Article

A Survey on Sleep Mode Techniques for Ultra-Dense Networks in 5G and Beyond

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Abstract

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.

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... To address the above issues, small cellular deployments inside buildings (such as micro, pico, and Femto Base Stations (FBSs)) can be used to cover signal gaps left by Macro Base Stations (MBSs), reduce signal penetration losses, achieve higher throughput and capacity, alleviate the load on MBSs, and minimize energy consumption [5] . In recent years, the deployment of FBSs has become increasingly dense [6] , and these FBSs together with MBSs form a HetNet. ...
... By considering the interference between MBSs and FBSs, Refs. [6,7] regard FBSs as intelligent agents leveraging the Qlearning algorithm to determine their optimal power settings within the network. In the case of not considering the impact of MBSs on FBSs, the overall network efficiency is controlled through the sleep-wake mechanism of FBSs, and proposing a particle swarm algorithm with binary input variables [11] . ...
... The downlink signal received by includes interference from FBSs and thermal noise. Therefore, the SINR of is calculated as follows [6] : ...
Article
The dense deployment of Femto Base Stations (FBS) assisting Macro Base Stations (MBS) in a Heterogeneous Network (HetNet) resolves the coverage issue of 5G signal transmission. However, the imprudent layout of FBSs results in extensive energy consumption and increased signal interference among base stations. Regulating the transmission power of each base station in the HetNets through the main controller or MBS is essential to maximize the power efficiency of the entire HetNets while adhering to the constraints of basic signal throughput and fairness. To address this challenge, this paper proposes an Adaptive Acceleration Particle Swarm Optimization (AA-PSO) algorithm. This algorithm dynamically determines the inertia weight based on each particle's optimal position and the global optimal position, and introduces the concept of time-varying parameters to control the learning rate, thus managing the search range and convergence speed of the particle swarm. The results demonstrate that the AA-PSO algorithm can efficiently determine the optimal transmission power of each base station in the HetNets, reduce interference between MBS and FBSs, as well as among FBSs, and ultimately improve the service efficacy of the entire network.
... In addition, it is also imperative to notice that before enabling the sleep mode in particular BSs, the intended sleep-enabled BSs must coordinate with other BSs and release its channel resources to active neighboring BSs. The active BS must provide extended data rate services and coverage to users located inside sleeping BSs [22]. ...
... From an implementation point of view, it is simple as compared to turning off/on BS. However, small-cell deployment is considered the most recent and robust method to improve EE in wireless cellular networks [22,25,[28][29][30]. This research work is also based on adapting EE in HetNets through a small-cell sleep deployment technique. ...
... Random sleep mode (RSM) and load-aware sleep mode (LSM) currently represent the state-of-the-art BS switch-off strategies within contemporary HetNets [22,28,30,42], as shown in Figure 5. However, they come up with their own limitations and challenges. ...
Article
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This research endeavors to advance energy efficiency (EE) within heterogeneous networks (HetNets) through a comprehensive approach. Initially, we establish a foundational framework by implementing a two-tier network architecture based on Poisson process distribution from stochastic geometry. Through this deployment, we develop a tailored EE model, meticulously analyzing the implications of random base station and user distributions on energy efficiency. We formulate joint base station and user densities that are optimized for EE while adhering to stringent quality-of-service (QoS) requirements. Subsequently, we introduce a novel dynamically distributed opportunistic sleep strategy (D-DOSS) to optimize EE. This strategy strategically clusters base stations throughout the network and dynamically adjusts their sleep patterns based on real-time traffic load thresholds. Employing Monte Carlo simulations with MATLAB, we rigorously evaluate the efficacy of the D-DOSS approach, quantifying improvements in critical QoS parameters, such as coverage probability, energy utilization efficiency (EUE), success probability, and data throughput. In conclusion, our research represents a significant step toward optimizing EE in HetNets, simultaneously addressing network architecture optimization and proposing an innovative sleep management strategy, offering practical solutions to maximize energy efficiency in future wireless networks.
... The ICT sector in general and more precisely the mobile communications sector have a close connection with the UN SDG framework [9]. Facilitating people's lives for a better sustainable future, modern communication network often comes with increased energy consumption as evident in the recent 5G network [10]. In fact, it is speculated that the price paid for this enormous growth of energy consumption will arise even further if no energyefficient method is deployed along with. ...
