AI 中文总结
研究面向海洋物联网的集成传感与通信系统能耗最小化问题，通过构建分层架构，联合优化无人机发射波束成形等多变量，采用逐次凸逼近方法设计算法，仿真验证算法有效，相比基准方案降低能耗，与最优值差距小。
AI 中文摘要
集成传感与通信（ISAC）已成为海洋物联网（MIoT）系统的一个有前景的技术框架。然而，所有设备都依赖电池供电，因此能量效率成为限制实际部署的核心瓶颈。本文研究面向MIoT的ISAC系统的能耗最小化问题。在该系统中，无人机（UAV）使用非正交多址接入（NOMA）同时进行目标传感并从无人水面航行器（USV）收集数据，然后将处理后的传感信息和USV数据转发到岸基基站（SBS）。在满足延迟限制和传感性能要求的情况下，通过联合优化多个变量，即无人机发射波束成形、专用传感信号、USV发射功率、无人机计算功率以及传感和通信阶段的时间资源分配，可以使系统总能耗最小化。为解决这个非凸优化问题，我们构建了一个分层解决方案架构，将原始问题分解为独立子问题，并根据其数学特征交替优化每个子问题。具体而言，我们首先推导了封闭形式的USV发射功率解并进行变量替换。采用逐次凸逼近（SCA）方法将剩余的非凸子问题转化为凸形式，并在此基础上设计了高效的迭代算法。仿真结果验证了我们算法在降低系统能耗方面的有效性和准确性。与正交频分多址接入（OFDMA）和遗传算法基准相比，我们的方案分别将系统能耗降低了19.71%和8%。此外，我们优化后的能量值与LINGO求解器求解的最优值之间仅有8.72%的差距。
英文摘要
Integrated sensing and communication (ISAC) has become a promising technical framework for Marine Internet of Things (MIoT) systems. Nevertheless, all devices rely on battery power, so energy efficiency becomes a core bottleneck limiting practical deployment. This paper investigates the energy consumption minimization problem of MIoT-oriented ISAC systems. In this system, an uncrewed aerial vehicle (UAV) uses non-orthogonal multiple access (NOMA) to simultaneously perform target sensing and collect data from uncrewed surface vehicles (USVs), then forwards processed sensing information and USV data to a shore-based base station (SBS). Subject to latency limits and sensing performance requirements, total system energy consumption can be minimized via joint optimization of multiple variables, UAV transmit beamforming, dedicated sensing signal, USV transmit power, UAV computation power, and time resource allocation for sensing and communication phases. To tackle this non-convex optimization problem, we build a layered solution architecture that divides the original problem into independent subproblems and optimizes each alternately according to its mathematical features. Specifically, we first derive closed-form USV transmit power solutions and conduct variable substitution. The successive convex approximation (SCA) method is adopted to convert remaining non-convex subproblems into convex forms, on which we design efficient iterative algorithms. Simulation results verify the validity and accuracy of our algorithm in reducing system energy consumption. Compared with orthogonal frequency division multiple access (OFDMA) and genetic algorithm benchmarks, our scheme lowers system energy consumption by 19.71% and 8%, respectively. In addition, our optimized energy value only has an 8.72% gap from the optimum solved by the LINGO solver.
Comments15 pages, 11 figures
