Energy Efficient Base Station Research Articles

ENERGY EFFICIENT OPERATION FOR NEXT GENERATION MASSIVE MIMO NETWORK

For the next generation of wireless networks, massive multiple-input, multiple-output (MIMO) is regarded as a potential technique. Base stations (BS) are provided with a relatively large number of antenna components in massive MIMO in order to increase spectral efficiency (SE) and energy efficiency (EE). Meanwhile, EE of BSs are becoming an increasingly important concern for communications operators in order to maintain profitability and reduce the overall negative impact on the environment and economic concerns for wireless network users. In this paper, an energy-aware, load-adaptive operation approach is proposed for massive MIMO for the next generation of wireless communication systems. Energy-aware, load-adaptive network operation has been identified as a promising method for improving the energy efficiency of telecommunication networks. Our research indicates that at optimal sumSE, fully load-adaptive massive MIMO may greatly increase network EE by up to 8.666%, 43.317%, and 69.367% for high, medium, and low load factors, respectively, when compared to traditional non-load-adaptive massive MIMO.

GREEN COMMUNICATIONS

Green communication is an innovative research area to find radio communication and networking solutions that can significantly improve energy efficiency and resource efficiency of wireless communications without compromising the QoS of users. It contributes to global environment improvement and achieves commercial benefits for telecommunication operators. The main goal of designing green base stations is to save energy and reduce power consumption while guaranteeing user service and coverage and ensuring the base station's capability for evolution. In a wireless network base station, power consumption is the biggest issue. With global warming and energy crises becoming the most compelling environmental challenges, green solutions are a common issue to be handled. Hence, the primary focus of the “Green cellular network” is saving power in base stations to “care for planet and operator’s valet.” we reviewed a few techniques for saving power consumption and improving energy efficiency in base stations. However, many technical challenges for base station architecture redesign, heterogeneous network deployment, radio resource management, etc., need to be addressed for energy-efficient base stations.

Energy-Efficient Base Station Switching-Off With Guaranteed Cooperative Profit Gain of Mobile Network Operators

This paper studies the energy-efficient cooperative base station (BS) switching-off in multi-input single-output (MISO) cellular networks, where the roaming-cost-based cooperation and the cooperative beamforming among multiple mobile network operators (MNOs) are jointly designed. To make the cooperation among MNOs practical, we introduce a new constraint to make sure the cooperative profit gain of MNOs is non-negative and also formulate an optimization problem to maximize the system energy efficiency (EE) by jointly considering multiple factors, including the operation modes of BSs, the connection states between users and BSs, the beamforming vectors of multi-antenna BSs under constraints of users’ quality of service, BSs’s power budget, and the non-negative profit gain of MNOs, etc. To solve the non-convex problem, we propose an energy-efficient beamforming-aware BS switching off method (EBBSOM), in which we first design a roaming-cost-based BS switching-off algorithm (EE-BSOA) to search the feasible inactive BSs and then propose a successive convex approximation (SCA) and Dinkelbach’s method-based EE maximization beamforming algorithm (EE-BFA) to optimize beamforming vectors. In order to keep the cooperative profit gain non-negative for MNOs and also to balance the load and power consumption of BS from the profit perspective, we present a profit-wise BS switching-off priority method. We theoretically prove that the proposed EBBSOM is able to resolve the inconsistency between BS switching-off and EE maximization. Simulations show that the proposed EBBSOM enhances the system EE without degrading the economic profit gain of MNOs. Also, the proposed profit-wise BS switching-off priority enables the EBBSOM to achieve higher EE than traditional load-wise one. Furthermore, it is also observed that there exists a feasible rate price region, which is able to clearly characterize network EE and the average profit gain of MNOs simultaneously.

Improving UAV base station energy efficiency for industrial IoT URLLC services by irregular repetition slotted-ALOHA