... In fact, it is speculated that the price paid for this enormous growth of energy consumption will arise even further if no energyefficient method is deployed along with. Several recent surveys estimated that the contribution of global CO 2 emission is nearly 4% and projected to surpass the assessed figure with the further progress of 5G and beyond [9,10]. However, 'UN SDG 13: Climate action which targets net zero emission by 2050' expects the mobile industry to be the first to make positive efforts in this regard. ...
... Evidently, the network energy consumption will also bear an additional price tag for mobile service providers. According to [10], energy price has been projected to be about 10%-15% of the total network Operating Expenses (OPEX) in mature markets that can further amount to 50% of the Operating Expenditure in developing markets [12,13]. India is the fastest-growing telecommunication market globally and one of the key contributors to the CAGR of internet users in Asia Pacific [2,3]. ...
Article
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The recent upsurge of data-demanding applications has necessitated a paradigm shift in deployment scenario in the direction of Multi-tier Ultra-Dense Heterogeneous networks (UDHN), which involve the dense deployment of more than one tier of small cells under-laying traditional macro cellular networks. However, higher data rates and the dense deployment of Small cell eNodeBs (SeNBs) elicit a possible escalation of network energy consumption which stirs up the mobile operators' operating expenditure. To deal with this, primarily, in this work, we present the Strategic Sleeping Policy of the SeNBs based on M/M/1 queuing theory and investigate its impact in reducing the power consumption of the proposed three-tier UDHN which consists of one tier of Macro eNodeB and two tiers of SeNBs based on performance metrics like Energy Efficiency and Area Energy Consumption Ratio. Further, we also introduce a novel Sleep Cycle Modulated Energy Harvesting Technique for SeNBs to ensure proper utilization of energy resources. An analytical model based on Continuous Time Markov Chain is also developed to evaluate the Energy Utilization of the proposed SCMEH method. The comprehensive performance analysis reveals that the implementation of integrated SCMEH enabled SeNBs under HetNet can not only guarantee QoS requirements under concurrent time-varying urban tele-traffic conditions but also ensure Sustainable Green Communication by radically controlling the estimated power consumption per hour basis throughout a day.
... Timely -published in 2020 or later [6], [8], [9], [12], [14], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25] Cloud-RAN [5], [7], [8], [9], [10], [11], [12], [13], [14], [15], [17], [21], [22] Open RAN [20], [21], [22] Virtual RAN [5], [7], [8], [10] , [13], [18], [21] Network sharing Network slicing [7], [8], [13] Sleep modes [23], [24] Power amplifier improvements [24] Artificial Intelligence [5], [7], [8], [20], [23], [25], [26] Renewable energy [5], [22] Energy harvesting [6], [7], [12], [17], [26] B5G and 6G energy saving directions [27], [16], [17], [18], [19] A critical review of litterature Energy savings overview and comparison [24] Overview of research projects [5], [8], [21], [28] to current networks [54]. Results in the area on energy reduction contributions include: [5], [55], [56]. ...
... Timely -published in 2020 or later [6], [8], [9], [12], [14], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25] Cloud-RAN [5], [7], [8], [9], [10], [11], [12], [13], [14], [15], [17], [21], [22] Open RAN [20], [21], [22] Virtual RAN [5], [7], [8], [10] , [13], [18], [21] Network sharing Network slicing [7], [8], [13] Sleep modes [23], [24] Power amplifier improvements [24] Artificial Intelligence [5], [7], [8], [20], [23], [25], [26] Renewable energy [5], [22] Energy harvesting [6], [7], [12], [17], [26] B5G and 6G energy saving directions [27], [16], [17], [18], [19] A critical review of litterature Energy savings overview and comparison [24] Overview of research projects [5], [8], [21], [28] to current networks [54]. Results in the area on energy reduction contributions include: [5], [55], [56]. ...
... Timely -published in 2020 or later [6], [8], [9], [12], [14], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25] Cloud-RAN [5], [7], [8], [9], [10], [11], [12], [13], [14], [15], [17], [21], [22] Open RAN [20], [21], [22] Virtual RAN [5], [7], [8], [10] , [13], [18], [21] Network sharing Network slicing [7], [8], [13] Sleep modes [23], [24] Power amplifier improvements [24] Artificial Intelligence [5], [7], [8], [20], [23], [25], [26] Renewable energy [5], [22] Energy harvesting [6], [7], [12], [17], [26] B5G and 6G energy saving directions [27], [16], [17], [18], [19] A critical review of litterature Energy savings overview and comparison [24] Overview of research projects [5], [8], [21], [28] to current networks [54]. Results in the area on energy reduction contributions include: [5], [55], [56]. ...
