Multiple Measurement Vector-based Sparse Bayesian Learning Channel Estimation for Efficient D2D Discovery and Pairing

Device-to-Device (D2D) communication in next-generation networks enables the creation of localized networks by directly connecting nearby devices, reducing base station traffic and enhancing spectral efficiency through frequency reuse. This work developed multiple measurement vector-based compressed sensing problem for the D2D system, where the composite multiple measurement vector (MMV) D2D channel is low-rank and exhibits common sparsity across multiple measurements. To exploit this common sparsity channel structure, we propose the MMV-based sparse Bayesian learning (MSBL) algorithm that achieves precise channel estimation by leveraging common sparsity. These estimates are then utilized to calculate the achievable rates for scheduling decisions for both cellular users and D2D links. Simulation results demonstrate the efficacy of the proposed MSBL method in improving rate predictions and scheduling accuracy in dense D2D networks.