Joint Optimization of Geometric and Probabilistic Constellation Shaping for OFDM-ISAC Systems

TL;DR

This paper explores how joint geometric and probabilistic constellation shaping can optimize the trade-off between sensing and communication in OFDM-ISAC systems using an autoencoder framework, leading to improved performance.

Contribution

It introduces a novel joint constellation shaping method that combines geometric and probabilistic approaches within an autoencoder framework for OFDM-ISAC systems.

Findings

Joint shaping enables a dynamic trade-off between sensing and communication.

Joint shaping significantly outperforms legacy modulation formats.

Different shaping strategies are preferred depending on whether sensing or communication is prioritized.

Abstract

6G communications systems are expected to integrate radar-like sensing capabilities enabling novel use cases. However, integrated sensing and communications (ISAC) introduces a trade-off between communications and sensing performance because the optimal constellations for each task differ. In this paper, we compare geometric, probabilistic and joint constellation shaping for orthogonal frequency division multiplexing (OFDM)-ISAC systems using an autoencoder (AE) framework. We first derive the constellation-dependent detection probability and propose a novel loss function to include the sensing performance in the AE framework. Our simulation results demonstrate that constellation shaping enables a dynamic trade-off between communications and sensing. Depending on whether sensing or communications performance is prioritized, geometric or probabilistic constellation shaping is preferred. Joint constellation shaping combines the advantages of geometric and probabilistic shaping, significantly outperforming legacy modulation formats.