Article
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Mobile network traffic is increasing and so is the energy consumption. The Radio Access Network (RAN) part is responsible for the largest share of the mobile network energy consumption, and thus; an important consideration when expanding mobile networks to meet traffic demands. This work analyses how the energy consumption of future mobile networks can be minimised by using the right RAN architecture, share the network with other operators and implementing the most efficient energy minimising technologies in the RAN. It is explored how the different approaches can be realised in real life networks as well as the research state of the art is highlighted. Furthermore, this work provides an overview of future research directions for 6G energy saving potentials. Different energy saving contributions are evaluated by a common methodology for more realistic comparison, based on the potential energy saving of the overall mobile network consumption. Results show that implementing selected technologies and architectures, the mobile network overall energy consumption can be reduced by approximately 30%, corresponding to almost half of the RAN energy consumption. Following this, a set of guidelines towards an energy optimised mobile network is provided, proposing changes to be made initially and in the longer run for brownfield network operators as well as a target network for greenfield network operators.
... SON automates network configuration and adapts to changes, reducing the need for manual intervention and optimizing performance. Nevertheless, the complexity of SON algorithms and increased computational and 36,37,39,47 . RSM involves turning small BSs on or off based on random probabilistic models such as Bernoulli, uniform, Poisson, and exponential processes. ...
... Summary of base station energy-saving techniques. method for enhancing energy efficiency in wireless cellular networks19,[36][37][38][39] . ...
Article
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This research addresses the critical need to optimize the Energy Efficiency (EE) for Ultra-Dense HetNets amid the ever-increasing demands for high-speed data networks. The rapid increase in high-speed devices highlights the urgent necessity for a transformative change in upcoming 5G cellular networks. According to Ericsson's 2022 report, mobile data traffic volume is expected to double by 2027, with mobile video traffic anticipated to rise by nearly 30% each year. In response to these challenges, researchers have identified essential technologies that facilitate 5G networks, including massive Multiple-input-Multiple-output (m-MIMO) systems and HetNets. Although both promise enhanced coverage and throughput, HetNets emerges as a cost-effective solution, surpassing m-MIMO in implementation cost and coverage. However, achieving maximum EE in HetNets necessitates careful consideration of various constraints, including delay, coverage probability, and Signal-to-Interference-plus-Noise Ratio (SINR) thresholds. This research marks a significant milestone in adopting the Distributed Dynamic Opportunistic Sleep Strategy (D-DOSS) approach. The D-DOSS method organizes small clusters throughout the network and evaluates key Quality of Service (QoS) parameters including Energy Utilization Efficiency (EUE), Coverage Probability, Data Throughput, and Success Probability using Monte Carlo simulations. This research analyzes Distributed Sleep (DS) and Centralized Schemes (CS) concerning given QoS parameters. While DS methodologies often exhibit performance trade-offs compared to CS, they provide significant advantages in terms of ease of implementation and management. CS, though representing the most commonly used method in ultra-dense HetNets involves high computational costs that complicate its management. By integrating the D-DOSS and addressing various constraints, this research not only advances HetNet technologies but also makes a significant contribution to optimizing EE while preserving network performance and QoS. The innovative D-DOSS approach offers a promising solution to the challenges of energy efficiency in wireless communication networks and paves the way for future advancements in HetNet deployments. The results and analysis show that D-DOSS effectively addresses the limitations of DS and outperforms existing CS techniques.
... However, they did not dive into potential obstacles and challenges in C-RAN implementation. Authors in [11] discussed energy-efficient sleeping strategies in B5G. Their paper carefully decomposes BSs power consumption, wake-up schedules, and operations in ultra-dense networks. ...
