Gamma-Distributed Geometric Constellation for ISAC: Design and Analysis

TL;DR

This paper introduces a Gamma-distributed geometric constellation framework for ISAC, optimizing sensing and communication performance using particle swarm optimization and analytical bounds, with competitive results and better system compatibility.

Contribution

It presents a novel constellation design framework combining Gamma and uniform distributions, optimized for ISAC, with analytical bounds and advantages over neural network-based methods.

Findings

The proposed constellation achieves competitive sensing and communication performance.

Analytical bounds for error rate and sensing accuracy are derived.

The design outperforms neural network-based constellations in parameter efficiency and compatibility.

Abstract

A novel Gamma-distributed geometric constellation design framework for integrated sensing and communication (ISAC) is proposed in this paper. In this framework, constellation points are modeled as samples drawn from a parameterized two-dimensional distribution, with a Gamma distribution for the amplitude and a uniform distribution for the phase. End-task performance metrics, namely, the probability of detection for sensing and mutual information for communication, are used as objective functions of the optimization problem, and the problem is solved via particle swarm optimization. We further derive analytical performance bounds for the proposed design, including the union bound on the symbol error rate for communication and the Cramer--Rao bound for sensing parameter estimation. The proposed method is compared with constellations obtained via end-to-end neural network design, demonstrating competitive performance while requiring significantly fewer parameters and no training data. Moreover, the proposed geometric constellation is more compatible with conventional system architectures than probabilistic or neural network-based designs.