In the past few years, the continual improvement of 5G technology has supported powerful IoT (Internet of Things) and IIoT (Industrial IoT) devices which have been used to provide a wide range of services. One of the service types that has been noticed recently is URLLC (Ultra-Reliable Low-Latency Communication) service that requires highly reliable communication and low latency bounds. In this paper, intending to meet the latency and reliability requirements of IIoT users located outdoor in a hard-to-reach area, we propose to use a UAV-BS (Unmanned Aerial Vehicle Base Station) for air-to-ground (A2G) communications. Despite the very fact that UAV-BSs have been used widely, yet they have some shortcomings in flight time. So we proposed an energy-efficient trajectory design method to reduce the UAV-BS’s energy consumption for both the communication and mobility functions while fulfilling the application’s reliability and latency requirements. We proposed a UMC-IRSA (UAV-BS Multi-Channel Irregular Repetition Slotted-ALOHA) method to adapt the New Radio (NR) distinct frame structure for IIoT users. The IIoT nodes are clustered by the UMC-IRSA method (based on Mahalanobis distance) to decrease the UAV-BS energy consumption. The simulation results show that the UMC-IRSA clustering method combined with the Q-Learning algorithm for clusters serving decreases the UAV-BS energy consumption for flying in fixed altitude. The reduction in energy consumption provided by using the combination of UMC-IRSA and Q-Learning, in comparison to the combination of UMC-IRSA and Random Serving, combination of CRP (Chinese Restaurant Process) and Q-Learning, and CRP and Random Serving is 19%, 24%, and 31% respectively.

Passive-Shaped Beam Synthesis Using Pattern Diversity Dielectric Resonator Antenna Array

The passive antenna array is attractive for energy-efficient base station antenna. However, due to the absence of active elements, a large number of antenna elements are required to provide adequate degree of freedoms (DoFs) for shaped beam synthesis. This results in a bulky antenna structure of the conventional passive antenna array. In this work, pattern diversity dielectric resonator antenna (DRA) is first utilized for passive antenna array-shaped beam synthesis. Pattern diversity DRA can provide extra DoFs by exciting orthogonal transverse electric (TE) and transverse magnetic (TM) modes with diverse ports. For demonstration, a four-element linear pattern diversity DRA array is designed to verify its feasibility on shaped beam synthesis. Array configurations and desired user patterns are formulated and optimized by the artificial fish swarm algorithm. Its synthesis performances on six pattern shapes with different peaks and nulls are evaluated and compared with a conventional four-element phase array. It was found that the proposed passive antenna array has better synthesis ability with a smaller mean square error than that of the conventional phased array. The measured results verify the feasibility of using pattern diversity DRA as a passive scheme for energy-efficient base station antenna.

Optimum Downlink Beamwidth Estimation in mmWave Communications

With increasing density of data-hungry devices per unit area, allocating single highly-directed beam per user in millimeter-wave communications is not practical. Therefore, the requirement is to serve multiple users over a single beam. Considering a single-cell scenario with a fixed number of users, this paper addresses the problem of selection of optimal beamwidth depending on user density and distribution. First, by considering fixed beam service time in each sector, optimal beamwidth is estimated using exhaustive search for average long-run user rate and base station energy efficiency maximization. Based on the results of the average long-run user rate maximization using an exhaustive search, another method of reduced complexity is proposed to find sub-optimal beamwidth. Subsequently, optimum beamwidth is estimated with user density dependent variable time scheduling in a sector, that offers improved performances over fixed time scheduling. An efficient algorithm on variable time scheduling is also provided. Finally, the effect of localization error on optimal beamwidth estimation is investigated. The numerical results show that using the narrowest beam does not necessarily result in achieving a better average long-run user rate. Further, localization error does not affect the selection of optimal beamwidth, however, user Quality-of-Service degrades.

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.

Energy-Efficient Base Station Control Framework for 5G Cellular Networks Based on Markov Decision Process

We study the problem of base station (BS) dynamic switching for energy efficient design of fifth-generation (5G) cellular networks and beyond. We formulate this problem as a Markov decision process (MDP) and use an approximation method known as policy rollout to solve it. This method employs Monte Carlo sampling to approximate the Q-value. In this paper, we introduce a novel approach to design an energy-efficient BS control algorithm. We design an MDP-based algorithm to control the on/off switching of BSs in real time; we exploit user mobility and location information in the selection of the optimal control actions. We start our formulation with the simple case of one-user one-ON. We then gradually and systematically extend this formulation to the multi-user multi-ON scenario. Simulation results show the potential of our novel approach of exploiting user mobility information within the MDP framework to achieve significant energy savings while providing quality-of-service guarantees.

On the Energy-Efficient Deployment for Ultra-Dense Heterogeneous Networks With NLoS and LoS Transmissions

We investigate network performance of ultra-dense heterogeneous networks and study the maximum energy-efficient base station deployment incorporating probabilistic non-line-ofsight and line-of-sight transmissions. First, we develop an analytical framework with the maximum instantaneous received power and the maximum average received power association schemes to model the coverage probability and related performance metrics, e.g., the potential throughput and the energy efficiency (EE). Second, we formulate two optimization problems to achieve the maximum energy-efficient deployment solution with specific service criteria. Simulation results show that there are tradeoffs among the coverage probability, the total power consumption, and the EE. To be specific, the maximum coverage probability with ideal power consumption is superior to that with practical power consumption when the total power constraint is small and inferior to that with practical power consumption when the total power constraint becomes large. Moreover, the maximum EE is a decreasing function with respect to the coverage probability constraint.

