Cloud Radio-Multistatic Radar: Joint Optimization of Code Vector and Backhaul Quantization

TL;DR

This paper proposes a joint optimization framework for multistatic radar systems with limited backhaul capacity, enhancing detection performance by optimizing code vectors and quantization strategies using information-theoretic measures.

Contribution

It introduces a novel joint optimization method combining Bhattacharyya distance and information-theoretic quantization measures for multistatic radar detection.

Findings

Joint optimization outperforms separate optimization methods.

The proposed approach improves detection performance under backhaul constraints.

Numerical results validate the effectiveness of the optimization framework.

Abstract

A multistatic radar set-up is considered in which distributed receive antennas are connected to a Fusion Center (FC) via limited-capacity backhaul links. Similar to cloud radio access networks in communications, the receive antennas quantize the received baseband signal before transmitting it to the FC. The problem of maximizing the detection performance at the FC jointly over the code vector used by the transmitting antenna and over the statistics of the noise introduced by backhaul quantization is investigated. Specifically, adopting the information-theoretic criterion of the Bhattacharyya distance to evaluate the detection performance at the FC and information-theoretic measures of the quantization rate, the problem at hand is addressed via a Block Coordinate Descent (BCD) method coupled with Majorization-Minimization (MM). Numerical results demonstrate the advantages of the proposed joint optimization approach over more conventional solutions that perform separate optimization.