Bandopadhaya Shuvabrata, Samal Soumya Ranjan, Poulkov Vladimir
School of Engineering & Technology, BML Munjal University, Gurugram 122414, India.
Faculty of Telecommunications, Technical University of Sofia, 1756 Sofia, Bulgaria.
Sensors (Basel). 2021 Jan 26;21(3):800. doi: 10.3390/s21030800.
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 inter-tier 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 with massive MIMO systems working in a sub 6GHz band. The coverage probability (CP) and area spectral efficiency (ASE) are considered to be the network performance metrics that quantify the reliability and achievable rate in the network, respectively. Here, an ML model is inferred to predict the numerical values of the performance metrics for an arbitrary network configuration. In the process of practical deployments of future networks, the use of this model could be very valuable.
为了支持即将出现的新型应用，第五代（5G）及5G之后的（B5G）无线网络正被推动使用异构网络（HetNet）解决方案和大规模多输入多输出（MIMO）的组合来部署具有超高频谱效率的超密集网络。由于大规模MIMO HetNet系统的部署涉及高额资本支出，网络服务提供商在投资前需要进行精确的性能分析。由于存在小区间和层间干扰，此类网络的性能受到限制。对这类网络的性能进行建模的传统分析方法并不简单，因为性能是许多网络参数的随机函数。本文提出一种机器学习（ML）方法，用于预测考虑多小区场景的大规模MIMO HetNet系统的网络性能。本文考虑一个两层网络，其中每层的基站都配备在低于6GHz频段工作的大规模MIMO系统。覆盖概率（CP）和区域频谱效率（ASE）分别被视为量化网络中可靠性和可实现速率的网络性能指标。在此，推断出一个ML模型来预测任意网络配置下性能指标的数值。在未来网络的实际部署过程中，使用该模型可能会非常有价值。
