Resource Allocation Using Deep Learning in Mobile Small Cell Networks

This work proposes a position-dependent deep learning (DL)-based algorithm that enables interference free resource allocation (RA) among mobile small cells (mScs). The proposed algorithm considers a vehicular environment comprising of city buses that generates historic data about the city buses positions. The position information of the moving buses is exploited to form interference free resource block (RB) allocation as data labels to the respective historic data. The long short-term memory (LSTM) algorithm is used for RA in mSc network based on position-dependent historic data. The numerical results obtained under non-dense and dense mSc network scenarios reveal that the proposed algorithm outperforms other machine learning (ML) and DL-based RA mechanisms. Moreover, the proposed RA algorithm shows improved results when compared to RA using Global Positioning System Dependent Interference Graph (GPS-DIG), but provides less data rates as compared to existing Time Interval Dependent Interference Graph (TIDIG)-based, and Threshold Percentage Dependent Interference Graph (TPDIG)-based RA while fulfilling the users' demands. The proposed scheme is computationally less expensive in comparison with TIDIG and TPDIG-based algorithms.