Conakry hybrid energy interference 5G base station

Can reinforcement learning optimize energy consumption in 5G heterogeneous networks?

Ali El Amine et al. have proposed a reinforcement learning-based approach to optimize energy consumption in 5G Heterogeneous Networks (HetNets) by dynamically adjusting small base station (SBS) sleep modes.

Can IoT collaborative control reduce energy consumption in 5G base stations?

Kuo-Chi Chang et al. have proposed an energy-saving technology for 5G base stations using Internet of Things (IoT) collaborative control. It addresses the issue of high energy consumption in dense 5G networks, particularly during periods of low traffic.

How to evaluate a 5G energy-optimised network?

To properly examine an energy-optimised network, it is very crucial to select the most suitable EE metric for 5G networks. EE is the ratio of transmitted bits for every joule of energy expended. Therefore, while measuring it, different perspectives need to be considered such as from the network or user’s point of view.

What are the factors affecting a 5G network?

Some of the prominent factors are such as traffic model, SE, topological distribution, SINR, QoS and latency. To properly examine an energy-optimised network, it is very crucial to select the most suitable EE metric for 5G networks. EE is the ratio of transmitted bits for every joule of energy expended.