Anticipatory power cycling of mobile network equipment for high demand multimedia traffic

Abstract

The increasing energy demand of mobile phone networks is a problem for the environment and a cost factor for mobile network operators. Multimedia content is increasingly popular, demands high data rates, and increases energy usage of mobile phone networks. Thus it is important to reduce energy usage of mobile phone networks while serving multimedia content with a high quality of experience (QoE).We developed an approach to power-cycle base stations and control the playback of video streams while reducing the energy consumption and not decreasing the QoE for users. We present two implementations: an optimization problem and an iterative algorithm.Up to 80% of energy can be saved by combining power cycling and video stream controlling in low-load situations. With increasing load, the energy consumption also increases while the QoE can still be improved.

A survey on QoE-oriented wireless resources scheduling

“…Uplink (Essaili et al, 2011;Wu et al, 2012;Condoluci et al, 2017;Liu et al, 2018;Ranjan et al, 2018) Multi-cell (Zheng et al, 2014;Cho et al, 2015;Kim et al, 2015;Miller et al, 2015) Heterogeneous networks (Toseef et al, 2011;Jailton et al, 2013;Seyedebrahimi and Peng, 2015;Morel and Randriamasy, 2017;Abbas et al, 2017) Device-to-device communications (Zhu et al, 2015a;Hong et al, 2017;Biswash and Jayakody, 2018) Vehicular networks (Xu et al, 2013;Yaacoub et al, 2015;Ding et al, 2018) Cognitive radio networks (Jiang et al, 2012;Wu et al, 2013;He et al, 2016;Zhang et al, 2017;Piran et al, 2017;Lin et al, 2017) Relay networks (Reis et al, 2010;Wu et al, 2013;Bethanabhotla et al, 2016;Xiang et al, 2017;Fan and Zhao, 2018) Multi-user MIMO networks (Cao et al, 2012;Bethanabhotla et al, 2016;Chen et al, 2017;Huang and Zhang, 2018) Base stations energy consumption (Ma et al, 2012;Li et al, 2012;Gabale and Subramanian, 2014;Draxler et al, 2014;Sapountzis et al, 2014;…”

“…In (Gabale and Subramanian, 2014), an "energy-source" based method for delivering multimedia content asynchronously is presented, which adjusts the consignment of delay-tolerant content according to the time intervals where the renewable energy is available, i.e., it tries to balance the service provider energy costs and the QoE (namely the delay) experienced by users. An approach to power-cycle base stations and to control the playback of video streams is presented in (Draxler et al, 2014), where the goal is to reduce the overall energy consumed by the base stations while maintaining a high QoE for the users. A framework to reduce small cell energy consumption contingent on QoE restrictions has been proposed in (Sapountzis et al, 2014), where an analytical investigation is performed regarding the trade-off between switching off underloaded base stations and the performance degradation experienced by the users.…”

Role of large scale channel information on predictive resource allocation

“…As the smart phone popularizes, improving the performance of mobile networks such as energy efficiency (EE) by exploiting context information has drawn attentions [1][2][3][4][5].…”

Energy-saving resource allocation by exploiting the context information

“…The user level context information can be exploited for predicting transmission rate to assist resource allocation in future time. For example, assuming perfect rate prediction, the transmission time or total power was minimized to save power in [2,3], and a scheduling and antenna closing strategy was proposed to improve EE in [4]. In [2][3][4], the knowledge of the error-free rate prediction plays important role in power saving, and the base station (BS) is assumed only serving one kind of traffic (e.g., multimedia streaming).…”