Energy Consumption In Cellular Networks Research Articles

Towards efficient device-to-device multicast content delivery with social awareness

Frequent content retrieval leads to significant energy consumption in cellular networks. Thereby, device-to-device (D2D) communication is used for proximal content delivery. Be aware that social friends may have common content interests, D2D content delivery is further improved by considering social network. However, how to implement efficient D2D multicast with the aid of social relationship needs further consideration. In this paper, with a proposed architecture on combined networks, the cooperation range of content sharing is investigated for D2D multicast to minimize the average energy consumption of serving a content request. Based on combined networks, a cooperation group formation scheme is proposed. Simulation results verify our analyzes and the advantage of proposed scheme.

Deep Reinforcement Learning With Spatio-Temporal Traffic Forecasting for Data-Driven Base Station Sleep Control

To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and capacity. However, as the high density of BSs is designed to accommodate peak traffic, it would consume an unnecessarily large amount of energy if BSs are on during off-peak time. To save the energy consumption of cellular networks, an effective way is to deactivate some idle base stations that do not serve any traffic demand. In this paper, we develop a traffic-aware dynamic BS sleep control framework, named DeepBSC, which presents a novel data-driven learning approach to determine the BS active/sleep modes while meeting lower energy consumption and satisfactory Quality of Service (QoS) requirements. Specifically, the traffic demands are predicted by the proposed GS-STN model, which leverages the geographical and semantic spatial-temporal correlations of mobile traffic. With accurate mobile traffic forecasting, the BS sleep control problem is cast as a Markov Decision Process that is solved by Actor-Critic reinforcement learning methods. To reduce the variance of cost estimation in the dynamic environment, we propose a benchmark transformation method that provides robust performance indicator for policy update. To expedite the training process, we adopt a Deep Deterministic Policy Gradient (DDPG) approach, together with an explorer network, which can strengthen the exploration further. Extensive experiments with a real-world dataset corroborate that our proposed framework significantly outperforms the existing methods.

Energy-efficient user association mechanism enabling fully hybrid spectrum sharing among multiple 5G cellular operators

Spectrum sharing (SS) is a promising solution to enhance spectrum utilization in future cellular systems. Reducing the energy consumption in cellular networks has recently earned tremendous attention from diverse stakeholders (i.e., vendors, mobile network operators (MNOs), and government) to decrease the CO2 emissions and thus introducing an environment-friendly wireless communication. Therefore, in this paper, joint energy-efficient user association (UA) mechanism and fully hybrid spectrum sharing (EE-FHSS) approach is proposed considering the quality of experience QoE (i.e., data rate) as the main constraint. In this approach, the spectrum available in the high and low frequencies (28 and 73 GHz) is sliced into three portions (licensed, semi-shared, and fully-shared) aims to serve the users (UEs) that belong to four operators in an integrated and hybrid manner. The performance of the proposed QoE-Based EE UA-FHSS is compared with the well-known maximum signal-to-interference-plus-noise ratio (max-SINR UA-FHSS). Numerical results show that remarkable enhancement in terms of EE for the four participating operators can be achieved while maintaining a high degree of QoE to the UEs.

Transfer restless multi-armed bandit policy for energy-efficient heterogeneous cellular network

This paper proposes a learning policy to improve the energy efficiency (EE) of heterogeneous cellular networks. The combination of active and inactive base stations (BS) that allows for maximizing EE is identified as a combinatorial learning problem and requires high computational complexity as well as a large signaling overhead. This paper aims at presenting a learning policy that dynamically switches a BS to ON or OFF status in order to follow the traffic load variation during the day. The network traffic load is represented as a Markov decision process, and we propose a modified upper confidence bound algorithm based on restless Markov multi-armed bandit framework for the BS switching operation. Moreover, to cope with initial reward loss and to speed up the convergence of the learning algorithm, the transfer learning concept is adapted to our algorithm in order to benefit from the transferred knowledge observed in historical periods from the same region. Based on our previous work, a convergence theorem is provided for the proposed policy. Extensive simulations demonstrate that the proposed algorithms follow the traffic load variation during the day and contribute to a performance jump-start in EE improvement under various practical traffic load profiles. It also demonstrates that proposed schemes can significantly reduce the total energy consumption of cellular network, e.g., up to 70% potential energy savings based on a real traffic profile.

