Successive Linear Approximation VBI for Joint Sparse Signal Recovery and Dynamic Grid Parameters Estimation

For many practical applications in wireless communications, we need to recover a structured sparse signal from a linear observation model with dynamic grid parameters in the sensing matrix. Conventional expectation maximization (EM)-based compressed sensing (CS) methods, such as turbo compressed sensing (Turbo-CS) and turbo variational Bayesian inference (Turbo-VBI), have double-loop iterations, where the inner loop (E-step) obtains a Bayesian estimation of sparse signals and the outer loop (M-step) obtains a point estimation of dynamic grid parameters. This leads to a slow convergence rate. Furthermore, each iteration of the E-step involves a complicated matrix inverse in general. To overcome these drawbacks, we first propose a successive linear approximation VBI (SLA-VBI) algorithm that can provide Bayesian estimation of both sparse signals and dynamic grid parameters. Besides, we simplify the matrix inverse operation based on the majorization-minimization (MM) algorithmic framework. In addition, we extend our proposed algorithm from an independent sparse prior to more complicated structured sparse priors, which can exploit structured sparsity in specific applications to further enhance the performance. Finally, we apply our proposed algorithm to solve two practical application problems in wireless communications and verify that the proposed algorithm can achieve faster convergence, lower complexity, and better performance compared to the state-of-the-art EM-based methods.

Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously, where a spatially non-stationary Markov random field (MRF) model is proposed to capture the 2-D joint burst sparsity. Specifically, the base station (BS) first transmits downlink pilots to sense the targets in the scattering environment. Then the user transmits uplink pilots to estimate the communication channel. Finally, joint scattering environment sensing and channel estimation are performed at the BS based on the reflected downlink pilot signal and received uplink pilot signal. A message passing based algorithm is designed based on the turbo approach. Furthermore, a variation of the expectation maximization method, based on pseudo-likelihood approximation, is proposed to learn the unknown parameters in the non-stationary MRF model that capture the random sizes and locations of the scattering clusters. The advantages of our proposed scheme are verified in the simulations.

Joint Scattering Environment Sensing and Channel Estimation for Integrated Sensing and Communication

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a partially common sparsity. By exploiting this, we propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously. Specifically, the base station (BS) first transmits downlink pilots to sense the targets in the scattering environment. Then the user transmits uplink pilots to estimate the communication channel. Finally, joint scattering environment sensing and channel estimation are performed at the BS based on the reflected downlink pilot signal and received uplink pilot signal. A message passing based algorithm is designed by combining the turbo approach and the expectation maximization method. The advantages of our proposed scheme are verified in the simulations.