Parameter Estimation for Perceptive Mobile Networks Based on Two-Level Block Sparsity

Abstract

Integrated sensing and communication (ISAC) is a prospective technique that focuses on enabling simultaneous communication and sensing functions to enhance spectral and energy efficiency in future wireless systems. Perceptive mobile network (PMN) is a typical communication-centric ISAC system that integrates sensing capabilities into cellular networks. In current literature, the estimation of sensing parameters in PMNs has been formulated to a generalized delay-quantized channel model and solved as a block multiple measurement vectors (MMV) reconstruction problem. To further improve the reconstruction performance, this paper estimates the parameters jointly by exploiting the temporal correlation of channels. The sparse signals representing the channel parameters of consecutive symbols are modeled to share partial support, and then a two-stage block MMV orthogonal matching pursuit algorithm is proposed to solve the formulated reconstruction problem. In addition, we further provide the theoretical analysis of the proposed algorithm based on restricted isometry property. Simulation results demonstrate that the proposed algorithm achieves higher accuracy in parameter estimation than the existing block MMV reconstruction algorithms.