... Note that the fronthaul typically employs optical transport connections to interconnect the RRHs with the cloud, facilitating the transmission of digital baseband signals [18]. The cloud exercises authority over the [6] 2019 Advancement in C-RAN: throughput, energy efficiency Limited exploration of challenges and trade-offs [7] 2017 Challenges for extensive data; power control, scheduling Focus on 4G; lacks specific energy-saving techniques [8] 2015 Spectral and energy efficiency balance; metrics and trade-offs Broad coverage, lacks algorithm examination [1] 2020 Application of machine learning for energy efficiency in 5G Concentrates on ML algorithms for 5G energy efficiency [9] 2018 UN frameworks for sustainable development Neglects SDG energy efficiency component and ICT role [10] 2014 C-RAN technological aspect; energy efficiency and cost Insufficient examination of potential limitations [11] 2021 Sleep mode in 5G; base station power, wake-up schemes Focus on sleep mode algorithms; broader strategies omitted [12] 2011 Environmental conscious measures; RAN efficiency Focus on radio access energy efficiency, no 5G algorithms [13] 2017 Energy-efficiency using dynamic BBU activation and loadbalancing Limited exploration of various algorithms within the context of C-RAN architecture [15] 2009 Energy-efficiency in wireless; strategies and challenges Lacks comprehensive analysis of limitations [16] 2021 Analog-based hybrid predistortion for efficient linearization Focus on predistortion-based linearization [17] 2011 LTE RAN backhaul design; specific requirements and issues Limited examination of non-LTE technologies RRHs and boasts reconfigurability, which implies that the quantity of BBUs can be altered over time. Functioning as virtual Base Stations (vBSs), the cloud undertakes baseband processing using general-purpose processors. ...
Preprint
Full-text available
Cloud Radio Access Network (C-RAN) is considered as a catalyst in increasing energy efficiency in 5G networks by centralizing baseband units that are interconnected to remote radio heads (RRHs). This technology allows to split functions among baseband and radio to significantly optimize performance across the network (lower latency, function offload). However, the C-RAN framework offers several split options, each of which carries specific advantages and disadvantages, when it comes to energy efficiency considerations. In this work, we investigate C-RAN systems in 5G and beyond networks (B5G), with a particular focus on energy efficiency specifications. At first, the study examines the measurable energy-related benefits offered by C-RAN to confirm its importance through operational results. Then, we explore the available C-RAN models in detail, while critically analyzing them, to assess their energy efficiency. The study goes on to discuss the challenges and strategies of choosing the right C-RAN model. Finally, our study concludes that there is no one-size-fits-all approach, when it comes to C-RAN strategies, as their effectiveness depends on variables such as network load, RRH level, energy cost, etc. However, in regard to energy efficiency in B5G, we underline the promising potential of C-RAN to significantly improve the energy efficiency.
... However, they did not dive into potential obstacles and challenges in C-RAN implementation. Authors in [11] discussed energy-efficient sleeping strategies in B5G. Their paper carefully decomposes BSs power consumption, wake-up schedules, and operations in ultra-dense networks. ...
... Note that the fronthaul typically employs optical transport connections to interconnect the RRHs with the cloud, facilitating the transmission of digital baseband signals [18]. The cloud exercises authority over the [6] 2019 Advancement in C-RAN: throughput, energy efficiency Limited exploration of challenges and trade-offs [7] 2017 Challenges for extensive data; power control, scheduling Focus on 4G; lacks specific energy-saving techniques [8] 2015 Spectral and energy efficiency balance; metrics and trade-offs Broad coverage, lacks algorithm examination [1] 2020 Application of machine learning for energy efficiency in 5G Concentrates on ML algorithms for 5G energy efficiency [9] 2018 UN frameworks for sustainable development Neglects SDG energy efficiency component and ICT role [10] 2014 C-RAN technological aspect; energy efficiency and cost Insufficient examination of potential limitations [11] 2021 Sleep mode in 5G; base station power, wake-up schemes Focus on sleep mode algorithms; broader strategies omitted [12] 2011 Environmental conscious measures; RAN efficiency Focus on radio access energy efficiency, no 5G algorithms [13] 2017 Energy-efficiency using dynamic BBU activation and loadbalancing Limited exploration of various algorithms within the context of C-RAN architecture [15] 2009 Energy-efficiency in wireless; strategies and challenges Lacks comprehensive analysis of limitations [16] 2021 Analog-based hybrid predistortion for efficient linearization Focus on predistortion-based linearization [17] 2011 LTE RAN backhaul design; specific requirements and issues Limited examination of non-LTE technologies RRHs and boasts reconfigurability, which implies that the quantity of BBUs can be altered over time. Functioning as virtual Base Stations (vBSs), the cloud undertakes baseband processing using general-purpose processors. ...
