Multi-RIS Deployment Optimization for mmWave ISAC Systems in Real-World Environments

TL;DR

This paper proposes an energy-efficient multi-RIS deployment optimization for mmWave ISAC systems in real-world environments, improving coverage and sensing accuracy while minimizing RIS size.

Contribution

It introduces a practical multi-RIS-ISAC system model, formulates a novel deployment optimization problem, and develops an iterative algorithm to enhance real-world performance.

Findings

Optimized RIS deployment improves coverage and sensing accuracy.

The proposed method outperforms existing approaches in simulations.

RIS size is minimized while maintaining system performance.

Abstract

Reconfigurable intelligent surface-assisted integrated sensing and communication (RIS-ISAC) presents a promising system architecture to leverage the wide bandwidth available at millimeter-wave (mmWave) frequencies, while mitigating severe signal propagation losses and reducing infrastructure costs. To enhance ISAC functionalities in the future air-ground integrated network applications, RIS deployment must be carefully designed and evaluated, which forms the core motivation of this paper. To ensure practical relevance, a multi-RIS-ISAC system is established, with its signal model at mmWave frequencies demonstrated using ray-launching calibrated to real-world environments. On this basis, an energy-efficiency-driven optimization problem is formulated to minimize the multi-RIS size-to-coverage sum ratio, comprehensively considering real-world RIS deployment constraints, positions, orientations, as well as ISAC beamforming strategies at both the base station and the RISs. To solve the resulting non-convex mixed-integer problem, a simplified reformulation based on equivalent gain scaling method is introduced. A two-step iterative algorithm is then proposed, in which the deployment parameters are determined under fixed RIS positions in the first step, and the RIS position set is updated in the second step to progressively approach the optimum solution. Simulation results based on realistic parameter benchmarks present that the optimized RISs deployment significantly enhances communication coverage and sensing accuracy with the minimum RIS sizes, outperforming existing approaches.