which are participate in cooperative sensing. Due to the existence of channel capability, the throughput of channel can not be improved infinitely with the increasing number of secondary users, while the average throughput of secondary users decreases severely. In order to improve the average throughput of secondary users, the unimodal characteristics of the secondary users’ throughput are proved to be a function of the sensing time for any given fusion parameter in multi-channel environment. To solve this optimization issue, a cross iterative algorithm is proposed. Computer simulations show that when the average signal-to-noise ratio of secondary users is-10 dB, compared with the classical fusion rule, the proposed algorithm can gain more than 20%of the average throughput of secondary users.