Base Station Sleep Research Articles

Joint Optimization of Functional Split, Base Station Sleeping, and User Association in Crosshaul-Based V-RAN

Joint Optimization of Functional Split, Base Station Sleeping, and User Association in Crosshaul-Based V-RAN

Hybrid Control Strategy for 5G Base Station Virtual Battery-Assisted Power Grid Peak Shaving

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load.

Performance Measurement of Small Cell Power Management Mechanism in 5G Cellular Networks using Firefly Algorithm

In cellular networks, with the increase in demand, designing a base station (BS) with less energy consumption remains a challenge for researchers. Also, in a heterogeneous network that is dense in nature, the distribution of numerous small BS has become a challenging issue in terms of expanding the cost of energy. In this paper, we investigate an optimized nature-based cluster sleep technique for reducing the power consumption in the BS as well as the interference in the network. The small BS are grouped along with the interference, which is assumed to be the cluster, which is quite large, where the fire fly (FF) algorithm is applied to frame the sleep technique for the small BS. These FF algorithms, which are based on fire fly attractiveness behavior, improve connectivity among the base stations in an energy-efficient way. The outcomes reveal that the projected sleep technique with the FF algorithm reduces the power consumed by the BS and also gives satisfactory performance for mobile users. The results were compared with the other techniques, such as BS conventional sleep mode and BS sleep mode with LEACH. The proposed method outperformed the other techniques.

Cellular traffic forecasting based on inverted transformer for mobile perception dual-level base station sleep control

Due to the extensive implementation of the fifth generation wireless communication networks (5 G), numerous base stations are being strategically deployed in densely inhabited areas to address the escalating need for network traffic. Within wireless cellular networks, over 80% of energy consumption is primarily concentrated on the side of the base station. In comparison to 4 G base stations, the 5 G base stations exhibit notable enhancements in terms of bandwidth, peak rate, and transmission power. Nonetheless, they suffer from limited signal coverage and extensive resource consumption. To address this challenge effectively, the present study introduces a Mobile Perception Dual-Level (MPDL) base station sleep control strategy based on cellular traffic forecasting. The strategy aims to satisfy the demands for reduced power consumption and enhanced service quality by monitoring both the current network traffic of the base station and the user's mobility, consequently placing the base station into two different sleep states. Specifically, we first designed an inverted Transformer (iTransformer) model to accurately forecast cellular traffic. The model is capable of effectively capturing both the spatial and temporal attributes of cellular traffic. It also takes into account the impact of geographical features, such as Points of Interest (POIs) and the quantity of Base Stations (BSs), on the forecasting outcomes. Consequently, it attains precise predictions of the cellular traffic. Based on the forecasting outcomes of cellular traffic, this study presents a mobility perception dual-level sleep strategy. This strategy proposal differentiates sleep into deep sleep and light sleep, while also taking into account the mobility attributes of users. It incorporates considerations for Quality of Service (QoS) and energy consumption. We assess the performance and application impacts of cellular traffic forecasting and base station sleep strategy in real-world datasets. Several experiments and validations have confirmed that the MPDL, as proposed in this paper, effectively decreases power consumption by a range of 22% to 41% when compared to baselines.

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.

Network Traffic Prediction with Reduced Power Consumption towards Green Cellular Networks

The increased number of cellular network subscribers is giving rise to the network densification in next generation networks further increasing the greenhouse gas emission and the operational cost of network. Such issues have ignited a keen interest in the deployment of energy-efficient communication technologies rather than modifying the infrastructure of cellular networks. In cellular network largest portion of the power is consumed at the Base stations (BSs). Hence application of energy saving techniques at the BS will help reduce the power consumption of the cellular network further enhancing the energy efficiency (EE) of the network. As a result, BS sleep/wake-up techniques may significantly enhance cellular networks' energy efficiency. In the proposed work traffic and interference aware BS sleeping technique is proposed with an aim of reducing the power consumption of network while offering the desired Quality of Service (QoS) to the users. To implement the BS sleep modes in an efficient manner the prediction of network traffic load is carried out for future time slots. The Long Short term Memory model is used for prediction of network traffic load. Simulation results show that the proposed system provides significant reduction in power consumption as compared with the existing techniques while assuring the QoS requirements. With the proposed system the power saving is enhanced by approximately 2% when compared with the existing techniques. His proposed system will help in establishing green communication networks with reduced energy and power consumption.

An Energy-Efficient Multi-Level Sleep Strategy for Periodic Uplink Transmission in Industrial Private 5G Networks.

