FDA-MIMO-Based Integrated Multi-Target Sensing and Communication System with Complex Coefficients Information Embedding

TL;DR

This paper presents an FDA-MIMO-based integrated sensing and communication system that enhances multi-target detection and communication efficiency through novel algorithms, ambiguity resolution, and information embedding, with proven performance improvements.

Contribution

It introduces a joint estimation method, a low-complexity spectrum estimation algorithm, and a complex coefficients embedding scheme for improved multi-target sensing and communication.

Findings

The proposed system achieves accurate multi-target estimation.

The LCSSE algorithm reduces computational complexity.

The CCIE scheme enhances communication rates.

Abstract

The echo signals of frequency diverse array multiple-input multiple-output (FDA-MIMO) feature angle-range coupling, enabling simultaneous discrimination and estimation of multiple targets at different locations. In light of this, based on FDA-MIMO, this paper explores an sensing-centric integrated sensing and communication (ISAC) system for multi-target sensing. At the base station, we propose the FDA-MIMO-based spatial spectrum multi-target estimation (SSMTE) method, which first jointly estimates the angle and distance of targets and then estimates the velocities. To reduce the sensing computational complexity, the low-complexity spatial spectrum estimation (LCSSE) algorithm is proposed. LCSSE reduces the complexity without degrading the sensing performance by converting the joint angle-range search into two one-dimensional searches. To address the range ambiguity caused by frequency offset, a frequency offset design criterion (FODC) is proposed. It designs the integer and fractional components of the frequency offset to ensure the ambiguity distance exceeds the maximum sensing range, thereby alleviating parameters pairing errors. Moreover, the complex coefficients information embedding (CCIE) scheme is designed to improve system communication rates, which carries extra bits by selecting complex coefficients from the coefficient vector. The closed-form expressions for the bit error rate (BER) tight upper bound and the Cram\'er-Rao bound (CRB) are derived. Simulation results show that the proposed system excels in multi-target sensing and communications.