A variegated look at 5G in the wild

Improving UE Energy Efficiency Through Network-Aware Video Streaming Over 5G

“…A popular method to reduce energy consumption in mobile phones is to optimize the tail energy, which is achieved in [38] by either prefetching or delaying packets. The authors in [3] proposed an energy-saving mechanism that opportunistically switches the network interface from 5G to 4G when the 5G mmWave network condition worsens. Such vertical handovers incur increased signalling message exchanges, as well as increased energy consumption [8].…”

“…However, the 5G technology remains within the development phase, and, thus, the availability of commercially deployed 5G networks is limited, particularly in the middle and low-economy countries. Although some existing works present real network 5G mmWave datasets [3], [11], [12], these have been collected using one or two smartphones, i.e., users. As the user experienced datarate is a function of the network load, i.e., the number of users associated with a BS, hence, it is significant to test the efficacy of any new algorithm for different network loads.…”

“…Consequently, the player is bound to initiate a download request even if the underlying network's signal quality is poor, thereby resulting in higher energy consumption at the User Equipments (UEs). A recent measurement study on 5G device energy consumption [3] shows that services such as ABR video streaming are more susceptible to increased energy usage in 5G mmWave networks. It is, therefore, imperative to design energy-efficient video streaming algorithms for 5G mmWave networks [4]- [6], especially to cater to the ubiquitous QoE demand of vehicular UEs.…”

“…These parameters are drastically different in a typical 5G network. In addition, 5G being a highly directional communication technology due to its high frequency of operation, other factors such as the number of antennas, the direction of antenna beams, etc., also impact the received throughput [3]. This inevitably implies that the throughput prediction model will need a complete redesigning.…”

“…Transfer learning is used to compensate for the limited availability of the 5G network throughput traces. For this, we first train a recurrent neural network with real-world 5G datasets [3], [11], [12] and then retrain the learned model with data collected from extensive simulations of a 5G mmWave network. The simulations help us capture every little nuance associated with the functioning of the 5G mmWave network while also allowing us to vary the number of active users in the network and observe the corresponding performance over the video streaming application.…”