Optimization of 3D Spectrum Management in Future Wireless Networks

In this paper, we propose an application customized network slicing mechanism to cope with dynamic traffic in future networks and various service requirements for network users, namely, context-aware network slicing (CANS). The resource allocation is carried out in frequency (f), space (s) and time (t) domains (referred to as three domains (3D) resource allocation) while the customization targets at low latency and high reliability, characteristic for IoT applications. We first formulate CANS as a two-step matching game with unknown preferences for access and back-haul networks. To dynamically resize mobile users' network slices (NSs), we propose application customized CANS++ method based on a coalition game to minimize the hand-off risk in the network. We then develop two-step stable matching (TSSM) and $\epsilon$ -Pareto stability techniques to find the solutions to CANS and CANS++, respectively. To improve energy and cost efficiency, an artificial intelligence-based Dynamic Network Architecture (DNA) with ON/OFF scheduling (AI-DOS) is proposed to dynamically maximize NS utilization. Finally, we carry out extensive numerical analysis and demonstrate that context-aware 3D network slicing and resizing schemes can improve at least three times the users' utilities. These utilities are enhanced up to 180% by minimizing their hand-off risk. Furthermore, NS shrinking technique can reduce the number of active nodes (controlled in the s-domain) by factor five while the average utility of macro base stations, controlled in the t-domain, is improved between 70% and 500%.