Conference Paper
Full-text available
Cloud Radio Access Network (C-RAN) is considered as a catalyst in increasing energy efficiency in 5G networks by centralizing baseband units that are interconnected to remote radio heads (RRHs). This technology allows to split functions among baseband and radio to significantly optimize performance across the network (lower latency, function offload). However, the C-RAN framework offers several split options, each of which carries specific advantages and disadvantages, when it comes to energy efficiency considerations. In this work, we investigate C-RAN systems in 5G and beyond networks (B5G), with a particular focus on energy efficiency specifications. At first, the study examines the measurable energy-related benefits offered by C-RAN to confirm its importance through operational results. Then, we explore the available C-RAN models in detail, while critically analyzing them, to assess their energy efficiency. The study goes on to discuss the challenges and strategies of choosing the right C-RAN model. Finally, our study concludes that there is no one-size-fits-all approach, when it comes to C-RAN strategies, as their effectiveness depends on variables such as network load, RRH level, energy cost, etc. However, in regard to energy efficiency in B5G, we underline the promising potential of C-RAN to significantly improve the energy efficiency.
... In this section, these works reviewed and categorized based on their primary contributions to CSO. It's worth noting that, as seen in previous survey papers [4][5][6], additional categories can be established based on algorithm parameters, including online/offline, BSs traffic awareness, UE 5 location awareness, and centralized/distributed approaches. Each of these categories has its own set of advantages and disadvantages. ...
... The pathloss model, L −1 j,k , is defined as presented in Eq. (4) [13]: d indoor is the minimum distance between the user and BS, is assumed to be 0.5 m. d j,k is the Euclidean distance between f j and user k , which is calculated using Eq. (5). The equation is commonly used in wireless communication to estimate the attenuation of the signal as it propagates over a certain distance, with an additional term accounting for indoor effects. ...
Article
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Energy efficiency is regarded as a critical concern in modern cellular networks, primarily due to the increasing demand for wireless communication services and the environmental impact associated with energy consumption. In this paper, an innovative approach named ACSO (Adaptive Cell Switch Off) is proposed to optimize energy efficiency in cellular networks. It focusses on leveraging clustering techniques and dynamic CSO (Cell Switch Off) strategies. In this approach, clustering of BSs (Base Station) is performed based on their traffic profiles, and within each cluster, an optimal set of BSs is determined to be switched off at different time intervals. By considering the characteristics of network traffic and spatial distribution, an attempt is made to strike a balance between energy conservation and the maintenance of high QoE (Quality of Experience) for users. Extensive simulations using a real-world dataset are conducted to compare our approach with existing methods, and superior performance is demonstrated in terms of efficient BS selection, algorithm execution time, and user QoE. Additionally, potential future research directions in the area of dynamic CSO are discussed, along with its implications for the development of energy-efficient cellular networks.
... To reduce power consumption, they demonstrate the trade-off between power consumption, blocking probability, and mean delay. Salahdine et al. [20] proposed a comprehensive survey of the BSs sleeping strategy for ultra-dense networks in 5G to reduce power consumption. ...
... Many authors proposed various methods to reduce power consumption in the 5G BSs. Salahdine et al. [20] proposed a comprehensive survey of the BSs sleeping strategy for ultra-dense networks in 5G to reduce power consumption. ...
Article
Full-text available
Base stations (BSs) sleeping strategy has been widely analyzed nowadays to save energy in 5G cellular networks. 5G cellular networks are meant to deliver a higher data speed rate, ultra-low latency, more reliability, massive network capacity, more availability, and a more uniform user experience. In 5G cellular networks, BSs consume more power which is about 4 times that of 4G. To reduce average power consumption and save power in 5G, we have modelled the 5G BSs sleeping mechanism as an M/G/1 queue with two types of vacations (two different sleep modes), idle period (close-down), and set-up periods. Based on the traffic load, the BSs adjust their transmitting power in the active state, idle state (close down state), sleep mode 1 (type 1 vacation), sleep mode 2 (type 2 vacation) and set-up state. The length of sleep mode 1 is smaller than the length of sleep mode 2. Sleep mode 1 consists of a maximum M sleeps. When the BSs are in sleep mode or shut off, they will experience a state delay. To overcome this delay, it is necessary to optimize sleep in sleep mode 1 considering a small amount of set-up time. To optimize the maximum sleeps in sleep mode 1, the tradeoff between power consumption/power-saving and throughput is shown. Finally, the trade-off between power consumption and saving is presented to get the energy efficiency from 5G BSs. Without finding the energy efficiency i.e., optimal power consumption and power saving in 5G BSs, it will not be possible to say how much the model is effective for use in 5G BSs.