Energy-Efficient Base Station Operation and Association in HetNets: Complexity and Algorithms

This paper studies the base station operation and association (BOA) problem for energy-efficient heterogeneous cellular networks. The objective is to find the set of base stations (BSs) to activate and to associate users to BSs under minimum rate requirements. BOA is formulated as a nonconvex programming problem. In order to solve it, we distinguish between two cases: 1) BOA with high-rate requirements (BOAH) and 2) BOA with low-rate requirements (BOAL). First, we show that finding feasible solutions for BOAH is NP-hard, and second, we reduce it to a BS operation problem (i.e., user association becomes straightforward). Based on this reduction, we develop a brute force algorithm and show that its complexity can be extremely reduced though it is still exponential. Hence, we propose a polynomial-time heuristic algorithm. As for BOAL, since BOA is extremely coupled, we relax the problem. Consequently, BOAL can be formulated as an integer linear program. Finding feasible solutions to it is shown to be NP-hard. To efficiently solve it, we propose a greedy-based algorithm. The proposed greedy algorithm is shown to admit a logarithmic approximation factor when it finds feasible solutions and a constant approximation factor otherwise. Finally, simulation results illustrate the performance of the proposed algorithms.

Traffic-Aware Energy-Saving Base Station Sleeping and Clustering in Cooperative Networks

We consider energy efficient base station (BS) sleeping and clustering problems in cooperative cellular networks, where clusters of base stations jointly transmit to users. Our key idea of energy saving is to exploit spatio-temporal fluctuation of traffic demand, and use minimal energy to provide achievable data rate only slightly greater than varying traffic demand. However, it is highly challenging to design traffic-aware algorithms without the future traffic demand information. To overcome this difficulty, we develop joint BS sleeping and clustering algorithms using queue instead of the future traffic information. The queue length information captures spatio-temporal mismatch between traffic demand and offered data rate. For BS clustering problem, we propose an optimal algorithm under given BS sleep mode state that has polynomial complexity. We integrate the optimal clustering solution into the sleeping problem, which is a complex combinatorial problem, and develop a joint optimal clustering and sleeping algorithm with reduced complexity compared to the exhaustive search. We also develop a greedy algorithm that finds a near-optimal clustering and sleeping solution with polynomial complexity. Through extensive simulations, we show that the proposed algorithms can save significant energy when traffic load is low.

An energy‐efficient joint antenna and user selection algorithm for multi‐user massive MIMO downlink

Massive multiple-input multiple-output (MIMO) technology employs hundreds of antennas at the base station (BS) of a cellular system. Activating hundreds of antennas requires hundreds of radio-frequency (RF) chains that lead to high cost and high energy consumption. Antenna selection techniques reduce the number of RF chains while curtailing the resulting performance loss. In this study, the authors consider massive-MIMO downlink with zero-forcing (ZF) precoding in a single cell. In ZF precoding with antenna selection, the number of users must always be less than or equal to the number of selected antennas, which may require adopting user selection (scheduling) algorithms. They propose a joint user and antenna selection algorithm to increase BS's energy efficiency (EE) and show that its performance is very close to that of the optimum exhaustive-search based scheme. Using an analytical approximation for EE of the proposed scheme in asymptotically large numbers of users region, they find the optimum number of antennas to be selected.

A Survey on Energy Efficient Base Station Sleeping Techniques in Green Communications

A Survey on Energy Efficient Base Station Sleeping Techniques in Green Communications

Energy Efficient Base Station Density Analysis for Heterogeneous Networks with eICIC

Energy Efficient Base Station Density Analysis for Heterogeneous Networks with eICIC

Base Station Energy Efficiency Improvement for Next Generation Mobile Networks

Abstract As more and more Base Stations (BSs) are being deployed by mobile operators to meet the ever increasing data traffic, solutions have to be found to try and reduce BS energy consumption to make the BSs more energy efficient and to reduce the mobile networks’ operational expenditure (OPEX) and carbon dioxide emissions. In this paper, a BS sleeping technology deployable in heterogeneous networks (HetNets) is proposed. The proposed scheme is validated by using extensive OMNeT++/SimuLTE simulations. From the simulations, it is shown that some lightly loaded micro BSs can be put to sleep in a HetNet when the network traffic is very low without compromising the QoS of the mobile network.

