Active User Detection in Massive MIMO Systems Using Compressed Sensing

In 5G and Beyond 5G wireless communication networks, Massive Multiple-Input Multiple-Output (MIMO) technologies are essential for improved energy and spectrum efficiency. These kinds of systems are very effective at channel estimation and active user detection (AUD) in situations where there are many users and only a small percentage of them are sending data during a given time frame. Finding an active user is the biggest issue in this situation because it will have a direct effect on system performance. Sparsity in the user's activity pattern can be exploited through the use of Compressed Sensing (CS) techniques. Orthogonal Matching Pursuit with Iterative Refinement (OMP-IR) and Enhanced OMP (OMPe), two variants of the classic OMP algorithm, are the main subject of this study for compressed Sensing. By adding chaotic sequences, accuracy and robustness are increased and also the impact of noise and interreference is successfully reduced, leading to good accuracy in AUD. Details of Modeling has been shared in this paper which shows how angular sparsity has been exploited for AUD. Comparative visualization of different flavors of OMP is performed. Simulations show that enhanced version of OMP outperforms other flavor in AUD precision, which makes it more reliable for AUD in Massive MIMO systems. Which ultimately optimize resource allocation in high density user environment.