This paper proposes an energy-efficient multi-level sleep mode control for periodic transmission (MSC-PUT) in private fifth-generation (5G) networks. In general, private 5G networks meet IIoT requirements but face rising energy consumption due to dense base station (BS) deployment, particularly impacting operating expenses (OPEX). An approach of BS sleep mode has been studied to reduce energy consumption, but there has been insufficient consideration for the periodic uplink transmission of industrial Internet of Things (IIoT) devices. Additionally, 5G New Reno's synchronization signal interval limits the effectiveness of the deepest sleep mode in reducing BS energy consumption. By addressing this issue, the aim of this paper is to propose an energy-efficient multi-level sleep mode control for periodic uplink transmission to improve the energy efficiency of BSs. In advance, we develop an energy-efficient model that considers the trade-off between throughput impairment caused by increased latency and energy saving by sleep mode operation for IIoT's periodic uplink transmission. Then, we propose an approach based on proximal policy optimization (PPO) to determine the deep sleep mode of BSs, considering throughput impairment and energy efficiency. Our simulation results verify the proposed MSC-PUT algorithm's effectiveness in terms of throughput, energy saving, and energy efficiency. Specifically, we verify that our proposed MSC-PUT enhances energy efficiency by nearly 27.5% when compared to conventional multi-level sleep operation and consumes less energy at 75.21% of the energy consumed by the conventional method while incurring a throughput impairment of nearly 4.2%. Numerical results show that the proposed algorithm can significantly reduce the energy consumption of BSs accounting for periodic uplink transmission of IIoT devices.

Joint coded caching and BS sleeping strategy to reduce energy consumption in 6G edge networks

In the coming sixth-generation mobile communication era, the intensive deployment of Internet of Things (IoT) devices and cellular networks is an irresistible trend, leading to system energy consumption and network traffic increasing sharply. Fortunately, edge caching as a promising technology to reduce system energy consumption and transmission latency is attracting wide attention. Although simply deploying cache in edge network and merely shutting down the idle base stations (BSs) during the idle periods can save certain energy to a certain extent, in this case, the contents with important mission cached in idle BSs cannot be accessed by users that will affect users’ experience. In this paper, we employ coded caching encoded by maximum distance separable (MDS) codes at the network edge, and we propose a joint coded caching and BS sleeping strategy, which utilizes the reconstruction feature of MDS codes to alleviate the impact of BS sleeping. Furthermore, the problem of minimizing energy consumption is studied, and we also design a discrete particle swarm optimization (DPSO) algorithm that is suitable to solve this mixed integer nonlinear programming problem. Simulation results reveal that energy consumption of the joint coded caching and BS sleeping strategy can be significantly decreased over 15.2% when compared with the current state-of-art strategy. Meanwhile, our proposed strategy can also improve the cache hit rate up to a maximum 11.1% compared with the existing strategies.

Optimization of mobile‐relay‐aided base station sleep mode in wireless dense small cell network

AbstractIn this paper, a novel sleep‐mode base station (BS) selection scheme, namely forced handover, is proposed using the mobile stations as mobile relay stations in order to maximize number of sleeping BSs. Forced‐handover is a concept of forcing edge mobile stations to neighbour relay stations for both energy saving and SINR improvement of the network. The proposed energy saving scheme can be applied to not only micro sleep and macro sleep mode but also turning off the BSs. The performances of BS sleep mode schemes is investigated in various environments such as small cell network and different user distributions.

Energy consumption optimization of 5G base stations considering variable threshold sleep mechanism

The explosive growth of mobile data traffic has resulted in a significant increase in the energy consumption of 5G base stations (BSs). However, the existing energy conservation technologies, such as traditional BS sleep strategy, rarely consider the dynamic real-time changes of users (UEs), which may make it difficult to maximize sleep idle or lightly loaded BSs, thereby affecting the reduction of BS energy consumption. Therefore, an energy consumption optimization strategy of 5G BSs considering variable threshold sleep mechanism (ECOS-BS) is proposed in this paper. Compared to current strategies, the ECOS-BS strategy can dynamically adjust BSs’ optimal sleep threshold on the premise of considering UEs’ dynamic changes, which is conducive to maximize sleep available BSs and realize the optimal management of BS energy consumption. Taking a heterogeneous cellular network as an example, numerical simulations are conducted to validate the effectiveness of the proposed strategy.

Semi‐dynamic Markov approximation‐based base station sleep with user association for heterogeneous networks

AbstractEnergy efficiency (EE) in the communication industry is the general trend of green communication development, and the base station sleep (BSS) strategy is crucial to improving EE. However, how to maximize the cell's EE and optimize the performance of users in the sleeping area after the BSS is a challenging problem. The sub‐6GHz and millimeter‐wave (mmWave) integrated networks are considered in this paper. The users’ backhaul of the cell is jointly provided by the sub‐6 GHz band and the mmWave band. User reaccess algorithm based on multiple parameters access criterion is adopted when their severed base stations (BSs) are shut down. The authors also consider a resource allocation algorithm based on the semi‐dynamic Markov approximation (MA) algorithm, which is proposed to quickly allocate sub‐channels to reaccessed users and ensure the maximum throughput. The simulation results show that the proposed algorithm can improve the EE of the heterogeneous network and promptly meet the users’ needs when some BSs are shut down.

