|Autors: Bandopadhaya, S., Samal, S.R., Poulkov, V. K.|
Title: Machine learning enabled performance prediction model for massive-MIMO HetNet system
Keywords: 5G; Area spectral density; B5G wireless networks; Coverage probability; HetNet; Machine learning; Massive MIMO
Abstract: To support upcoming novel applications, fifth generation (5G) and beyond 5G (B5G) wireless networks are being propelled to deploy an ultra-dense network with an ultra-high spectral efficiency using the combination of heterogeneous network (HetNet) solutions and massive Multiple Input Multiple Output (MIMO). As the deployment of massive MIMO HetNet systems involves a high capital expenditure, network service providers need a precise performance analysis before investment. The performance of such networks is limited because of presence of inter-cell and intertier interferences. The conventional analytic approach to model the performance of such networks is not trivial, as the performance is a stochastic function of many network parameters. This paper proposes a machine learning (ML) approach to predict the network performance of a massive MIMO HetNet system considering a multi-cell scenario. This paper considers a two-tier network in which the base stations of each tier are equipped ..
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