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Learning based Network Adaptation in 5G NR

In this paper, we propose a novel terrain sensing algorithm which helps the user traversing across various terrains to adapt its connectivity to the network. For example, this algorithm enables dynamic switching between Sub-6 GHz (FR1) and mmWave (FR2) in 5G NR Networks. Users in dense urban areas and hot-spots have higher data rate demands and hence Small cell deployments in mmWave bands are preferred, while users traversing in sub-urban, rural or sparse networks need good connectivity and long-reach where Macro cell deployments in sub-6 GHz fit well. In this context, a multi-RAT equipped user, capable of sensing the terrain, can dynamically adapt its connectivity to the best-fit network. The proposed Learning-based Terrain Sensing Algorithm (LTSA) generates periodic recommendation, which are used to dynamically adapt to the network. The dynamic adaptation also enables automatic vehicle-to-vehicle connectivity on highways or urban grids based on traffic density. We show the benefits of the algorithm via simulations where the average network throughput significantly improves when LTSA is enabled.