Compressive Sensing-Based Multiuser Detection via Iterative Reweighed Approach in M2M Communications

Massive machine-to-machine (M2M) is an important application for Internet of Things in 5G. In this letter, we focus on solving the multiuser detection problem supported by low-activity code division multiple access for M2M communications. To address the user activity factor unknown issue in the optimal maximum a posterior probability and improve the signal reconstruction ability, we propose iterative reweighed and minimum mean-square-error iterative reweighed algorithms based on compressive sensing theory. The simulation results demonstrate that the proposed algorithms achieve substantial performance gain over traditional detectors.