Wavelet NOMA for cell-free massive MIMO: mitigating intra-cluster and inter-cluster interference in future wireless communication systems

As communication systems evolve in a Cell-Free (CF) environment with the utilization of massive Multiple Input Multiple Output (mMIMO) techniques for the fifth generation and beyond, and a multitude of users access the communication networks through multiple devices and applications, it is imperative to ensure seamless connectivity with better quality of service for all the users. The rapidly increasing number of users is distributed in clusters in a CF-mMIMO communication system to efficiently manage and allocate resources. However, this increases the complexity of managing clusters, resulting in greater intra and inter-cluster interference. Therefore, an integrated solution based on wavelet transform-shaped Non-Orthogonal Multiple Access (NOMA) scheme in a CF-mMIMO system is proposed in this article for reduced intra and inter-cluster interference and to support a greater number of users. Moreover, a user-centric approach, leveraging machine learning (ML) algorithms is adopted for efficient user clustering, and closed-form expressions for intra and inter-cluster interference are derived. The system's performance is evaluated considering the key performance indicators (KPIs), such as achievable sum-rate, bit error rate (BER), and ergodic sum-rate. Our results demonstrate significant improvement for these KPIs, compared to the conventional NOMA scheme within a CF-mMIMO framework.