GPU-Accelerated Robust MA--RIS Pre-Optimization for Communication and Target Illumination
Abstract
Movable antennas (MAs) and reconfigurable intelligent surfaces (RISs) offer strong spatial control for integrated sensing and communication (ISAC), but repeatedly adapting MA positions and RIS phases to each channel realization can be impractical. This letter develops a GPU-accelerated robust pre-optimization framework that jointly selects slow-timescale MA geometry and RIS phases and returns a reusable Pareto set balancing expected worst-user rate and transmit-side worst-target illumination. The illumination objective measures directional transmitted power rather than complete echo-detection or parameter-estimation performance. The proposed Regime-Aware Multiobjective Pre-optimization (RAMP) preserves regime-specific Pareto populations, regulates cross-regime mating using Pareto-survival feedback, and constructs a worst-regime robust archive. Numerical results demonstrate competitive robust Pareto quality against the considered evolutionary baselines while avoiding online physical re-optimization.
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BibTeX
@misc{dung2026gpuaccelerated,
title = {{GPU-Accelerated Robust MA--RIS Pre-Optimization for Communication and Target Illumination}},
author = {Tran Le Dung and Ho Viet Duc Luong},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.22431},
eprint = {2610.22431},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.22431},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - GPU-Accelerated Robust MA--RIS Pre-Optimization for Communication and Target Illumination
AU - Tran Le Dung
AU - Ho Viet Duc Luong
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.22431
UR - https://arxiv.org/abs/2610.22431
AB - Movable antennas (MAs) and reconfigurable intelligent surfaces (RISs) offer strong spatial control for integrated sensing and communication (ISAC), but repeatedly adapting MA positions and RIS phases to each channel realization can be impractical. This letter develops a GPU-accelerated robust pre-optimization framework that jointly selects slow-timescale MA geometry and RIS phases and returns a reusable Pareto set balancing expected worst-user rate and transmit-side worst-target illumination. The illumination objective measures directional transmitted power rather than complete echo-detection or parameter-estimation performance. The proposed Regime-Aware Multiobjective Pre-optimization (RAMP) preserves regime-specific Pareto populations, regulates cross-regime mating using Pareto-survival feedback, and constructs a worst-regime robust archive. Numerical results demonstrate competitive robust Pareto quality against the considered evolutionary baselines while avoiding online physical re-optimization.
ER -