Open trends on TCP performance over urban 5G mmWave networks

Abstract

The 5G (fifth-generation) mobile networks, especially by exploiting higher bandwidth in the mmWave (millimeter wave) spectrum, is the leading candidate to be used as the coming generation for ubiquitous networks. The vast available bandwidth in mmWave can satisfy the high data rate and low latency expectations from 5G networks in order to provide new services and use cases. Although 5G mmWave networks come up with innovative and robust services, they suffer from a drawback. As the frequency rises, the penetration power and coverage area of the network decreases, so it results in having discontinuous communication between a base station and a user. This intermittent characteristic is caused due to an existing obstacle such as a car or a building on the communication path that can hurdle the establishment of a transmission, which is called NLoS (Non-Line of Sight) state. NLoS states can degrade the functionality of the network and prevent from having seamless connectivity by forcing fluctuations in the network's channels. The reason for this shortcoming is because of the susceptibility of high frequencies to the blockage that can be generated by obstacles. The intense negative effect of having a blockage in the network is on an end-to-end communication when other layers protocols such as the transport layer widely used protocol TCP (Transmission Control Protocol) are used. Having frequent disconnections in the network impairs the TCP's functionality with inducing congestion states and preventing it from achieving higher performance. In this paper, we present the performance evaluation and analysis of TCP in different situations in an urban area and find out how various conditions can affect the performance of the protocol. The simulation results indicate that conventional TCPs are not adequate enough to be exploited in 5G mmWave networks. For having them functioning in their full potential, some modifications should be made in order to adapt them to 5G mmWave networks. Some ML (Machine Learning) techniques such as Neural Networks and Reinforcement Learning can be deployed as the key enablers to network performance improvement.