User re-association and resource/power re-allocation of cell switched-off User Equipments in green cellular networks

Energy consumption of cellular networks has gained significant attention in recent years as green cellular networking paradigm appeared. Base Station (BS) switch-off is a solution for minimizing energy consumption of green cellular networks. However, User Equipments (UEs) of switched-off BSs lose their connectivity to the network and should be re-associated with BSs, attain new radio resources, and re-adjust their transmission powers. Previous studies have either not addressed this issue or have considered the joint problem of BS switch-off and BS association/resource allocation, while those joint problems are not spectrum efficient. In this paper, we formulate two different optimization problems for spectrum efficient re-association of involved UEs and re-allocation of their uplink radio/power resources after an energy-efficient BS switch-off. The first optimization problem aims at maximizing the achievable rate of cell switched-off UEs, while the second problem intends to maximize the achievable rate of all UEs of the network when a cell switch-off occurs. In formulated problems, the required rate of UEs is regarded as the QoS constraint. Decoupling the problems into sub-problems, we propose a distributed solution for user re-association, channel re-allocation, and power adjustment of UEs. Simulation results show superior performance of the proposed methods compared to the baseline methods in terms of the network capacity and QoS guarantee.

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

The increase of the technology related to radio localization and the exponential rise in the data traffic demanded requires a large number of base stations to be installed. This increase in the base stations density also causes a sharp rise in energy consumption of cellular networks. Consequently, energy saving and cost reduction is a significant factor for network operators in the development of future localization networks. In this paper, a localization method based on ray-tracing and fingerprinting techniques is presented. Simulation tools based on high frequencies are used to characterize the channel propagation and to obtain the ray-tracing data. Moreover, the fingerprinting technique requires a costly initial learning phase for cell fingerprint generation (radio-map). To estimate the localization of mobile stations, this paper compares power levels and delay between rays as cost function with different distance metrics. The experimental results show that greater accuracy can be obtained in the location process using the delay between rays as a cost function and the Mahalanobis distance as a metric instead of traditional methods based on power levels and the Euclidean distance. The proposed method appears well suited for localization systems applied to indoor and outdoor scenarios and avoids large and costly measurement campaigns.

Queueing decision model for throughput maximization in green communications networks

The various advancement in cellular technology has drawn huge attention to green communication by the stakeholders. Base station (BS) sleeping technique has been proposed as a way to reduce energy consumption in cellular networks, however, this may result in service delay for cellular users. In this article, we propose a queueing decision model for BS sleeping technique that maximizes energy-efficient utilization of the BSs in a green communication network while reducing the mean service waiting time for cellular users. The proposed model also ensures an optimal and interference-free resource sharing scheme that effectively enables channel borrowing through BS association in the network, thereby reducing call drop rate and further improving quality of service in the network. Numerical simulations are performed to observe the impact of the proposed model on spectrum access and consequently on the throughput of the green communication network. The article also observed the possible improvement in network delay with the proposed model. Results show that the queueing decision model is more energy-efficient. The model also provides a more spectrum-efficient sharing scheme compared to existing models, thereby enabling better network throughput.

