Machine Intelligence Technique for Blockage Effects in Next-Generation Heterogeneous Networks
S. Amalorpava Mary Rajee, A. Merline · Radioengineering · 2020
Millimeter wave (mmWave) links such as 28 GHz and 60 GHz propose high data rates and capacity needed in 5G Heterogeneous network (Hetnet) real-time system.The key factors in network planning of Hetnet are the locations and links of base stations, and their coverage, transmitted power, antenna angle, orientation etc.However, large-scale blockages like static buildings, human etc. affect the performance of urban Hetnets especially at mmWave frequencies.A mathematical framework to model dynamic blockages is adapted and their impact on cellular network performance is analyzed.A machine learning approach based on Q-learning with Epsilon-Greedy algorithm is proposed to solve the blockage problem in such complex networks.The proposed results are evident and show the positive effect of increasing the base station density linearly with the blockage density to maintain the network connectivity.The performance of the proposed Epsilon-Greedy algorithm is compared with Epsilon-Soft algorithm.The performances of above said mmWave links are compared in terms of their coverage probability and throughput.The results show that an Epsilon-Greedy algorithm outperforms an Epsilon-Soft algorithm.