Low-Complexity Multi-Target Detection in ELAA ISAC

TL;DR

This paper introduces a low-complexity, manifold optimization algorithm for multi-target detection and communication in ELAA ISAC systems, significantly reducing computational time compared to traditional methods.

Contribution

It develops a Riemannian stochastic gradient descent-based augmented Lagrangian algorithm that ensures constraint compliance and achieves ultra-low complexity for large-scale antenna systems.

Findings

56 times faster than benchmark for 257 antennas

Achieves high communication sum rate with sensing constraints

Significantly reduces computational time for ELAA systems

Abstract

Multi-target detection and communication with extremely large-scale antenna arrays (ELAAs) operating at high frequencies necessitate generating multiple beams. However, conventional algorithms are slow and computationally intensive. For instance, they can simulate a \num{200}-antenna system over two weeks, and the time complexity grows exponentially with the number of antennas. Thus, this letter explores an ultra-low-complex solution for a multi-user, multi-target integrated sensing and communication (ISAC) system equipped with an ELAA base station (BS). It maximizes the communication sum rate while meeting sensing beampattern gain targets and transmit power constraints. As this problem is non-convex, a Riemannian stochastic gradient descent-based augmented Lagrangian manifold optimization (SGALM) algorithm is developed, which searches on a manifold to ensure constraint compliance. The algorithm achieves ultra-low complexity and superior runtime performance compared to conventional algorithms. For example, it is \num{56} times faster than the standard benchmark for \num{257} BS antennas.