Hybrid power supply solutions for off-grid green wireless networks

ABSTRACTIncreasing deployment of cellular networks across the globe is pushing the energy consumption in cellular networks at an exceptional rate. The integration of renewable energy (RE) harvesting technology into future mobile networks has the potential to positively cope with environmental contamination and ensure self-energy sustainability as a means to decrease fossil fuel consumption. Diesel generator (DG) in conjunction with on-site RE harvester has emerged as an economic and extent efficient option where commercial grid supply is not viable. This paper is focused on the cost aware energy management framework addressing to least net present cost (NPC) for the envisioned hybrid powered green cellular base stations (BSs) considering tempo-spatial traffic dynamics. In such wireless networks, solar photovoltaic modules are considered as a primary energy source, while the DG and energy storage device are kept as the standby supply in case of inadequate solar energy to ensure zero outage. A comprehensive simulation-based investigation is carried out in the context of downlink Long-Term Evolution (LTE) cellular networks for evaluating cost-efficiency and reliability performance under a wide range of network settings. Particularly, this paper examines the energy yield, greenhouse gas emissions, and cost analysis based on the optimal architecture of Remote Radio Head-enabled LTE BS. Moreover, wireless network performance in terms of throughput, energy efficiency gain, and radio efficiency is thoroughly investigated using Monte Carlo simulations. Numerical results demonstrate a substantial reduction of carbon footprints with minimum NPC while satisfying the quality of service requirements.

Optimal matching between energy saving and traffic load for mobile multimedia communication

SummaryOptimizing cell locations of cellular networks is one of the most fundamental problems of network design. However, in order to meet a growing appetite for mobile data services, a large number of base stations are being deployed, which leads to tremendous energy consumption in cellular networks. This augmentation increases not only the system's capital and operational expenditure (CAPEX/OPEX) for mobile operators but also CO2 emissions. Besides the issue of meeting overwhelming traffic demands, network operators around the world now realize the importance of managing their cellular networks in an energy‐efficient manner. In this paper, we develop a self‐organizing framework for energy saving in orthogonal frequency‐division multiple‐access–based cellular access networks. We consider three different objectives, namely, coverage maximization, overlap minimization, and power consumption minimization, which is different from all existing works on energy saving in cellular networks.

Sector and Site Switch-Off Regular Patterns for Energy Saving in Cellular Networks

Cell switch-off (CSO) is an important approach to reducing energy consumption in cellular networks during off-peak periods. CSO addresses the research question of which cells to switch off when. Whereas online CSO, based on immediate user demands and channel states, is problematic to implement and difficult to model, off-line CSO is more practical and tractable. Furthermore, it is known that regular cell layouts generally provide the best coverage and spectral efficiency, which leads us to prefer regular static (off-line) CSO. We introduce sector-based regular CSO patterns for the first time. We organize the existing and newly introduced patterns using a systematic nomenclature; studying 26 patterns in total. We compare these patterns in terms of energy efficiency and the average number of users supported, via a combination of analysis and simulation. We also compare the performance of CSO with two benchmark algorithms. We show that the average number of users can be captured by one parameter. Moreover, we find that the distribution of the number of users is close to Gaussian, with a tractable variance. Our results demonstrate that several patterns that activate only one out of three sectors are particularly beneficial; such CSO patterns have not been studied before.

Resource Allocation for CoMP in Cellular Networks With Base Station Sleeping

Base station sleeping (BSS) can result in significant reduction in energy consumption of cellular networks during low traffic conditions. We show that the coverage loss due to BSS can be compensated via coordinated multi-point (CoMP) -based transmission in a cluster of base stations. For a BSS with CoMP-based system, we propose various BSS patterns to achieve suitable trade-offs between energy savings and throughput. We formulate the CoMP resource allocation and $\alpha -$ Fair user scheduling as a joint optimization problem. We derive the optimal time fraction and user scheduling for this problem and use it to formulate a simplified BSS with CoMP optimization problem. A heuristic that solves this problem is presented. Through extensive simulations, we show that suitable trade-offs among energy, coverage, and rate can be achieved by appropriately selecting the BSS pattern, CoMP cluster, and rate threshold.

Mobile Data Offloading for Green Wireless Networks

The explosive growth in mobile data traffic has resulted in unprecedented energy consumption in cellular networks, and has also induced huge operational expenditure for mobile network operators. A promising solution to address this problem in 5G systems is to use complementary technologies, such as Wi-Fi, to offload the traffic originally targeted toward cellular networks. In this article, we discuss the recent advances in the technologies and economics of two types of mobile data offloading: operator-initiated offloading and user-initiated offloading. In operator- initiated offloading, the mobile operators offload cellular traffic to Wi-Fi networks, which may belong to the mobile operators, mobile operators' residential subscribers, and third party Wi-Fi owners. In user-initiated offloading, the users decide how to offload traffic with or without the mobile operators' coordination. We present a taxonomy of various data offloading models, discuss various technical and economic challenges, and summarize the algorithms and mechanisms that we design to address these challenges. Finally, we outline some open problems that require further investigation.

