Efficient Resource Allocation for Multi-User and Multi-Target MIMO-OFDM Underwater ISAC

TL;DR

This work tackles the challenge of simultaneous multi-user communication and multi-target sensing in underwater environments by introducing an interleaved OFDM-based MIMO-UWA-ISAC system with a horizontal array. It formulates a multi-objective resource allocation problem aiming to maximize the product of rate and range (PRR) while satisfying sensing and PAPR constraints, and solves it using a two-dimensional grouped random search (TDGRS) algorithm. The approach enables efficient downlink data transmission and robust target sensing through adaptive subcarrier interleaving and power/frequency allocation, validated on real-world UWA channels with substantial gains in convergence speed and resilience over baseline methods. The results underscore the practicality of TDGRS for fast, robust ISAC resource optimization in IoUT scenarios, with implications for scalable, multi-user underwater networks.

Abstract

Integrated sensing and communication (ISAC) technology is crucial for next-generation underwater networks. However, covering multiple users and targets and balancing sensing and communication performance in complex underwater acoustic (UWA) environments remains challenging. This paper proposes an interleaved orthogonal frequency division multiplexing-based MIMO UWA-ISAC system, which employs a horizontal array to simultaneously transmit adaptive waveforms for downlink multi-user communication and omnidirectional target sensing. A multi-objective optimization framework is formulated to maximize the product of communication rate and range (PRR) while ensuring sensing performance and peak-to-average power ratio (PAPR) constraints. To solve this mixed-integer nonconvex problem, a two-dimensional grouped random search algorithm is developed, efficiently exploring subcarrier interleaved patterns and resource allocation schemes. Numerical simulations under real-world UWA channels demonstrate the designed system's superiority and effectiveness: our algorithm achieves 90% faster convergence than conventional exhaustive search with only a marginal 0.5 kbps km PRR degradation. Furthermore, the proposed resource allocation scheme maintains robustness beyond the baseline allocation schemes under stringent PRR and PAPR constraints.

Figures (8)

• Figure 1: Real-world UWA environment and experiment layout.
• Figure 4: PRR versus $\mathrm{PAPR}_0$ with $PRR_{min}=4$ kbps$\cdot$km.
• Figure : (a) Different $G$.
• Figure : (a) $N_u=2$.
• Figure : (a) Different $G$.