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中文摘要：
在认知水声通信中,频谱感知是动态频谱接入和动态频谱共享的基础.相比于陆地环境,水下环境变化剧烈：如严重的频率选择性衰落、低的声波传播速度和多径效应等.因此,许多可用于认知无线电的频谱感知算法不能直接用于认知水声通信.除此之外,水下用户或节点均用电池供电,而基于融合中心（融合中心可能与感知用户相隔很远）的频谱感知算法需要将各个感知用户的感知数据传送到融合中心,由于功率受限并且计算资源有限,该方法几乎是不可行的.类似于无线通信系统,水声通信系统中的频谱使用率也很低,这使得水声通信信号在频域是稀疏的.研究结果表明,压缩感知算法可以有效的恢复稀疏信号.基于此,为了克服前述困难,本文提出了分布式压缩频谱感知算法.在该算法中,多个认知用户通过协作的方式获得空间分集增益来克服水声信道的严重衰落,并利用联合稀疏性来增强恢复稀疏信号的能力.通过分布式计算,该算法将协作频谱感知转化为去中心的局部优化问题,对于每个感知用户而言,只需要与其相邻的感知用户进行数据交互,这大大减少了每个感知用户的计算量和传输数据所需的功率消耗.本文对所提出的算法进行了仿真,并与其他算法进行了比较.实验结果证明了本算法在认知水声通信中检测频谱的有效性.
英文摘要：
Spectrum sensing is a key technology for dynamic spectrum access and dynamic spectrum sharing in cognitive underwater acoustic communication（CUAC） networks.Because the underwater environment is very different from terrestrial environment,such as severe frequency-dependent attenuation,low speed of wave propagation and excessive multipath delay spread,many spectrum sensing techniques in cognitive radio cannot be simply transplanted to CUAC.In under water,nodes or users in underwater are battery operated.Spectrum sensing based on fusion center（FC） may be infeasible due to power constraints and limited computation resources,because nodes or users need transfer all data to the FC（possibly located far away）.Similar to terrestrial wireless communication networks,due to the low percentage of spectrum occupancy in underwater acoustic communication,the signals are also very sparse in the frequency domain.Recent researches have shown that sparse signals can be reliably recovered based on compressive sensing.Taking this advantage,a distributed cooperative compressive spectrum sensing approach is proposed to overcome the underwater channel fading,the limited power and computing resources.To obtain spatial diversity gains against underwater channel fading,and to enhance sparsity recovery ability by exploiting joint sparse structure,multiple secondary users collaborate to sense spectrum in the proposed scheme.In our new cooperative spectrum detection model,spectrum sensing boils down to recover the sparse energy vectors from multiple measurement vectors.To reduce the data acquisition costs,distributed computation and local optimization is utilized to solve the spectrum sensing in a distributed manner.In this way,the new algorithm entails low computation and power overhead per secondary users,and affordable data transferring among one-hop neighbors.Benefiting from the distributed computation and spatial diversity,this new method is able to attain high sensing performance at a reasonable computation and power overhead
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