Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> EPC Control Strategies for BS Energy Management and <s> The operation of cellular network infrastructure incurs significant electrical energy consumption. From the perspective of cellular network operators, reducing this consumption is not only a matter of showing environmental responsibility, but also of substantially reducing their operational expenditure. We discuss how dynamic operation of cellular base stations, in which redundant base stations are switched off during periods of low traffic such as at night, can provide significant energy savings. We quantitatively estimate these potential savings through a first-order analysis based on real cellular traffic traces and information regarding base station locations in a part of Manchester, United Kingdom. We also discuss a number of open issues pertinent to implementing such energy-efficient dynamic base station operation schemes, such as various approaches to ensure coverage, and interoperator coordination. <s> BIB001 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> EPC Control Strategies for BS Energy Management and <s> In this paper, we investigate dynamic base station (BS) switching to reduce energy consumption in wireless cellular networks. Specifically, we formulate a general energy minimization problem pertaining to BS switching that is known to be a difficult combinatorial problem and requires high computational complexity as well as large signaling overhead. We propose a practically implementable switching-on/off based energy saving (SWES) algorithm that can be operated in a distributed manner with low computational complexity. A key design principle of the proposed algorithm is to turn off a BS one by one that will minimally affect the network by using a newly introduced notion of network-impact, which takes into account the additional load increments brought to its neighboring BSs. In order to further reduce the signaling and implementation overhead over the air and backhaul, we propose three other heuristic versions of SWES that use the approximate values of network-impact as their decision metrics. We describe how the proposed algorithms can be implemented in practice at the protocol-level and also estimate the amount of energy savings through a first-order analysis in a simple setting. Extensive simulations demonstrate that the SWES algorithms can significantly reduce the total energy consumption, e.g., we estimate up to 50-80% potential savings based on a real traffic profile from a metropolitan urban area. <s> BIB002
Big Data Analytics. The power consumption in BSs can be minimized by considering strategies that allow tuning their energy consumption to the network load. For example, by adapting the transmission power in relation to the offered traffic demand [82, BIB002 . This can overcome the radio access network architecture limitations which is provisioned to handle maximum expected network load, yet for each BS the load fluctuates over time due to different usage patterns and mobility. To enable power transmission adaptation and load balancing across BSs, traffic load information (operator mobile traffic datasets) obtained from the EPC can be utilized to extract relevant demand patterns to design dynamic BS management mechanisms BIB001   . Optimization based on mobile traffic datasets from the operator, obtained from multiple network elements, will make it possible to minimize the amount of time it takes to steer traffic on a real time basis. These datasets hold a very promising potential when it comes to mobile network system analysis, as they can capture the traffic dynamics over time and space, at unmatched scales. Their use is expected to pave the way towards very efficient and self-adaptable network solutions.