Performance Assessment and Comparison of Deployment Options for 5G Millimeter Wave Systems

Abstract

The roll-outs of fifth-generation (5G) New Radio (NR) systems operating in the millimeter-wave (mmWave) frequency band are essential for satisfying IMT-2020 requirements set forth by ITU-R in terms of the data rate at the access interface. To overcome mmWave-specific propagation phenomena, a number of radio access network densification options have been proposed, including a conventional base station (BS) as well as integrated access and backhaul (IAB) with terrestrial and aerial IAB nodes. The aim of this paper is to qualitatively and quantitatively compare the proposed deployments using coverage, spectral efficiency and BS density as the main metrics of interest. To this end, we develop a model capturing the specifics of various deployment options. Our numerical results demonstrate that, while the implementation of terrestrial relaying nodes potentially improves coverage and spectral efficiency, aerial relays provide the highest coverage, three times that of a direct link connection, and also significantly reduce the required BS density. The main benefit is provided by the link between the BS and the aerial relay. However, gains are highly dependent on a number of elements in antenna arrays and targeted outage probability. The use of terrestrial relays can be considered a natural trade-off between coverage and the aggregate rate.

Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models

“…The number of connected devices is increasing, resulting in a dramatic growth in traffic volume, causing anomalies such as network congestion, decreased quality of service, network delays, data loss, and blocking of new connections [6]. The network architecture should adapt to the volumes of traffic generated by various applications and use it for decision-making, taking into account several types of traffic with different service and priority requirements [7][8][9]. Artificial intelligence (AI) and machine learning (ML) are now trends for 5G networks that could provide more efficient and reasonable network planning and management [10].…”