Eunsung Oh

Toward dynamic energy-efficient operation of cellular network infrastructure

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.

Dynamic Base Station Switching-On/Off Strategies for Green Cellular Networks

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.

Energy Savings through Dynamic Base Station Switching in Cellular Wireless Access Networks

Reducing the energy consumption of cellular wireless access networks is not only beneficial for the global environment but also makes commercial sense for telecommunication operators. Since access networks are designed to support peak time traffic, the utilization of base stations can be very inefficient during off-peak time because the traffic profile is time varying. We study the dynamic switching of base stations (BS) to reduce the energy consumption considering the time varying characteristic of the traffic profile. We show via analysis that the mean and variance of traffic profile and the BS density are the dominant factors that determine the amount of energy saving that can be achieved. Simulations using ideal and real traffic profiles are used to quantify the potential savings from dynamic BS switching in a realistic setting.

Energy-aware hierarchical cell configuration: From deployment to operation

This paper develops an energy-aware hierarchical cell configuration framework that encompasses both deployment and operation in downlink cellular networks. Specifically, we first formulate a general problem pertaining to total energy consumption minimization while satisfying the requirement of area spectral efficiency (ASE), and then decompose it into deployment problem at peak time and operation problem at off-peak time. For the deployment problem, we start from an observation about various topologies including the real deployment of BSs that there is a strong correlation between the area covered by an additional micro BS and the increment of ASE. Under such an assumption, we prove the submodularity of ASE function with respect to micro BS deployment and propose a greedy algorithm that is shown to be a constant-factor approximation of optimal deployment. Although the greedy algorithm can be also applied as an offline centralized solution for the operation problem, we further propose online distributed algorithms with low complexity and signaling overhead to have more practical solutions. Extensive simulations based on the acquired real BS topologies and traffic profiles show that the proposed algorithms can significantly reduce the energy consumption.

Energy-efficient design of heterogeneous cellular networks from deployment to operation

The ever-increasing traffic demand has motivated mobile operators to explore how they can boost their network capacity with a minimal increase in their capital and operating expenditures. In order to tackle this problem, we investigate the energy-efficient design of heterogeneous cellular network (or simply HetNet), especially with a focus on deployment and operation strategies. We first formulate a general problem pertaining to minimizing the total energy consumption cost while satisfying the requirement of area spectral efficiency (ASE). We decompose this problem into a deployment problem at peak time and an operation problem at off-peak time. Under practical assumptions made from an observation on various topologies including an acquired real base-station deployment dataset, we demonstrate the submodularity of ASE function with respect to micro base-station deployment. Subsequently, we propose a greedy algorithm that is shown to be a constant-factor approximation to the optimal deployment. Although the greedy algorithm can be applied as an offline centralized solution for the operation problem, we further propose two online distributed algorithms with low complexity and signaling overhead using Lagrangian relaxation technique. Extensive simulations show that the proposed algorithms can significantly reduce the energy consumption with minimal deployment of micro base-stations.

Performance analysis of dynamic channel allocation based on the greedy approach for orthogonal frequency-division multiple access downlink systems

This paper presents a performance analysis of dynamic channel allocation (DCA) based on the greedy approach (GA) for orthogonal frequency-division multiple access downlink systems over Rayleigh fading channels. The GA-based DCA achieves its performance improvement using multiuser diversity. We analyze the statistics of the number of allocable users that represents the multiuser diversity order at each allocation process. The derived statistics are then used to analyze the performance of GA-based DCA. The analysis results show that the number of subcarriers allocated to each user must be equal to achieve the maximum system performance based on outage probability and data throughput. Copyright

Impact of Demand and Price Uncertainties on Customer-side Energy Storage System Operation with Peak Load Limitation

Abstract This article investigates customer-side energy storage system operations to minimize the electricity bill under a peak load limitation constraint and uncertain environments. Specifically, it is discussed how the demand and price uncertainties impact the system performance. It is shown that the energy storage system operation based on the Markov decision process with stochastic information has near-optimum performance, which is achieved by an iterative method with perfect information when the electricity price and demand are slightly varied. To address a problem, such as the failure of peak load reduction due to high uncertainties, two heuristic methodologies are suggested by modifying the peak load threshold and the charge/discharge reservation quantity. It is demonstrated that the proposed approach can effectively manage the uncertainties with marginal performance degradation.

Group Building based Power Consumption Scheduling for the Electricity Cost Minimization with Peak Load Reduction

In this paper, we investigate a group building based power consumption scheduling to minimize the electricity cost. We consider the demand shift to reduce the peak load and suggest the compensation function reflecting the relationship between the change of the building demand and the occupants’ comfort. Using that, the electricity cost minimization problem satisfied the convexity is formulated, and the optimal power consumption scheduling algorithm is proposed based on the iterative method. Extensive simulations show that the proposed algorithm achieves the group management gain compared to the individual building operation by increasing the degree of freedom for the operation.

A Unified Base Station Switching Framework Considering Both Uplink and Downlink Traffic

This letter studies the dynamic operation of macro base stations (BSs) for potential energy savings. Unlike existing work in literature, most of which overlooked the importance of uplink (UL) traffic, we develop a generalized framework for a dynamic BS operation that not only considers the downlink (DL) traffic load but also the UL traffic load. The proposed BS switching sequential algorithm, which is built based on the generalized framework, is computationally efficient and easy to implement. Simulation results demonstrate that jointly handling UL and DL can provide more energy savings.

A framework for consumer electronics as a service (CEaaS): a case of clustered energy storage systems

With advanced technology such as the Internet of Things, traditional consumer electronics are progressing not only toward personal devices but also sharable appliances that provide more coordinated management and intelligence. Under these conditions, the concept of consumer electronics as a service (CEaaS) has been introduced. In this paper, a framework for CEaaS is described. Specifically, an architecture and operational technology are suggested for a clustered energy storage system (ESS) that is shared among multiple households as a case of CEaaS. In multi-dwelling units that are common in urban areas, a clustered ESS installed in a shared space is considered because it is difficult to have sufficient space to install and operate an ESS at home. To operate a clustered ESS that is shared and virtually integrated among individual houses, the proposed architecture is designed in two phases: benefit maximization for all households, and benefit rebalancing for each household. Simulations show that clustered ESS operation based on the proposed architecture outperforms a conventional individual ESS operation in various conditions considering the characteristics of the household and appliances.