Configuration Tuning for ISAC: Cost-Efficient Adaptation via RACE-CMA

TL;DR

This paper introduces RACE-CMA, a cost-efficient configuration tuning framework for ISAC systems that enhances sensing reliability and reduces computational costs through a novel optimization approach.

Contribution

It presents a new feedback-driven tuning framework with RACE-CMA, combining racing, noise-aware ranking, and constraint handling for adaptive sensing in ISAC.

Findings

Sensing reliability improved by about 35%

Computational cost reduced by about 25%

Performance cost efficiency roughly doubled

Abstract

This paper studies a feedback driven configuration tuning framework for adaptive sensing feedback in Integrated Sensing and Communication (ISAC) systems. We propose a framework in which the User Equipment (UE) adapts sensing parameters under dynamic conditions while satisfying network defined constraints. The problem is formulated as a stochastic constrained optimization problem, to improve sensing reliability and latency. We consider a bistatic ISAC sensing feedback setup and instantiate the framework via threshold optimization as a representative case study, enabling benchmarking against baseline methods. To ensure efficiency under UE computational limits, we propose Ranking Aware, Constrained, and Efficient CMAES (RACE CMA), which integrates two stage racing, common random numbers, noise aware ranking, and feasible constraint handling. Results show that the proposed approach improves sensing reliability by about 35 percent while reducing computational cost by about 25 percent, yielding roughly a twofold gain in performance cost efficiency. This highlights that UE side configuration tuning is a promising mechanism for enhancing closed loop ISAC performance under practical system constraints.