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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Information

سال: 2023

ISSN: ['2078-2489']

DOI: https://doi.org/10.3390/info14020139