... Most of the existing strategies focus on the temporal dimension, and achieve energy saving by shuting down inactive or low-load base stations during non-peak times 9 . The state-of-the-art of such strategies involve complex shutdown oprations at the cell, carrier, and symbol levels 10,11 , and, in yielding considerable energy savings, their energy-saving effect has appeared to be reaching the theoretical and operational limit today 12,13 . Therefore, it is imperative to explore additional energy-saving potential of the MCN, not only for global carbon emission reduction, but also for the sustainability of the 5G network. ...
... , the total status quo power of the MCN is 998,600W; while those in the two scenarios are 950,029W (4.86% energy savings) and 751,675W (24.73% energy savings), respectively. The figures, especially the latter, compare favorably with the energy savings obtained by the currently mainstream temporal shutdown operations[11][12][13] . ...
Preprint
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Improving energy efficiency is vital for the 5G mobile telecommunication network, while the conventional temporal shutdown strategy is exhausting its energy-saving potential. Inspired by the symmetricity between the temporal peaks and valleys and the geographical highlands and lowlands in network traffic, we propose a new strategy for telecom energy-saving pivoting spatial optimization. We showed by the maximum entropy principle that traffic should follow highly skewed spatial distribution forms, and empirically tested that the probability density function of 4G network traffic typically has a power-law form with an expotential parameter no less than 2 in absolute value. Also, the high-traffic areas form stable highlands which enable spatial optimization of base station locations for energy-saving. A simplified model hence built yields up to 19.59% energy savings for the 4G network, and 29.68% for a hypothetical 5G network, prominent margins over what the temporal shutdown approach would gain.
... This graph representation should be considered as a dynamic graph; after cell switching, the state of all node and edge features change to x v and â u,v , following the change of to ˆ and the resultant P BS for all BSs calculated by Eqs. (7), (8), and (2). It should be recognized that other graph representation designs may have differentiated learning outcomes combined with different GNN models. ...
... • Power consumption P tot : This is the HetNet unit's instantaneous power consumption during a day defined in Eq. (1) for each method calculated based on Eq. (2). Measured in Watts (W), this metric evaluates the performance of each solution as it reflects the variations in network power consumption in different time slots of the day. ...
Article
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The development of ultra-dense heterogeneous networks (HetNets) will cause a significant rise in energy consumption with large-scale base station (BS) deployments, requiring cellular networks to be more energy efficient to reduce operational expense and promote sustainability. Cell switching is an effective method to achieve the energy efficiency goals, but traditional heuristic cell switching algorithms are computationally demanding with limited generalization abilities for ultra-dense HetNet applications, motivating the usage of machine learning techniques for adaptive cell switching. Graph neural networks (GNNs) are powerful deep learning models with strong generalization abilities but receive little attention for cell switching. This paper proposes a GNN-based cell switching solution (GBCSS) that has a smaller computational complexity than existing heuristic algorithms. The presented performance evaluation uses the Milan telecommunication dataset based on real-world call detail records, comparing GBCSS with a traditional exhaustive search (ES) algorithm, a state-of-the-art learning-based algorithm, and the baseline without cell switching. Results indicate that GBCSS achieves a 10.41% energy efficiency gain when compared with the baseline and achieves 75.76% of the optimal performance obtained with ES algorithm. The results also demonstrate GBCSS’ significant scalability and generalization abilities to differing load conditions and the number of BSs, suggesting this approach is well-suited to ultra-dense HetNet deployment.
... Recently, the exponential growth of the numerous wireless devices and the datahungry applications have earned huge significance. This required imperious expansion of the 5G network to support the forthcoming 5G use cases, such as video live-streaming, conferencing, online gaming, etc. [1,2]. Moreover, the 5G cellular network is planned to elevate the capacity 1000 times and the spectrum efficiency by 5-15 times with respect to 4G [3,4]. ...
