Interference Mitigation in mmWave Heterogeneous Cloud-Radio Access Network: for Better Performance and User Connectivity

Abstract

The rapid advancements in wireless communications have prompted a surge in mobile data traffic, necessitating innovative solutions for 5G and beyond. This paper introduces a two-tier Heterogeneous Cloud Radio Access Network (HC-RAN) model leveraging millimeter Wave (mmWave) and sub-6 GHz frequencies to address this need. It integrates User-RRH associations to mitigate interference, enhance network throughput (via Heuristic Algorithm) and RRH-BBU clustering (via k-means) to manage resources in the network. The study evaluates SINR and rate coverage probabilities across various deployment scenarios, including Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions, as well as random and edge-based deployments. Results demonstrate that strategic placement of Remote Radio Heads (RRHs) and efficient clustering significantly improve network efficiency and user connectivity. In LOS conditions, random RRH deployments deliver superior coverage and throughput due to spatial diversity and reduced path loss. Conversely, edge-based deployments necessitate more resources to handle traffic demands but can excel in controlled scenarios. The proposed joint User-RRH association with RRH-BBU k-means clustering algorithm effectively manages interference, also maintains a balance between quality of service and efficient resource management. The proposed User-RRH association sub problem scheme that based on minimum path loss as a basic criterion outperforms on Limited Capacity User-RRH Association scheme (LC UA) in both the random and edge deployment scenarios and yield increasing in average throughput by approximately 38% and 27%, respectively. In other hand, the adaptive solution of RRH-BBU k-means clustering sub problem depend on actual load and number of active RRHs in the network to find the number of k RRH-BBU clusters, which manage resource consumption. This highlights the challenges in resource allocation and management with and without clustering. This paper concludes that optimized cell site deployment combined with association and clustering algorithms can significantly enhance 5G network performance, particularly in dense urban environments. These insights help network operators balance high service quality with efficient resource utilization.