1. Gaussian-Mixture-Model Based Clutter Suppression in Perceptive Mobile Networks
- Author
-
Y. Jay Guo, Md. Lushanur Rahman, J. Andrew Zhang, Zhiping Lu, and Xiaojing Huang
- Subjects
Computer science ,Estimation theory ,Orthogonal frequency-division multiplexing ,020206 networking & telecommunications ,02 engineering and technology ,Mixture model ,Computer Science Applications ,law.invention ,Compressed sensing ,law ,Modeling and Simulation ,Expectation–maximization algorithm ,0202 electrical engineering, electronic engineering, information engineering ,Cellular network ,Clutter ,Electrical and Electronic Engineering ,Radar ,Algorithm - Abstract
Suppression of undesired non-information bearing multipaths, aka clutter, from received signals is a key process for sensing parameter estimation in the perceptive mobile network, a next generation mobile network that integrates radar sensing into communications. In this correspondence, we propose a novel clutter suppression method based on the Gaussian mixture model (GMM) and expectation maximization (EM) estimation, which can achieve fast and effective clutter estimation requiring only a small number of samples. We then apply a one-dimension (1D) compressive sensing (CS) based sensing algorithm to extract useful channel information after removing the estimated clutter. Simulation results are provided for the proposed solution and existing techniques, and validate the effectiveness of the proposed scheme.
- Published
- 2021