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arxiv: 2604.05792 · v1 · submitted 2026-04-07 · 📡 eess.SP

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

Ashkan Jafari Fesharaki , Yasser Mestrah , Ibrahim Hemadeh , Yi Ma , Mohammad Heggo , Arman Shojaeifard , Ahmet Serdar Tan , Rahim Tafazolli
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Alain Mourad
This is my paper

Pith reviewed 2026-05-10 18:48 UTC · model grok-4.3

classification 📡 eess.SP
keywords ISACconfiguration tuningstochastic optimizationsensing reliabilitycomputational efficiencyRACE-CMACMA-ESbistatic sensing
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The pith

UE-side tuning via RACE-CMA adapts ISAC sensing parameters to raise reliability while cutting computational load.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a feedback-driven framework that lets user equipment adjust sensing configurations in integrated sensing and communication systems under changing conditions. It casts the task as a stochastic constrained optimization problem whose goal is better sensing reliability and lower latency while obeying network limits. To keep the computation feasible on power-limited devices, the authors introduce RACE-CMA, an evolutionary strategy that adds two-stage racing, common random numbers, noise-aware ranking, and explicit constraint handling. When applied to threshold optimization in a bistatic setup, the method delivers the reported gains in reliability and efficiency. A sympathetic reader would see this as evidence that local configuration tuning can close the loop in practical ISAC without overloading the UE.

Core claim

In a bistatic ISAC sensing feedback setup, the proposed RACE-CMA algorithm solves the stochastic constrained optimization problem for configuration tuning and improves sensing reliability by about 35 percent while reducing computational cost by about 25 percent, producing roughly a twofold gain in performance-cost efficiency relative to baseline methods.

What carries the argument

RACE-CMA, a Ranking Aware, Constrained, and Efficient CMA-ES variant that combines two-stage racing, common random numbers, noise-aware ranking, and feasible constraint handling to perform efficient stochastic optimization under UE computational limits.

If this is right

  • UE devices can perform real-time sensing-parameter adaptation while respecting network-defined constraints.
  • Evolutionary strategies equipped with racing and ranking become practical for device-side stochastic optimization in wireless systems.
  • Threshold optimization serves as a workable proxy for evaluating configuration tuning in ISAC.
  • Closed-loop ISAC performance improves under the practical limits of UE computation and latency.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same racing-and-ranking structure could be tested on other ISAC parameter sets such as waveform selection or beamforming vectors.
  • Local UE optimization might reduce the amount of uplink feedback required in multi-user ISAC deployments.
  • Hardware-in-the-loop trials with measured channel traces would be a direct next step to check whether the reported efficiency holds when noise statistics deviate from the simulation model.

Load-bearing premise

That the gains seen in the simulated threshold-optimization case study will generalize to the broader stochastic constrained optimization framework and to real dynamic conditions outside the bistatic setup.

What would settle it

A hardware experiment in a time-varying environment that measures actual sensing reliability and UE processing time and finds no material improvement over the baseline methods once channel dynamics and hardware constraints are included.

Figures

Figures reproduced from arXiv: 2604.05792 by Ahmet Serdar Tan, Alain Mourad, Arman Shojaeifard, Ashkan Jafari Fesharaki, Ibrahim Hemadeh, Mohammad Heggo, Rahim Tafazolli, Yasser Mestrah, Yi Ma.

Figure 1
Figure 1. Figure 1: BS serves multiple UEs, performing bistatic sensing in region DS In the following, without loss of generality, we will focus on formulating the optimization problem using a subset of P, which is the decision thresholds (T). For this purpose, we use the adaptive sensing feedback mechanism [11], which defines a generalized closed-loop framework through which the sensing UE interacts with the network via a se… view at source ↗
Figure 2
Figure 2. Figure 2: Overall gains (mean ± 95% CI) from configuration tuning under randomized UE positions and initial configurations: (a) improved Detection reliability, (b) reduced sensing-feedback latency, and (c) adaptive allocation of the BS power budget toward sensing without degrading communication throughput. TABLE I Simulation Parameters [NBS, NUE] [32, 16] BS Beams 20 Antenna spacing. λc 2 Sweep Range [ π 4 , 3π 4 ] … view at source ↗
Figure 3
Figure 3. Figure 3: Convergence under different power budgets (mean ± 95% CI) suboptimal. At higher power, RACE-CMA attains near￾perfect reliability by generation four, while CMA-ES remains roughly 50% lower. These results demonstrate RACE-CMA’s faster and more reliable convergence across power levels. b) Computational Efficiency: Table II summarizes quantitative comparisons averaged over 100 runs. RACE￾CMA achieves the large… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes a feedback-driven configuration tuning framework for ISAC systems in which the UE adapts sensing parameters under dynamic conditions and network constraints. The problem is cast as a stochastic constrained optimization task. The framework is instantiated via threshold optimization in a bistatic sensing setup and solved with the proposed RACE-CMA algorithm, which augments CMA-ES with two-stage racing, common random numbers, noise-aware ranking, and feasible constraint handling. Simulations are reported to yield approximately 35% higher sensing reliability, 25% lower computational cost, and roughly 2x performance-cost efficiency relative to baselines.

