Co-Channel Interference Management for Heterogeneous Networks Using Deep Learning Approach

TLDR

Co‑channel interference in public‑safety networks co‑existing with UAVs and LTE‑based railway networks requires careful study, as UAVs serve as mobile base stations for edge coverage while railway links demand high reliability and low latency, necessitating priority allocation of shared radio access channels to railway users. The study aims to employ deep‑learning–based enhanced and further enhanced ICIC strategies to mitigate interference from the public‑safety network to railway and UAV links. The authors analyze shared and non‑shared RACs, implement DL‑based coordinated multipoint scheduling alongside eICIC and FeICIC, and evaluate these methods in simulations against baseline ICIC and CoMP schemes. Offloading PSN mobile users to the railway or UAV base stations improves resource utilization, and DL‑based FeICIC combined with coordinated‑scheduling CoMP yields the best performance when shared RACs are used.

Abstract

The co-channel interference for mobile users (MUs) of a public safety network (PSN) in the co-existence of heterogeneous networks such as unmanned aerial vehicles (UAVs) and LTE-based railway networks (LRNs) needs a thorough investigation, where UAVs are deployed as mobile base stations (BSs) for cell-edge coverage enhancement. Moreover, the LRN is employed for the train, and its control signal demands high reliability and low latency. It is necessary to provide higher priority to LRN users when allocating resources from shared radio access channels (RACs). By considering both sharing and non-sharing of RACs, co-channel interference was analyzed in the downlink network of the PSN, UAV, and LRN. By offloading more PSN MUs to the LRN or UAVs, the resource utilization of the LRN and UAV BSs was enhanced. In this paper, we aimed to adopt deep-learning (DL)-based enhanced inter-cell interference coordination (eICIC) and further enhanced ICIC (FeICIC) strategies to deal with the interference from the PSN to the LRN and UAVs. Moreover, a DL-based coordinated multipoint (CoMP) for coordinated scheduling technique was utilized along with FeICIC and eICIC to enhance the performance of PSN MUs. In the simulation results, the performance of DL-based interference management was compared with simple eICI, FeICIC, and coordinated scheduling CoMP. The DL-based FeICIC and CoMP for coordinated scheduling performed best with shared RACs.