Reducing the Energy Footprint of Cellular Networks with Delay-Tolerant Users

One of the most promising techniques to drastically reduce the energy consumption of cellular networks is the use of sleep-mode (SM) methods: when the traffic load is low, some components of the network, such as a base station (BS), can be switched off. In this case, the traffic load is managed by BSs that stay on . In this paper, we investigate how user cooperation can further reduce the energy consumption of a cellular network that uses SM strategies. In particular, we study how sleeping periods can be extended when users tolerate a delay before the start of their service. We propose two delay-tolerant-user-aware SM strategies and provide mathematical grounds for the evaluation of their performance. We evaluate the strategies in the context of LTE networks with realistic daily traffic patterns. The results show considerable daily energy reductions (up to 21% compared to the always- on paradigm and up to 15% compared to the SM strategy without user cooperation).

A Solar Energy Solution for Sustainable Third Generation Mobile Networks

The energy consumption of cellular networks has become increasingly important to cellular network operators, due to the significant economic and ecological influence of these networks in the future. The development of alternative energy technologies has resulted in the consideration of a solar powered base station (BS) as a long-term solution for the mobile cellular network industry, to reduce the operational expenditures and CO2 footprints of cellular networks. This study addresses the deployment and operational issues of a solar powered universal mobile telecommunications system (UMTS; a third generation mobile cellular system) BS (i.e., Node B) that is currently deployed (i.e., UMTS Node B 2/2/2 and UMTS Node B 4/4/4). In addition, this study employs a hybrid optimization model for an electric renewable software simulator developed by the American National Renewable Energy Laboratory. Four key aspects are discussed in this study: optimal solar system architecture, energy production, the cash flow of the solar powered UMTS Node B project, and the economic feasibility of a solar powered system compared with traditional sources. Simulation results show that the proposed solution ensures 100% energy autonomy and long-term energy balance for the UMTS Node B, with cost effectiveness.

Techno-Economic Evaluation of a Stand-Alone Power System Based on Solar Power/Batteries for Global System for Mobile Communications Base Stations

Energy consumption in cellular networks is receiving significant attention from academia and the industry due to its significant potential economic and ecological influence. Energy efficiency and renewable energy are the main pillars of sustainability and environmental compatibility. Technological advancements and cost reduction for photovoltaics are making cellular base stations (BSs; a key source of energy consumption in cellular networks) powered by solar energy sources a long-term promising solution for the mobile cellular network industry. This paper addresses issues of deployment and operation of two solar-powered global system for mobile communications (GSM) BSs that are being deployed at present (GSM BS 2/2/2 and GSM BS 4/4/4). The study is based on the characteristics of South Korean solar radiation exposure. The optimum criteria as well as economic and technical feasibility for various BSs are analyzed using a hybrid optimization model for electric renewables. In addition, initial capital, replacement, operations, maintenance, and total net present costs for various solar-powered BSs are discussed. Furthermore, the economic feasibility of the proposed solar system is compared with conventional energy sources in urban and remote areas.

Energy and Spectral Efficiency of Cellular Networks With Discontinuous Transmission

Cell discontinuous transmission (DTX) has been proposed as a solution to reduce the energy consumption of cellular networks. This paper investigates the impact of network traffic load on the spectral and energy efficiency of cellular networks with DTX. The signal-to-interference-plus-noise ratio (SINR) distribution as a function of traffic load is derived first. Then, the sufficient condition for ignoring thermal noise and simplifying the SINR distribution is investigated. Based on the simplified SINR distribution, the network spectral and energy efficiency as functions of network traffic load are derived. It is shown that the network spectral efficiency increases monotonically in traffic load, while the optimal network energy efficiency depends on the ratio of the sleep-mode power consumption to the active-mode power consumption of base stations. If the ratio is larger than a certain threshold, the network energy efficiency increases monotonically with network traffic load and is maximized when the network is fully loaded. Otherwise, the network energy efficiency first increases and then decreases in network traffic load. The optimal load can be identified with a binary search algorithm. The power ratio threshold depends solely on the path loss exponent $\alpha$ , e.g., 56% for $\alpha = 4$ . All these analytic results are further validated by the numerical simulations.

