Constellation Selection and Power Control for OFDM-based ISAC: From Theory to Prototype

TL;DR

This paper develops a communication-centric ISAC framework that is standards-compliant and compatible with existing devices, and proposes a low-complexity constellation selection scheme over a finite, off-the-shelf alphabet, achieving an efficient sensing-communication trade-off without custom waveforms or frame-structure changes.

Abstract

Integrated sensing and communication (ISAC) techniques can leverage existing, wide-coverage communication networks to perform sensing tasks, enabling large-scale and low-cost target sensing. However, the inherent randomness of communication data payloads introduces undesired sidelobes in the ambiguity function that may degrade target detection and parameter estimation performance. This paper develops a communication-centric ISAC framework that is standards-compliant and compatible with existing devices. Specifically, we propose a low-complexity constellation selection scheme over a finite, off-the-shelf alphabet, achieving an efficient sensing-communication trade-off without custom waveforms or frame-structure changes. To this end, we analyze two classical sensing receivers including matched filtering (MF) and reciprocal filtering (RF) for ranging measurements, and derive closed-form sensing laws that link constellation statistics to sensing performance. Under any finite-alphabet constellation combination, MF sidelobes depend on the weighted sum of the kurtosis values of the per-subcarrier constellations, while RF noise enhancement depends on the inverse second moment of the transmit symbol, providing a tractable expression for tuning the sensing-communication trade-off. The analysis extends to multi-symbol coherent integration and achieves the expected processing gain. We prove that in flat-fading channels, any Pareto-optimal solution activates no more than three constellations. For frequency-selective channels, a bilevel algorithm with closed-form inner updates attains near-optimal performance while sharply reducing computational complexity. We validate the entire theoretical pipeline with numerical simulations as well as experimental results.

Figures (13)

• Figure 1: Scenarios of ranging measurements using same communication signals.
• Figure 2: Illustration of constellation selection to balance sensing and communication performance.
• Figure 3: Constellation proportions versus $R_{\min}$ ($P_{\text{ave}} = 6$).
• Figure 4: Constellation results per subcarrier allocation with $R_{\min} = 6$ bps/Hz and $N = 64$, $M = 16$.
• Figure 5: Range RMSE comparison for MF and RF.