Energy Minimization Oriented Resource Allocation for Integrated Sensing and Communication in Marine IoT Networks

2026-07-15

AI Summary

This work addresses energy-constrained integrated sensing and communication systems in maritime Internet of Things by proposing a joint optimization framework that coordinates UAV beamforming, dedicated sensing signals, USV transmit and computing power allocation, and time-frequency resource management to minimize total energy consumption under latency and sensing performance constraints. The original non-convex problem is decomposed via a hierarchical solution architecture into alternating subproblems, enhanced through variable substitution and closed-form solutions for computational efficiency. The algorithm integrates non-orthogonal multiple access, successive convex approximation, and iterative optimization, with performance validated against LINGO-based global optimization. Compared to OFDMA and genetic algorithm benchmarks, the proposed approach reduces system energy consumption by 19.71% and 8%, respectively, while achieving a solution within 8.72% of the LINGO-derived optimum.

Abstract

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.

Problem

• Integrated Sensing and Communication
• Marine IoT
• Energy Minimization
• UAV
• Resource Allocation

Innovation

• Integrated Sensing and Communication (ISAC)
• Energy Minimization
• Non-orthogonal Multiple Access (NOMA)
• Successive Convex Approximation (SCA)
• Marine IoT