Underwater wireless sensor networks (UWSNs) contain quite a lot of components such as vehicles and sensors that are deployed in a specific acoustic area to perform collaborative monitoring and data collection errands. These networks are adopted interactively between diverse nodes and ground‐based stations. Currently, UWSNs face problems and challenges that pertain to limited bandwidth, media access control, high propagation delay, 3D topology, spectrum sensing, resource utilization, routing, and power constraints. This proposal deals with the intelligent spectrum sensing in underwater cognitive sonar communication networks (CSCN). Here, the improved performance of spectrum sensing in underwater communication is attained by optimizing the cooperative spectrum sensing and data transmission. The parameters of system like subchannel allocation and transmission power is optimized by a new hybrid meta‐heuristic algorithm by integrating the concepts of deer hunting optimization algorithm (DHOA) and lion algorithm (LA) termed as lion‐enabled DHOA (L‐DHOA). The main intention of optimizing these parameters is to maximize the spectrum efficiency (SE) and energy efficiency (EE) of the underwater channel communication system. From the analysis, with respect to convergence rate, minimum detection probability, and local sensing time, it is proved that the novel hybrid optimization algorithm keeps a great role in making the trade‐off between the SE and EE in underwater channel modeling.

中文翻译：

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通过使用混合优化的最佳协作频谱感测来提高水下信道通信的频谱效率和能效

水下无线传感器网络（UWSN）包含大量组件，例如车辆和传感器，它们被部署在特定的声学区域中以执行协作监视和数据收集任务。这些网络在不同的节点和地面站之间以交互方式被采用。当前，UWSN面临与带宽受限，媒体访问控制，高传播延迟，3D拓扑，频谱感测，资源利用率，路由和功率约束有关的问题和挑战。该提议涉及水下认知声纳通信网络（CSCN）中的智能频谱传感。在此，通过优化协同频谱感测和数据传输来实现水下通信中频谱感测的改进性能。诸如子信道分配和传输功率之类的系统参数通过一种新的混合元启发式算法进行了优化，它整合了猎鹿优化算法（DHOA）和狮子算法（LA）的概念，这些概念称为启用了狮子的DHOA（L-DHOA）。优化这些参数的主要目的是使水下信道通信系统的频谱效率（SE）和能量效率（EE）最大化。通过分析，在收敛速度，最小检测概率和局部感知时间方面，证明了新型混合优化算法在水下通道建模中在SE和EE之间做出取舍方面发挥了重要作用。