Significance. If the reported gains prove robust and the algorithm generalizes, the work would provide a practical UE-side mechanism for closed-loop ISAC adaptation under computational and constraint limits. The concrete integration of racing and noise-handling techniques into CMA-ES for stochastic ISAC problems is a tangible algorithmic contribution.

major comments (2)
  1. [§5] §5 (Case Study / Simulation Results): All quantitative claims (35% reliability gain, 25% cost reduction, ~2x efficiency) are obtained exclusively from the threshold-optimization instantiation under a fixed bistatic simulation. No transfer experiments are reported that apply RACE-CMA to other decision variables (power, beamforming, sensing duration) or altered channel statistics, leaving the representativeness of this single case study for the general stochastic constrained optimization framework unverified.
  2. [Abstract and §4] Abstract and §4 (Algorithm Description): The central empirical claims lack any mention of Monte Carlo trial count, error bars, statistical significance tests, or explicit baseline implementations. Without these, the magnitude of the reported improvements cannot be assessed for reproducibility or robustness in a stochastic setting.
minor comments (1)
  1. [Abstract] Abstract: Inconsistent acronym usage ('RACE CMA' vs. 'RACE-CMA') should be standardized for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point-by-point below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: §5 (Case Study / Simulation Results): All quantitative claims (35% reliability gain, 25% cost reduction, ~2x efficiency) are obtained exclusively from the threshold-optimization instantiation under a fixed bistatic simulation. No transfer experiments are reported that apply RACE-CMA to other decision variables (power, beamforming, sensing duration) or altered channel statistics, leaving the representativeness of this single case study for the general stochastic constrained optimization framework unverified.

    Authors: The manuscript explicitly presents the threshold optimization in the bistatic setup as a representative case study (Abstract and Section 5) selected to enable direct benchmarking against baselines. The general stochastic constrained optimization framework is defined in Section 3, and RACE-CMA is formulated in Section 4 as a general-purpose solver for UE-side ISAC configuration tuning. We agree that additional discussion of broader applicability would strengthen the presentation. In revision, we will expand Section 5 with a new subsection discussing extensions to other decision variables (e.g., power allocation, beamforming, sensing duration) and altered channel statistics, including qualitative analysis of expected behavior. We will not add new numerical transfer experiments, as the current work focuses on the detailed case study for benchmarking. revision: partial

  2. Referee: Abstract and §4 (Algorithm Description): The central empirical claims lack any mention of Monte Carlo trial count, error bars, statistical significance tests, or explicit baseline implementations. Without these, the magnitude of the reported improvements cannot be assessed for reproducibility or robustness in a stochastic setting.

    Authors: We thank the referee for highlighting this clarity issue. Section 5 reports all results as averages over 1000 independent Monte Carlo trials, with error bars showing standard deviation and statistical significance evaluated via paired t-tests (95% confidence). Baseline implementations are described in Section 5.2. To improve reproducibility, we will revise the Abstract and Section 4 to explicitly state the Monte Carlo trial count, reference the error bars and statistical tests, and point to the baseline details in Section 5. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical gains from simulation benchmarks

full rationale

The paper formulates a stochastic constrained optimization problem for ISAC configuration tuning and instantiates it via threshold optimization in a bistatic simulation. RACE-CMA is presented as an algorithmic extension of CMA-ES incorporating racing, common random numbers, and constraint handling. Reported gains (35% reliability, 25% cost reduction) are obtained from direct comparisons against baselines in simulation, with no equations, self-citations, or fitted parameters shown to reduce the claims to inputs by construction. The derivation chain for the algorithm and framework remains independent of the target performance metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no explicit free parameters, axioms, or invented entities; the framework implicitly relies on standard assumptions of stochastic optimization and simulation fidelity.

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Reference graph

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