Multi-Agent Reinforcement Learning with Information-sharing Constrained Policy Optimization for Global Cost Environment

Multi-agent Reinforcement Learning (MARL) is a machine learning method that solves problems by using multiple learning agents in a data-driven manner. Because of the advantage of utilizing multiple agents simultaneously, MARL has become an efficient solution to large-scale problems in a wide range of fields. However, as with general single-agent reinforcement learning, MARL requires trial and error to acquire the appropriate policies for each agent in the learning process. Therefore, how to guarantee performance and constraint satisfaction in MARL is a critical issue for application to real-world problems. In this study, we propose an Information-sharing Constrained Policy Optimization (IsCPO) method for MARL that guarantees constraint satisfaction during learning. In detail, IsCPO sequentially updates the policies of multiple agents in random order while sharing information of the surrogate costs and KL-divergence for evaluating the current and updated policies to the next agent. In addition, if there are no candidates of policies to be updated in accordance with the shared information, IsCPO skips updating the policies of the rest of the agents until the next iteration. As a result, IsCPO makes it possible to acquire the individual suboptimal policies of agents, satisfying constraints on global costs related to the state of the environment and the actions from multiple agents. We also introduce a practical algorithm for IsCPO that simplifies its implementation by adopting several mathematical approximations. Finally, we show the validity and effectiveness through simulation results on a multiple cart-pole problem and base station sleep control problem in a mobile network.

Smarter Base Station Sleeping for Greener Cellular Networks

One approach to achieving greener cellular networks is to power them with renewable energy. When there is insufficient renewable energy, base stations (BSs) with lower predicted traffic loads are dynamically switched to sleep mode as a means of reducing total BS energy consumption. Prediction is done using only the traffic logs of active BSs since traffic logs are not available for sleeping BSs. However, prediction accuracy is degraded if information for the entire network is not used. We propose a scheme built on software-defined-networking and edge computing technologies for maintaining the accuracy of traffic prediction by incorporating the most important traffic logs into the prediction even when using only traffic logs for active BSs. It works by estimating the contribution of the traffic logs for each BS to traffic prediction accuracy. Evaluation of the proposed scheme through extensive numerical experiments using a dataset of actual BS traffic logs demonstrates that the proposed scheme is superior to two benchmark schemes in terms of prediction accuracy and robustness against the reduction of active BSs for energy saving and different BS sets.

A survey on sleep mode techniques for ultra-dense networks in 5G and beyond

The proliferation of mobile users with an attendant rise in energy consumption mainly at the base station has requested new ways of achieving energy efficiency in cellular networks. Many approaches have been proposed to reduce the power consumption at the base stations in response to the contribution of energy cost to the increase of OPEX of the mobile operators and the rise of the carbon footprint on global climate. As a springboard to the application of sleep mode methods in ultra-dense cellular networks, this paper provides a comprehensive survey of the base station sleep mode strategies in heterogeneous mobile networks from perspectives of modeling and algorithm design. Specifically, the sleep mode enabling strategies and sleep wake-up schemes are reviewed. The base station sleep-mode techniques in ultra-dense networks are further discussed as well as the challenges and possible solutions.

Computing-Aware Base Station Sleeping Mechanism in H-CRAN-Cloud-Edge Networks

In this paper, a power minimization problem using base station sleeping is proposed for heterogeneous cloud radio access networks (H-CRANs) taking into account the computing delay constraints. In the proposed system, which is modeled using M/M/k queues, the edge device coexists with the small base station (SBS) to provide computing capabilities beside the central cloud. In general, the SBS sleeping is governed by the availability of resources provided the macro base station (MBS) which is in charge of accommodating offloaded users from sleeping SBSs. However, switching off lightly loaded SBSs can impose significant burdens on cloud servers. Here, the proposed sleeping scheme allows SBSs serving more computing tasks to remain active in order to fulfill the task completion deadlines requested by mobile users and to keep the cloud response time within a predefined limit. In other words, the proposed scheme aims to save power by undertaking a centralized selection of active and sleeping SBSs taking into account the delay constraints of both cloud and mobile devices. First, we consider a disjoint cloud-edge system, where computing services can be provided by either the cloud or the edge device, and aim to minimize the number of active SBSs. The problem is formulated as a 0-1 knapsack problem with SBS utilization considered as the weight while the ratio of computing tasks to all incoming tasks is considered as the value of that SBS. In this problem, which is solved using dynamic programming, SBSs processing less computing tasks are given higher values; and as a result, higher chance to sleep compared to others. Second, a shared computing system is proposed whereby active SBSs (edge devices) contribute to the total computing capability. Here, an exhaustive search approach is used to achieve the optimal power saving. We also proved that the shared computing system performs better in terms of response time compared to the disjoint system depending on the number of active SBSs.

