Array Resource Allocation for Radar and Communication Integration Network

TL;DR

This paper develops array resource allocation algorithms for radar and communication integration networks, enhancing localization accuracy and communication capacity through optimization techniques.

Contribution

It introduces a novel array allocation framework using MM and PGD methods for joint radar and communication performance optimization.

Findings

Improved localization accuracy in RCI networks.

Enhanced communication capacity with optimized array allocation.

Algorithms outperform baseline methods in simulations.

Abstract

A radar and communication integration (RCI) system has great flexibility in allocating antenna resources to guarantee both radar and communication performance. This paper considers the array allocation problems for multiple target localization and multiple platforms communication in an RCI network. The objective of array allocation is to maximize the communication capacity for each channel and to minimize the localization error for each target. In this paper, we firstly build a localization and communication model for array allocation in an RCI network. Minorization maximization (MM) is then applied to create surrogate functions for multiple objective optimization problems. The projected gradient descent (PGD) method is further employed to solve two array allocation problems with and without a certain communication capacity constraint. Computer simulations are conducted to evaluate the performance of the proposed algorithms. The results show that the proposed algorithms have improved localization and communication performance after efficiently allocating the array resource in the RCI network.