Interference Mitigation for Millimeter Wave Communications in 5G Networks Using Enhanced Q-Learning (Journal of Engineering, 2025)
Since the coverage of millimeter waves (mmWave) is limited due to high path loss and blockage, it is deployed in small cells. This dense deployment of base stations and access points resulted in significant interference. Therefore, interference mitigation is the main challenge in designing the new millimeter-wave communication technologies in the existing 5G system. Therefore, in this paper, a two-tier Heterogeneous Cloud Radio Access Network model is presented, which performs a technique inspired by soft frequency reuse (SFR) to mitigate interference. The cellular service region is divided into two sub-regions, center region is served by conventional macro base stations, which operate in the sub-6 GHz frequency band, while the edge area is served by Remote Radio Heads (RRHs), which operate in the millimeter-wave frequency band to avoid interference between tiers. User-RRH associations are introduced to mitigate interference between small RRHs and maximize network throughput using an Online Multi-Agent Q-Learning (MAQL). The proposed MAQL solution, based on the least path loss as a basic criterion for User-RRH association, outperforms in average network throughput per user a previous study based on average SINR as a basic criterion for association for two types of RRHs deployment scenarios in the heterogeneous network approximately by 66.4% and 21%, respectively, at the lowest number of users. The difference gradually decreases with the increasing user numbers until it reaches 8.7% and 9.8%, respectively. Even though the gap between throughput performance narrows as user density increases, the proposed method consistently outperforms the alternative strategy, indicating its ability to adapt and manage network resources more effectively even under higher traffic loads.