Adaptive Multi-state Millimeter Wave Cell Selection Scheme for 5G communication

Millimeter wave bands have been introduced as one of the most promising solutions to alleviate the spectrum secrecy in the upcoming future cellular technology (5G) due the enormous amount of raw bandwidth available in these bands. However, the inherent propagation characteristics of mmWave frequencies could impose new challenges i.e. higher path loss, atmospheric absorption, and rain attenuation which in turn increase the outage probability and hence, degrading the overall system performance. Therefore, in this paper, a novel flexible scheme is proposed namely Adaptive Multi-State MmWave Cell Selection (AMSMC-S) through adopting three classes of mmWave base stations, able to operate at various mmWave carrier frequencies (73, 38 and 28 GHz). Two mmWave cellular Grid-Based cell deployment scenarios have been implemented with two inter-site-distances 200 m and 300 m, corresponding to target area of (2.1 km2) and (2.2 km2). The maximum SINR value at the user equipment (UE) is taken in to consideration to enrich the mobile user experience. Numerical results show an improvement of overall system performance, where the outage probability reduced significantly to zero while maintaining an acceptable performance of the 5G systems with approximately more than 50% of the mobile stations with more than 1Gbps data rate.

An optimal multitier resource allocation of cloud RAN in 5G using machine learning

The networks are evolving drastically since last few years in order to meetuser requirements. For example, the 5G is offering most of the available spec-trum under one umbrella. In this work, we will address the resource allocationproblem in fifth-generation (5G) networks, to be exact in the Cloud Radio AccessNetworks (C-RANs). The radio access network mechanisms involve multiplenetwork topologies that are isolated based on the spectrum bands and it shouldbe enhanced with numerous access technology in the deployment of 5G net-work. The C-RAN is one of the optimal technique to combine all the availablespectral bands. However, existing C-RAN mechanisms lacks the intelligence per-spective on choosing the spectral bands. Thus, C-RAN mechanism requires anadvanced tool to identify network topology to allocate the network resources forsubstantial traffic volumes. Therefore, there is a need to propose a frameworkthat handles spectral resources based on user requirements and network behav-ior. In this work, we introduced a new C-RAN architecture modified as multitierHeterogeneous Cloud Radio Access Networks in a 5G environment. This archi-tecture handles spectral resources efficiently. Based on the simulation analysis,the proposed multitier H-CRAN architecture with improved control unit innetwork management perspective enables augmented granularity, end-to-endoptimization, and guaranteed quality of service by 15 percentages over theexisting system