... This can be achieved by utilizing heterogeneous networks (HetNets), which can enhance the system data rates and the quality of service (QoS) of the users as the small cells (SCs) are deployed within the macro cells (MCs) coverage area. Furthermore, SCs offer the benefit of providing service to previously uncovered regions and in the network regions demanding larger capacity [2,3,[5][6][7]. Figure 1 shows a general representation of the HetNet scenario with the MCs underlaid by the densely deployed SCs. ...
Article
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The dense deployment of small cells (SCs) in the 5G heterogeneous networks (HetNets) fulfills the demand for vast connectivity and larger data rates. Unfortunately, the power efficiency (PE) of the network is reduced because of the elevated power consumption of the densely deployed SCs and the interference that arise between them. An approach to ameliorate the PE is proposed by switching off the redundant SCs using machine learning (ML) techniques while sustaining the quality of service (QoS) for each user. In this paper, a linearly increasing inertia weight–binary particle swarm optimization (IW-BPSO) algorithm for SC on/off switching is proposed to minimize the power consumption of the network. Moreover, a soft frequency reuse (SFR) algorithm is proposed using classification trees (CTs) to alleviate the interference and elevate the system throughput. The results show that the proposed algorithms outperform the other conventional algorithms, as they reduce the power consumption of the network and the interference among the SCs, ameliorating the total throughput and the PE of the system.
... The concept of cell sleeping for energy saving has been extensively explored in the literature [33]. For instance, the authors in [34] address the joint problem of cell clustering and BS sleeping, while [35] proposes a joint strategy for cell sleeping and interference coordination. ...
Preprint
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The high energy footprint of 5G base stations, particularly the radio units (RUs), poses a significant environmental and economic challenge. We introduce Kairos, a novel approach to maximize the energy-saving potential of O-RAN's Advanced Sleep Modes (ASMs). Unlike state-of-the-art solutions, which often rely on complex ASM selection algorithms unsuitable for time-constrained base stations and fail to guarantee stringent QoS demands, Kairos offers a simple yet effective joint ASM selection and radio scheduling policy capable of real-time operation. This policy is then optimized using a data-driven algorithm within an xApp, which enables several key innovations: (i) a dimensionality-invariant encoder to handle variable input sizes (e.g., time-varying network slices), (ii) distributional critics to accurately model QoS metrics and ensure constraint satisfaction, and (iii) a single-actor-multiple-critic architecture to effectively manage multiple constraints. Through experimental analysis on a commercial RU and trace-driven simulations, we demonstrate Kairos's potential to achieve energy reductions ranging between 15% and 72% while meeting QoS requirements, offering a practical solution for cost- and energy-efficient 5G networks.
... Concerns over the carbon footprint of 6G networks have arisen because of the deployment of tiny cells, large MIMO (multiple input, multiple output) antennas, and other infrastructure components that require increasing power usage [41]. We need innovative strategies to address this problem [27], such as adaptive sleep modes [42], dynamic energy management, and the integration of energy from renewable sources such as wind and solar energy into the network architecture [43]. ...
Article
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Wireless communication has revolutionized the evolution of humankind. The rapid growth and development of mobile communication has created an ecosystem better than what has been before. However, issues such as ample energy consumption and resulting carbon emissions, a lack of proper disposal mechanisms for large amounts of electronic waste, and the recycling of electronic materials interrupt growth. When the world is waiting for the implementation of 6G mobile communication technology, it is mandatory to resolve these issues for the sustainability of 6G technology. In this review, we present the superiority of 6G over previous generations accompanied by issues that cause extensive damage to the environment. To mitigate this adverse effect, we present a lifecycle analysis of 6G wireless communication technology from production to disposal, focusing on issues surrounding electronic waste, energy consumption, and environmental impact. This study explains the intricacies of electronic parts, toxic compounds, and the dangers of incorrect disposal techniques. It also investigates energy consumption issues specific to 6G technology, such as manufacturing processes and network infrastructures that require considerable energy. We also present a quantitative evaluation of the 6G lifecycle in detail. In addition, we present a comprehensive strategy and insights to make 6G sustainable. Furthermore, we suggest an ecological policy for all stakeholders for the sustainability of 6G. We also present political and commercial implications for 6G. As the process of 6G development continues, we show the impact of network fragmentation on standardization, which helps improve sustainability. Finally, we conclude that while the existing research has made significant advances in 6G, there is a need for correct disposal techniques to refine the key government policies for managing e-waste. New cooling technologies and renewable energy sources must be adopted to reduce the current greenhouse emission of 200 g of CO2 and energy consumption of 2.5 kWh per GB for 6G networks.
