B5G/6G Service Planning Process for the Deployment of Smart Cities as a Model for Massive Urban Area Applications

Abstract

By the end of this decade, the optimal solution for Mobile Network Operators (MNO) must be proposed after a thorough evaluation of various incremental cost network deployment options. The deployments combining enhanced Wi-Fi standards, Beyond 5G (B5G), and upcoming 6G based Radio Access Technologies (RAT), all of which are thought to be a part of the Future or 6G-based Wireless & Mobile Heterogeneous Networks, are highlighted in this article with particular emphasis (6G-WMHN). So, mobile broadband subscriptions grow twice as fast as the fixed ones and the Internet of Things (IoT) comes forth, the Byeond 5G (B5G) and 6G vision of the Internet of Everything (IoE) (people, devices, and things), becomes a substantial and credible part of the near future. The goal of this paper is to shed light on B5G/6G network planning. In this paper, firstly, we give a brief overview of the key enabling technologies for B5G/6G network planning requirements including capacity estimation and reservation, and dynamic capacity allocation. In addition, the B5G/6G air interface, and its various propagation models which is suitable for low, mid, and high bands (mmW) spectrum. RF key parameters and planning parameters, traffic types, propagation modeling, and calculation methodology of system throughput and capacity. This paper is illustrating the estimation process for various operational scenario-based B5G/6G link budgets and smart cities and their traffic forecasting. Finally, we propose a planning tool that takes into consideration many practical aspects such as flexibility, high reliability to smart cities, and multi-operating frequency bands. The Model supports High-loss and Low-loss models, Massive MIMO with beamforming. The presented results cover different types of smart city configurations such as street canyons, business areas, downtown, and shopping areas. The planning crossing point (from coverage oriented to capacity-oriented and vice versa) is deducted iteratively by the presented model.