Energy Efficiency Gain of Cellular Base Stations with Large-Scale Antenna Systems for Green Information and Communication Technology

Due to the ever-increasing data demand of end users, the number of information and communication technology (ICT)-related devices and equipment continues to increase. This induces large amounts of heat emissions, which can cause serious environmental pollution. In recent times, signal transmission systems such as cellular base stations (BSs) have been constructed everywhere and these emit a large carbon footprint. Large-scale antenna systems (LSASs) that use a large amount of transmission antennas to serve a limited number of users can increase energy efficiency (EE) of BSs based on the beamforming effect, and thus can be a promising candidate to reduce the carbon footprint of the ICT field. In this paper, we discuss the necessary schemes to realize LSASs and show the expected EE gain of the LSAS with enough practicality. There are many obstacles to realize the high EE LSAS, and even though several studies have shown separate schemes to increase the EE and/or throughput (TP) of LSASs, few have shown combinations of schemes, and presented how much EE gain can be achieved by the schemes in the overall system. Based on the analysis in this paper, we believe more detailed work for the realization of high energy efficient BSs with LSASs is possible because this paper shows the necessary schemes and the maximum achievable energy efficiency gain as a reference. Extensive analysis and simulation results show that with proper implementation of the power amplifier/RF module and a robust channel estimation scheme, LSASs with 600 transmitter (TX) antennas can achieve 99.4 times more EE gain compared to the current systems, thereby resulting in significant reduction of carbon footprints.

Advanced Radio Resource Scheduling Algorithm for Energy Efficient Cellular Base Station

Advanced Radio Resource Scheduling Algorithm for Energy Efficient Cellular Base Station

Design of an Energy Efficient Future Base Station with Large-Scale Antenna System

Due to the continuous increase in data demanded by end-users, an energy-efficient base station (BS) is a vital topic of interest that would not only result in a substantial economic impact on service providers, but would also reduce the carbon footprint of operating a network. In this regard, we propose the structure and systematic operation of a BS with a large-scale (LS) antenna system that can increase the energy efficiency (EE) of cellular systems. The proposed BS structure includes various power-related units, such as a central management apparatus, power controller, EE calculator, radio site-dependent parameter space (RSD-PS) and determiner. With the information provided from each unit, the decision unit determines how to adjust each component of the BS in order to maximize the EE. Extensive simulations show that the proposed BS improves the EE performance by about 83.05% relative to the reference BS.

Cooperation-enabled energy efficient base station management for dense small cell networks

Dense small cell networks are deployed for future wireless communication to meet the ever-increasing mobile traffic demand. However, network densification will significantly increase the energy budget and lead to energy inefficiency due to the constant operation of network hardware. In this paper, we consider cooperation-enabled dynamic base station (BS) management for downlink dense small cell networks. By introducing two traffic-aware sleep modes, i.e., deep sleep mode and opportunistic sleep mode which are operating in different time and energy consumption scales, the network hardwares are turned to be the resources that can be occupied and released dynamically. Small cell BSs (SBSs) with zero or low load are completely switched off and reside in deep sleep mode during a predefined time interval. At each time slot, SBS dynamically turn some antennas and associated physical components into opportunistic sleep mode according to the short term traffic distribution, and the users are jointly served by the remaining antennas via cooperative transmission. The corresponding sleep mode decision making are presented respectively to find the optimal number of SBS and antennas that should be switched off. Numerical results are then presented to illustrate the superior performance in terms of energy efficiency gain. In summary, the proposed cooperation-aided sleep strategies for dense small cell networks take both traffic features and optimal cooperative transmission into account, and can achieve great energy saving while maintaining required quality of service.

Interference Reduction by Millimeter Wave Technology for 5G-Based Green Communications

The primary goal of this paper is the optimization of data transmissions and connections between 5G base stations (BSs) as well as the improvement of access technologies and transmission methods in consideration of massive multi-input multi-output, a key technology in 5G networks. In order to reach an access technology supported by multiple BSs and small cells, we use 5G millimeter wave (mmWave), due to its high directivity and sensitivity to blockage, to enhance the connection system. In the simulation, we will consider extremely high-frequency band and small angle of mmWave, and arrange obstructions in the environment in view of high attenuation characteristics in mmWave signals. After a wave beam penetrates through a wall, the power of the wave sharply decreases. For reduction of energy consumption, the wave therefore will select an mmWave BS with poor signal quality but without blockage to transmit data. Because the number of macro-cells will affect the communication quality and the computational complexity, this paper especially focuses on three factors of a network: delay, capacity, and path loss, and purposes to figure out the most energy-efficient BS densities for 5G-based green communications.