Multiobjective self‐optimization of the cellular architecture for green 5G networks

Abstract

Cellular architecture, energy efficiency, and self‐optimizing network are key features to meet the forecast requirements of the fifth‐generation networks. Frequently, the major optimization focus is on maximizing the data rate, but many other objectives are considered ahead to be optimized in the standardization of green fifth‐generation networks such as ubiquitous coverage, cost, availability, latency, automation, energy efficiency, load balancing, mobility, and awareness, which are often conflicting objectives. In this paper, we propose a multiobjective evolutionary framework using a real traffic profile and three‐dimensional beamforming to ensure green self‐optimization of the network cellular layouts. The framework is based on a genetic algorithm (GA) handling conflicting objectives: coverage, capacity, energy efficiency, load balancing, and mobility. The simulation results show a dynamic and automated design of the network layouts following the spatial and temporal fluctuation of the traffic. The sectors are switched‐on, shaped, and switched‐off only where and when required for both macro and virtual small cell layers.

Fog computing–based node‐to‐node communication and mobility management technique for 5G networks

“…Modern cellular networks have the most adaptive and supportive end‐users deployment as the long term evolution (LTE). The exponential growth in smartphone users (with Internet‐based applications) demands the high data rate, less bandwidth consumption, and minimum delay–based communication and they are proportional to high energy consumption . It motivates the researchers to introduce new cutting edge communication methodology, which should support the high data rate, least bandwidth utilization, minimum energy consumption, affordable and best end‐to‐end Quality of Experience (QoE) .…”