Energy Consumption Of Base Stations Research Articles

Energy Management Strategy for Distributed Photovoltaic 5G Base Station DC Microgrid Integrated with the CF-P&O-INC MPPT Algorithm

With its technical advantages of high speed, low latency, and broad connectivity, fifth-generation mobile communication technology has brought about unprecedented development in numerous vertical application scenarios. However, the high energy consumption and expansion difficulties of 5G infrastructure have become the main obstacles restricting its widespread application. Therefore, aiming to optimize the energy utilization efficiency of 5G base stations, a novel distributed photovoltaic 5G base station DC microgrid structure and an energy management strategy based on the Curve Fitting–Perturb and Observe–Incremental Conductance (CF-P&O-INC) Maximum Power Point Tracking (MPPT) algorithm from the perspectives of energy and information flows are proposed. Simulation results show that the proposed MPPT algorithm can increase the efficiency to 99.95% and 99.82% under uniform irradiation and partial shading, respectively. Under the proposed strategy, when the base station load changes drastically, the voltage fluctuation of the DC bus is less than 1.875%, and returns to a steady state within 0.07s, alleviating the high energy consumption of 5G base stations effectively and achieving coordinated optimization management of various types of energy in multi-source power supply systems.

Leveraging Edge Computing for Minimizing Base Station Energy Consumption in Multi-Cell (N)OMA Downlink Systems

Leveraging Edge Computing for Minimizing Base Station Energy Consumption in Multi-Cell (N)OMA Downlink Systems

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.

Mathematical modeling method based on heterogeneous cellular network algorithm and computer multi-dimensional space

Due to the rapid development of services such as computer multi-dimensional space, traditional cellular networks are quantified and diversified, and it is difficult to meet the future connection needs of green network users. The heterogeneous cellular network algorithm places multiple microcells and relay nodes under the macrocell. This multi-layer network architecture can not only shorten the data transmission link, but also reduce the energy consumption of base stations and terminals. In addition, through spectrum space reuse Capability and spectrum efficiency improve the efficiency of the system, but this also increases the number of base stations and network construction costs, and significantly increases energy consumption and operating costs of operators' electricity expenditures. In addition, in order to achieve parallel transmission of data, placing multiple millimeter wave input and output antenna systems in heterogeneous network microcells can meet the high-speed data needs of indoor users, but the antenna array requires many RF links to drive, which increases the hardware implementation. Complexity, cost and energy consumption. According to the above-mentioned problems, a reconfigurable modular robot is established based on the multi-dimensional computer space. It is composed of modules with the same interface and can be assembled into different configurations according to different tasks. Compared with the original robot, the reconfigurable modular robot is more adaptable to the work and environment, and flexible. Then the basic and core technologies such as kinematics, type synthesis, scale synthesis, error and control of the reconfigurable modular robot are mathematically modeled, so as to effectively apply and promote the reconfigurable modular robot in practice, and make it has theoretical and practical value.

Resilience-oriented planning strategy for the cyber-physical ADN under malicious attacks

With the increasing coupling between the communication network and the power system, malicious cyber-physical attacks pose a serious threat to the security of cyber-physical systems (CPS). Besides the direct failures caused by the attacks on the corresponding systems, the indirect failures caused by the physical attacks on the cyber side are also of concern. Therefore, this paper proposes a resilience-oriented planning strategy for the cyber-physical active distribution network (ADN) under the malicious attacks. The strategy first coordinates the planning of line hardening and signal protection to reduce the size of direct and indirect failures. In addition, the energy consumption of base stations (BSs) in wireless communication networks is adjusted based on communication reliability (CR) to improve the controlled state of resilience resources during the recovery process. Finally, the controllable distributed generations, capacitor banks and soft open points are scheduled considering the response capability differences to ensure the balance of active and reactive power during the emergency response and restoration dispatch. The proposed strategy is modeled as a D-A-Multi-D model. To evaluate the strategy, simulations are implemented on the 33-bus and 123-bus ADNs. The results show that neglecting the impact of indirect failures leads to an underestimation of the unserved load cost of about 21.7% compared to the proposed strategy. Moreover, BS adjustment and temporal recovery processes are more applicable to the resilience enhancement of the ADN.