On Optimal Cell Flashing for Reducing Delay and Saving Energy in Wireless Networks

To save energy from cellular networks or to increase user-perceived performance, studying base station (BS) switching on–off is actively ongoing. However, many studies focus on the tradeoff between energy efficiency and user-perceived performance. In this paper, we propose a simple technique called cell flashing. Cell flashing means that base stations are turned on and off periodically and rapidly so that, when one base station is turned on, the adjacent base stations which make interferences are always off. Thus, both energy efficiency and cell edge user performances can be improved. In general, switching off base stations to save energy can lead to longer file download time (or delay) to customers. Using flow-level dynamics, we analyze average delay and energy consumption of cellular networks when cell flashing is used. We show that both of total energy consumption and average flow-level delay decrease in the case of small cells. Extensive simulations confirm that cell flashing can significantly save the energy of the base stations, e.g., by up to 25% and, at the same time, reduce the average delay by up to 75%.

Two-stage base station sleeping scheme for green cellular networks

In this paper, we propose a two-stage base station (BS) sleeping scheme to save energy consumption in cellular networks. The BS sleeping mode is divided into a light sleeping stage and a deep sleeping stage according to whether there is a user in the BS's coverage. In the light sleeping stage, a BS will retain its coverage and frequently switch between the on state and the doze state according to the service characteristics. While in the deep sleeping stage analysis, the BS will shut down its coverage, and neighbor BSs will patch the coverage hole. Several closed-form formulas are derived to demonstrate the power consumption in each sleeping stage and the stage switching conditions are discussed to minimize the average power consumption. The average traffic delay caused by BS sleeping and the average deep sleeping rate under a given traffic load have also been studied. In addition, it is shown that BS sleeping is not always possible because of the limited quality of service (QoS) requirements. Simulation results show that the proposed scheme can effectively reduce the average BS power consumption, at the cost of some extra traffic delay. In summary, our proposed framework provides an essential understanding of the design of future green networks that aim to take full advantage of different stages of BS sleeping to obtain the best energy efficiency.

Next-Generation Environment-Aware Cellular Networks: Modern Green Techniques and Implementation Challenges

Over the last decade, mobile communications have been witnessing a noteworthy increase of data traffic demand that is causing an enormous energy consumption in cellular networks. The reduction of their fossil fuel consumption in addition to the huge energy bills paid by mobile operators is considered as the most important challenges for the next-generation cellular networks. Although most of the proposed studies were focusing on individual physical layer power optimizations, there is a growing necessity to meet the green objective of fifth-generation cellular networks while respecting the user's quality of service. This paper investigates four important techniques that could be exploited separately or together in order to enable wireless operators achieve significant economic benefits and environmental savings: 1) the base station sleeping strategy; 2) the optimized energy procurement from the smart grid; 3) the base station energy sharing; and 4) the green networking collaboration between competitive mobile operators. The presented simulation results measure the gain that could be obtained using these techniques compared with that of traditional scenarios. Finally, this paper discusses the issues and challenges related to the implementations of these techniques in real environments.

The Design of LTE System

There has been a paradigm shift in the field of mobile communication, with an overwhelming increase in data usage. As more and more users are migrating to Smart-phones, the amount of data being transmitted has increased manifold times. However, such huge amount of data and signal propagation is bound to be detrimental to the ecological balance. Long Term Evolution (LTE), due to its flexibility, and backward compatibility has emerged as the network of choice for 4G and beyond. In this article, the significance of core technologies for LTE network is highlighted, along with the inherent advantage of reducing the energy consumption of cellular network. An Energy Efficient design of LTE is proposed which blends the technologies proposed by 3GPP such as adaptive OFDMA with that of MU-MIMO.