Robust User Identification in Massive MIMO Systems Using Compressed Sensing Techniques

In 5G and beyond 5G wireless communication networks, massive multiple‐input multiple‐output (MIMO) technologies are essential for improved energy and spectrum efficiency. These systems are very effective at channel estimation and active user detection in situations with many users, and only a small percentage of them send data during a given time frame. Finding an active user is the biggest issue because it directly affects system performance. Sparsity in the user’s activity pattern can be exploited using compressed sensing (CS) techniques. OMP with iterative refinement (OMP‐IR) and OMP enhanced (OMPe), two variants of the classic orthogonal matching pursuit (OMP) algorithm, are the main subjects of this study for CS. Adding chaotic sequences increases accuracy and robustness, and the impact of noise and interference is successfully reduced, leading to good accuracy in active user detection (AUD). This paper shares modeling details, which show how angular sparsity has been exploited for AUD. Comparative visualization of different improved versions of OMP is performed. Simulations show that the enhanced version of OMP outperforms improved versions in AUD precision, specifically at low SNR, making it more reliable for AUD in massive MIMO systems and also optimizes resource allocation in a dense user environment.