Fronthaul-Constrained Distributed Radar Sensing

TL;DR

This paper investigates optimizing fronthaul compression and time allocation in distributed radar networks to enhance sensing performance under capacity constraints, proposing an efficient algorithm and demonstrating significant gains through simulations.

Contribution

It introduces a joint optimization framework for fronthaul compression and time allocation in distributed radar sensing, with a novel algorithm and analysis of its convergence and complexity.

Findings

Distributed sensing improves scene coverage.

Joint optimization outperforms separate approaches.

Significant performance gains in capacity-limited scenarios.

Abstract

In this paper, we study a network of distributed radar sensors that collaboratively perform sensing tasks by transmitting their quantized radar signals over capacity-constrained fronthaul links to a central unit for joint processing. We consider per-antenna and per-radar vector quantization and fronthaul links with dedicated resources as well as shared resources based on time-division multiple access. For this setting, we formulate a joint optimization problem for fronthaul compression and time allocation that minimizes the Cramer Rao bound of the aggregated radar signals at the central unit. Since the problem does not admit a standard form that can be solved by existing commercial numerical solvers, we propose refomulations that enable us to develop an efficient suboptimal algorithm based on semidefinite programming and alternating convex optimization. Moreover, we analyze the convergence and complexity of the proposed algorithm. Simulation results confirm that a significant performance gain can be achieved by distributed sensing, particularly in practical scenarios where one radar may not have a sufficient view of all the scene. Furthermore, the simulation results suggest that joint fronthaul compression and time allocation are crucial for efficient exploitation of the limited fronthaul capacity.