An energy-efficient resource allocation strategy in massive MIMO-enabled vehicular edge computing networks

The vehicular edge computing (VEC) is a new paradigm that allows vehicles to offload computational tasks to base stations (BSs) with edge servers for computing. In general, the VEC paradigm uses the 5G for wireless communications, where the massive multi-input multi-output (MIMO) technique will be used. However, considering in the VEC environment with many vehicles, the energy consumption of BS may be very large. In this paper, we study the energy optimization problem for the massive MIMO-based VEC network. Aiming at reducing the relevant BS energy consumption, we first propose a joint optimization problem of computation resource allocation, beam allocation and vehicle grouping scheme. Since the original problem is hard to be solved directly, we try to split the original problem into two subproblems and then design a heuristic algorithm to solve them. Simulation results show that our proposed algorithm efficiently reduces the BS energy consumption compared to other schemes.

Research on Energy Efficiency Optimization Technology in Cellular Network

This paper analyzes the energy consumption of base stations involved in cellular networks, including main equipment, power supply equipment, environmental equipment and other auxiliary equipment, etc. On this basis, this paper discusses energy efficiency optimization techniques at several levels, such as base station deployment, components, and repair cotton. Under the current limited channel resources, it is inevitable to consider the balance between data services and voice services, and then to analyze the relevant indicators of network performance as a basis for the specific time.

Federated-Based Deep Reinforcement Learning (Fed-DRL) for Energy Management in a Distributive Wireless Network

Studies on developing future generation wireless systems are expected to support increased infrastructure development and device subscriptions with densely deployed base stations (BSs). Economically, decreasing BS energy consumption levels and achieving "greenness" remain key factors for the giant industry. Some research works have proposed deep reinforcement techniques to solve energy management (EM) issues in cellular networks. However, these techniques are inefficient in a distributive network environment and expose the devices to privacy issues. Federated learning (FL) is proven to enforce device privacy and train models distributively. Thus, this work proposes an autonomous switching mode framework for BSs based on federated-deep reinforcement learning (Fed-DRL) to address the aforementioned challenges encountered by prior studies. Specifically, we deploy multiple DRL agents to influence the decision of the BS for EM. On the other hand, to make DRL-based decisions feasible and satisfy device quality-of-service, we train the DRL agents distributively by employing the FL concept. The results show the effectiveness of our proposed framework under distributed network scenarios compared with other benchmark algorithms.

MAXIMIZATION THE LATENT HEAT STORAGE UNIT (LHSU) ENERGY SAVING USING SIMULATED ANNEALING ALGORITHM

An integration between latent heat storage unit (LHSU) and an air cooler was studied to reduce the space cooling energy consumption in telecommunications base stations (TBSS). A mathematical model was developed to address the heat transfer processes within energy storage modules and air cooler. Furthermore, a solid works simulation was used to simulate the real system virtually. A simulated annealing algorithm (SA) was utilized to maximize annual energy savings ratio which was considered a performance criterion for the proposed system. The calculations included comparisons the relative errors of air and temperature difference between the optimized using simulated annealing algorithm and tested (virtual) results which were calculated for both operation modes energy charging and energy discharging. The obtained results illustrated a remarkable improvement both relative errors and annual energy savings ratio for the study case.

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.

Using Reinforcement Learning to Reduce Energy Consumption of Ultra-Dense Networks With 5G Use Cases Requirements

In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively increase the network capacity due to cell splitting and densification. A Base Station (BS) is a fixed transceiver that is the main communication point for one or more wireless mobile client devices. As UDNs are densely deployed, the number of BSs and communication links is dense, raising concerns about resource management with regard to energy efficiency, since BSs consume much of the total cost of energy in a cellular network. It is expected that 6G next-generation mobile networks will include technologies such as artificial intelligence as a service and focus on energy efficiency. Using machine learning it is possible to optimize energy consumption with cognitive management of dormant, inactive and active states of network elements. Reinforcement learning enables policies that allow sleep mode techniques to gradually deactivate or activate components of BSs and decrease BS energy consumption. In this work, a sleep mode management based on State Action Reward State Action (SARSA) is proposed, which allows the use of specific metrics to find the best tradeoff between energy reduction and Quality of Service (QoS) constraints. The results of the simulations show that, depending on the target of the 5G use case, in low traffic load scenarios and when a reduction in energy consumption is preferred over QoS, it is possible to achieve energy savings up to 80% with 50 ms latency, 75% with 20 ms and 10 ms latencies and 20% with 1 ms latency. If the QoS is preferred, then the energy savings reach a maximum of 5% with minimal impact in terms of latency.

