The rapid proliferation of next-generation wireless systems, such as 5G and beyond, presents unprecedented challenges in managing interference generated by dense heterogeneous deployments, spectrum sharing, and mobile user dynamics. Conventional static or semi-dynamic interference mitigation strategies cannot address real-time network dynamics whose characteristics fluctuate over time of the network. This shortcoming results in suboptimal performance and a degradation of quality of service (quality of service) for the user. This paper presents a comprehensive framework for real-time interference management through intelligent algorithms, utilizing a combination of machine learning-based predictive models, coordinated multipoint (CoMP) transmission, and dynamic spectrum access procedures. The proposed framework utilizes innovative methods to explore and maximize the potential of real-time data analytics and adaptively beamform multiple transceivers to identify and potentially mitigate sources of interference. The simulation results showed improvements in signal-to-interference-plus-noise ratio (SINR), spectral efficiency, and lower latencies compared to conventional interference solutions. The proposed framework also emphasizes cross-layer optimization and the incorporation of edge intelligence to facilitate low latency and scalability. The figure presented in the paper also represents significant advances in research on interference resiliency frameworks, which are essential for the dependability and efficiency of wireless networks in the future.
Abidi et al. (Mon,) studied this question.