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.

Power Consumption and GoS Tradeoff in Cellular Mobile Networks With Base Station Sleeping and Related Performance Studies

Mobile network operators usually consider power consumption and Grade of Service (GoS) as two important aspects in the design and planning of modern cellular networks. Base station (BS) sleeping is an effective approach to reduce the power consumption of the network, by switching some of the BSs to a low-power “sleep mode” during off-peak traffic hours. In this paper, we model each BS with sleeping mechanism as an M/G/1/K queue with vacations, and the entire cellular network as a network of such queues, to incorporate practical factors in BS sleeping, such as close-down and startup periods and additional power consumption for activating a sleeping BS. We investigate the power consumption and GoS under three BS sleeping schemes: (1) the isolated scheme, in which each BS switches between active and sleep modes based on its own realtime traffic load, (2) the cooperative scheme, in which selective BSs are switched to long-term sleep and traffic is allowed to overflow from sleeping BSs to nearby active BSs, and (3) the hybrid scheme, in which some BSs are switched to long-term sleep and other BSs switch modes according to their real-time traffic load. A robust, scalable and computationally efficient analytical method is proposed to evaluate GoS metrics, including mean delay and blocking probability, and power consumption under each scheme. We validate the accuracy of the proposed method, demonstrate the trade-off among power consumption, blocking probability and mean delay, and compare the performance of the three schemes via extensive and statistically reliable numerical experiments.

Joint Load Control and Energy Sharing for Renewable Powered Small Base Stations: A Machine Learning Approach

The deployment of dense networks of small base stations represents one of the most promising solutions for future mobile networks to meet the foreseen increasing traffic demands. However, such an infrastructure consumes a considerable amount of energy, which, in turn, may represent an issue for the environment and the operational expenses of the mobile operators. The use of renewable energy to supply the small base stations has been recently considered as a mean to reduce the energy footprint of the mobile networks. In this article, we consider a hierarchical structure in which part of the base stations are powered exclusively by solar panels and batteries. Base stations are grouped in clusters and connected in a micro-grid. A central controller enables base station sleep mode and energy sharing among the base stations based on the available energy budget and the traffic demands. We propose three different implementations of the controller through Machine Learning models, namely Imitation Learning, Q-Learning and Deep Q-Learning, capable of learning optimal sleep mode and energy sharing policies. We provide an exhaustive discussion on the achieved performance, complexity and feasibility of the proposed models together with the energy and cost savings attained.

A self-organizing base station sleeping and user association strategy for dense cellular networks

Due to the rising concerns of energy consumption in wireless networks, base station (BS) sleeping strategies were introduced to save energy in low traffic scenarios. In this paper we analyse a weighted trade-off between energy consumption and user-perceived performance in dense cellular networks. We present an optimization problem representing this trade-off and derive properties of its optimal solutions. Using these properties we design a self-organizing strategy that dynamically (online) makes load-aware user association and BS operation decisions. Our strategy is self-organizing in the sense that it does not need any information or optimization beforehand, it simply relies on real-time load measurements at the BSs and user-reported SINR values. We furthermore present extensive simulation results, demonstrating the effectiveness of our self-organizing strategy and the impact of increased energy consumption on the user-perceived performance.

Analysis of Energy Consumption Using Sequential to Better Signal (SBS) Scheme for Green Celluler Network

Over time, cellular communication technology developed significantly from year to year. This is due to increasing the number of users and the higher needed. To overcome this problem, many providers increase the number of new base station installations to fill up the customer's needed. The increase number of base stations does not take into account the amount of power consumption produced, where in the cellular network Base Stations (BS) are the most dominant energy consuming equipment estimated at 60% - 80% of the total energy consumption in the cellular industry. In addition, energy waste often occurs in the BS where the emission power will always remain even if the number of users is small. Power consumption and energy savings are important issues at this time because they will affect CO2 emissions in the air. This paper proposes to save energy consumption from BS by turning off BS (sleep mode) if the number of users is small and distributed to other BS (neighboring BS) which is called cell zooming technique. The cell size can zoom out when the load traffic is high and zoom in when the load traffic is low. To determine the central BS and neighboring BS, a sequential to better signal (SBS) scheme is used where this scheme sorts neighboring BS based on the SINR value received (user). The results of this research, base station can be able to save energy 29.12% and reduce CO2 emission around 3580 kg/year. It means saving energy consumption which is also reducing air pollution occurs and this term can be named as green cellular network.