Mathematical Methods on Optimal Frequency Selection Model for Wireless Communication Transmission Signal under 5G Internet of Everything

Adaptive 5G communication has been accelerated thanks to the catalytic advances in the stable Internet of Things (IoT) which is a growing industry. This essay examines the impact of IoT, describes the evolution of the Internet under beneficial 5G communications, and explores 5G adaptive exchange and coordinating IoT advances. The reproduction evaluation was compatible with the evolution of telephone breathing, and developments in base station rigidity lowered base station energy consumption and club energy under the specification of ensuring communication quality, which means to further reassure compatibility. According to research, in a thick and uniform client course condition, power consumption has dwindled due to the extension of force use, but it has fundamentally improved and the correspondence quality has increased beyond what many would imagine achievable. It can ensure the client’s reasonableness and plan ability to execute, as well as fulfill the rate requirements of a variety of clients. The quick outcomes greatly test the speculative accuracy examination.

Energy-Efficient Ultra-Dense Network With Deep Reinforcement Learning

With the explosive growth in mobile data traffic, ultra-dense network (UDN) where a large number of small cells are densely deployed on top of macro cells has received a great deal of attention in recent years. While UDN offers a number of benefits, an upsurge of energy consumption in UDN due to the intensive deployment of small cells has now become a major bottleneck in achieving the primary goals viz., 100-fold increase in the throughput in 5G+ and 6G. In recent years, an approach to reduce the energy consumption of base stations (BSs) by selectively turning off the lightly-loaded BSs, referred to as the sleep mode technique, has been suggested. However, determining an appropriate active/sleep modes of BSs is a difficult task due to the huge computational overhead and inefficiency caused by the frequent BS mode conversion. An aim of this paper is to propose a deep reinforcement learning (DRL)-based approach to achieve a reduction of energy consumption in UDN. Key ingredient of the proposed scheme is to use decision selection network to reduce the size of action space. Numerical results show that the proposed scheme can significantly reduce the energy consumption of UDN while ensuring the rate requirement of network.

Field study on the performance of a thermosyphon and mechanical refrigeration hybrid cooling system in a 5G telecommunication base station

The increases in power density and energy consumption of 5G telecommunication base stations make operation reliability and energy-efficiency more important. In this paper, a novel type of rack-level hybrid cooling system which combines a thermosyphon loop with a mechanical refrigeration loop was developed and applied in two parallel cabinets installed different operating powers of the communication equipment. Its performance was tested on-site in a real 5G telecommunication base station in transitional season at Wuhan city, China. Thermal safety and energy consumption under the normal and urgent operation modes were evaluated. The results showed that under the normal daily operation mode 1, communication equipment can be cooled effectively, but the power usage effectiveness value reached 1.59. Under the urgent mode 7, communication equipment can maintain safe operation for over 20 min. In view of this, five novel energy-saving operation modes 2–6 were proposed and tested. The cooling effect and energy consumption under different modes were analyzed and compared. Under the thermosyphon mode 6, the cooling system can only meet the cooling demand for one side of the cabinet when the outdoor temperature is around 20 °C. Compared to mode 1, mode 5 was recommended due to the 7–9 °C lower operating temperature and 27.3% decrease of energy consumption.

DIC-ST: A Hybrid Prediction Framework Based on Causal Structure Learning for Cellular Traffic and Its Application in Urban Computing

The development of technology has strongly affected regional urbanization. With development of mobile communication technology, intelligent devices have become increasingly widely used in people’s lives. The application of big data in urban computing is multidimensional; it has been involved in different fields, such as urban planning, network optimization, intelligent transportation, energy consumption and so on. Data analysis becomes particularly important for wireless networks. In this paper, a method for analyzing cellular traffic data was proposed. Firstly, a method to extract trend components, periodic components and essential components from complex traffic time series was proposed. Secondly, we introduced causality data mining. Different from traditional time series causality analysis, the depth of causal mining was increased. We conducted causality verification on different components of time series and the results showed that the causal relationship between base stations is different in trend component, periodic component and essential component in urban wireless network. This is crucial for urban planning and network management. Thirdly, DIC-ST: a spatial temporal time series prediction based on decomposition and integration system with causal structure learning was proposed by combining GCN. Final results showed that the proposed method significantly improves the accuracy of cellular traffic prediction. At the same time, this method can play a crucial role for urban computing in network management, intelligent transportation, base station siting and energy consumption when combined with remote sensing map information.

