多跳认知水声通信中的分布式稀疏频谱检测算法

Distributed Sparse Spectrum Detection in Multihop Cognitive Underwater Acoustict Communication Networks

水声信道常表现为严重的频率选择性衰落、低的声波传播速度和严重的多径效应等。这些特性使得认知水声通信中的频谱检测变得非常困难。除此之外,水声通信网络通常为自组织网络,缺少融合中心,而基于融合中心的频谱检测算法需要将各个认知用户的感知数据传送到融合中心,因此该方法在认知水声通信中是不可行的。与认知无线电类似,由于低的频谱使用率,认知水声通信中的频谱也是稀疏的。考虑到水声信道的特殊性,基于压缩感知理论,该文对认知无线电中的压缩频谱检测算法进行了改进,提出了两种不同情况下(已知水声信道状态信息和未知水声信道状态信息)的适用于认知水声通信的分布式稀疏频谱检测算法。通过近邻认知用户之间的合作,这两种算法利用空间分集增益和联合稀疏特性来提高算法的频谱检测性能。通过分布式计算和局部优化,新算法使得认知用户与其近邻认知用户之间只需进行少量的数据交互。仿真实验结果证明了该文提出的算法在检测认知水声通信系统中频谱空洞的有效性。

Since the underwater acoustic channel suffers often severe frequency-dependent attenuation, low speed of wave propagation and excessive multipath delay spread, the implementation of spectrum detection in Cognitive Underwater Acoustic Communication （CUAC） becomes very difficult. Beside, there is no fusion center in Ad hoc underwater acoustic communication networks. Therefore, the centralized spectrum detection methods in CUAC are not available. Similar to Cognitive Radio （CR）, since the spectrum utility in CUAC is also low, the spectrum is sparse. Based on compressed sensing and considering the specificity of underwater acoustic, compressed spectrum detection algorithm for cognitive radio is improved, and then two distributed cooperative spectrum detection methods, which are suitable for CUAC, are proposed for different scenarios （with and without channel state information）. By strengthening among secondary users, the proposed algorithms obtain spatial diversity gains and exploit joint sparse structure to improve the performance of spectrum detection. Via distributed computation and localized optimization, the new schemes entail low computation and power overhead per cognitive users. Simulation results corroborate the effectiveness of the proposed methods in detecting the spectrum holes in underwater acoustic environment.