Performance Optimization and Parameters Estimation for MIMO-OFDM Dual-functional Communication-radar Systems

TL;DR

This paper investigates parameter estimation in MIMO-OFDM dual-functional communication-radar systems, optimizing transmit powers and proposing a tensor-based method that approaches the theoretical CRLB for improved accuracy.

Contribution

It introduces a tensor-based estimation method and power optimization strategy for MIMO-OFDM systems, enhancing parameter accuracy in dual-functional radar-communication applications.

Findings

Tensor-based method approaches CRLB performance

Power optimization improves estimation accuracy

Proposed method efficiently recovers multiple target parameters

Abstract

In dual-functional communication-radar systems, common radio frequency (RF) signals are used for both communication and detection. For better compatibility with existing communication systems, we adopt multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) signals as integrated signals and investigate the estimation performance of MIMO-OFDM signals. We first analyze the Cramer-Rao lower bound (CRLB) of parameters estimation. Then, transmit powers over different subcarriers are optimized to achieve the best tradeoff between transmission rate and estimation performance. Finally, we propose a more accurate estimation method which utilizes canonical polyadic decomposition (CPD) of three-order tensor to obtain the parameter matrices. Due to the characteristic of the column structure of the parameter matrices, we just need to use DFT / IDFT to recover the parameters of multiple targets. The simulation results show that the estimation method based on tensor can achieve performance close to CRLB and the estimation performance can be improved by optimizing the transmit powers.