Energy-Aware Collaborative Service Caching in a 5G-Enabled MEC With Uncertain Payoffs

Mobile edge computing (MEC) is an enabling technology for low-latency AI applications, by caching AI services originally deployed in remote data centers to 5G base stations in network edge. Due to limited computing resource of 5G base stations, not all services can be cached in base stations to meet the resource demands of user requests. Also, if the workload of a 5G base station reaches to its resource capacity, the energy consumption of the base station will be pushed up exponentially. To reduce the energy consumption and overcome resource limitations on base stations, an alternative is to allow the base stations to collaborate with each other to admit user requests. In this paper, we investigate the problem of collaborative service caching and request offloading between a 5G-enabled MEC and remote data centers, while meeting the quality of service (QoS) requirements of users, and resource capacities on base stations that are operated by multiple selfish network service providers. We aim to maximize the total payoff of all base stations. To this end, we first propose a two-stage optimization framework: In the first stage, we develop a mechanism that adopts a best-reply rule for dynamically distributed coalition formation. In the second stage, we propose a near-optimal payoff allocation method by devising a randomized algorithm with a provable approximation ratio. We then evaluate the performance of the proposed optimization framework by extensive experimental simulations. Simulation results show that the proposed framework outperforms its counterparts by achieving at least 30% higher payoff and 20% lower energy consumption of base stations.

Improvement of energy efficiency by dynamic load consolidation in C‐RAN

SummaryThe Fifth Generation (5G) network is the next step in the evolution of cellular networks, which are anticipated to encounter a remarkable growth in traffic. To meet this traffic requirement, the network capacity must be increased, which would inevitably necessitate the expansion of additional base stations (BSs). Moreover, the expenses and energy consumption of BSs are excessively high. The highly complex signal processing in baseband units contributes to a substantial amount of energy consumption. Consequently, cloud radio access network (C‐RAN) has been demonstrated as an effective approach to overcoming 5G wireless challenges through an energy‐efficient means. C‐RAN architecture has a baseband unit (BBU) pool that consists of the number of BBUs and remote sites, which are called remote radio heads (RRHs). As a sharing of ancillary subsystems into one location, BBU pooling has a significant impact on reducing power consumption. However, C‐RAN necessitates a novel paradigm of energy consumption for resource optimization. In this article, a C‐RAN with a dynamic load consolidation algorithm depending on traffic condition is presented to jointly take the power consumption and energy efficiency targeting to decrease power consumption and increase energy efficiency. To capture the energy consumption of C‐RAN, a new energy consumption scheme considering the BBU cloud, optical link, and radio side power has been proposed that optimizes the BBU resources. Numerical results come about based on simulation settings illustrate that the presented model accomplishes an improved energy‐efficient performance with respect to the referred baseline RAN system scheme.

Toward Net-Zero Base Stations with Integrated and Flexible Power Supply in Future Networks

The energy consumption and carbon emissions of base stations (BSs) raise significant concerns about future network deployment. Renewable energy is thus adopted and supplied to enable the net-zero (or zero-carbon) BS. However, due to severe inconsistency between renewable energy generation and power demand, the conventional one-to-one power supply architecture could cause large waste and low utilization of renewable energy. In this article, we design a many-to-many power supply architecture for BSs to maximize the utilization of renewable energy. More specifically, we strategically group multiple renewable energy generators into virtual cells to serve multiple BSs in an integrated way, thus minimizing the inconsistency between the renewable energy generation and power demand. To finetune the power mismatch between power supply and demand in each virtual cell, we propose software-defined techniques to flexibly control the discharging/charging of a battery energy storage system. Illustrative results from a case study demonstrate that high renewable energy utilization can be achieved with the proposed architecture and mechanisms.

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.

Optimal configuration for photovoltaic storage system capacity in 5G base station microgrids

Base station operators deploy a large number of distributed photovoltaics to solve the problems of high energy consumption and high electricity costs of 5G base stations. In this study, the idle space of the base station’s energy storage is used to stabilize the photovoltaic output, and a photovoltaic storage system microgrid of a 5G base station is constructed. Aiming at the capacity planning problem of photovoltaic storage systems, a two-layer optimal configuration method is proposed. The inner layer optimization considers the energy sharing among the base station microgrids, combines the communication characteristics of the 5G base station and the backup power demand of the energy storage battery, and determines an economic scheduling strategy for each photovoltaic storage system with the goal of minimizing the daily operation cost of the base station microgrid. The outer model aims to minimize the annual average comprehensive revenue of the 5G base station microgrid, while considering peak clipping and valley filling, to optimize the photovoltaic storage system capacity. The CPLEX solver and a genetic algorithm were used to solve the two-layer models. Considering the construction of the 5G base station in a certain area as an example, the results showed that the proposed model can not only reduce the cost of the 5G base station operators, but also reduce the peak load of the power grid and promote the local digestion of photovoltaic power.