... The authors of [17] discussed several innovative solutions to reduce power consumption in wireless networks, including cloud architectures, renewable energy, and smart sleeping modes. In a recent study, the authors of [22] presented the different BS power consumption models, energy-saving measurements, and performance evaluation metrics. The paper further reviewed the BS sleep wake-up schemes in Ultra-Dense Networks (UDN). ...
... With the advancement of information and communication technologies (ICT), fifthgeneration mobile communication technology (5G) offers high-bandwidth, high-capacity, and low-latency communication [1], exerting significant economic benefits and profound social impacts on the development of the digital economy [2], smart cities [3], and intelligent manufacturing [4]. Currently, 5G network coverage is continuously expanding, while the energy consumption of 5G base stations is rapidly increasing [5]. ...
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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.
... In this context, both environmental sustainability and operational expenditures are two major issues in Information and Communication Technologies (ICT) (Freitag et al. 2021) in general, and in 5G/6G UDNs in particular (Mughees et al. 2021;Salahdine et al. 2021), as base stations account for 60% to 80% of the power consumption of the cellular network (Yao et al. 2019) and more than 1000 BSs/km 2 are expected to be deployed (Lopez-Perez et al. 2015;Stoynov et al. 2023). This is especially critical in periods of low or no traffic demands, when most of the SBSs are not serving any user. ...
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... To meet the higher data rate and reliable connectivity, service providers are increasingly using macro-cells, pico-cells, femto-cells, and micro-cells in ultra dense small cell networks (UDSCNs). 17 In a recent survey, [18][19][20] it is found that more than 50% of the energy is consumed at the base station for wireless communication. Hence, the s-BS sleep strategy is used to minimize power consumption, and reduction in the deployment of s-BSs. ...
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... As a result, HO optimization appears to be a feasible strategy for improving network performance. [24][25][26] ...
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... Thus, the hardware may waste energy when the network traffic is in a low state. The BS sleep strategy aims to sleep or shut down some underutilized hardware when the load is lower than a certain threshold [37]. Accordingly, searching for cells that are under low load for a long time can help operators optimize sleep strategies. ...
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... Carrier shutdown is mentioned as a promising technique for reducing the power consumption at the base station in several recent technological surveys such as [1], [4], [5] and industry white papers as [6], [7]. A similar approach allows the base station to adapt the bandwidth to the traffic needs via the concept of bandwidth part, without the need of powering off the whole carrier, as described in [8], [3]. ...
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By shutting down frequency carriers, the power consumed by a base station can be considerably reduced. However, this typically comes with traffic performance degradation, as the congestion on the remaining active carriers is increased. We leverage a hysteresis carrier shutdown policy that attempts to keep the average traffic load on each sector within a certain min/max threshold pair. We propose a closed-loop Bayesian method optimizing such thresholds on a sector basis and aiming at minimizing the power consumed by the power amplifiers while maintaining the probability that KPI's are acceptable above a certain value. We tested our approach in a live customer 4G network. The power consumption at the base station was reduced by 11% and the selected KPI's met the predefined targets.
... Furthermore, with macro-BS muting considered, ρ rises significantly and can reach a value of 1 both empirically and theoretically, leading to interference minimization. When mmWave frequencies are employed, very high densification gains (greater than 1) may be attainable [19]. Since transmission at these frequencies is often noise-limited, the combination of UDN and high frequency signals has the potential to considerably increase SINR. ...
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... The BS sleep strategy aims to sleep or shut down some underutilized hardware when the load is lower than a certain threshold. [28] Accordingly, searching for cells that are under low load for a long time can help operators to optimize sleep strategies. ...
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Cellular networks have been widely deployed and are under ever-growing communication pressure. Detecting traffic hotspots or other essential characteristics of network traffic distributions can help to adjust the base station control strategies of networks to save energy. Generally, existing approaches detect these characteristics by data analysis techniques, making the detection process generally inefficient and not automatic. Moreover, those approaches are also difficult to describe the dynamic spatio-temporal evolution characteristics of network traffics. In this paper, we propose a novel modeling and analysis approach by applying the spatio-temporal model checking technique to the detection of network traffic characteristics. First, we model the spatio-temporal evolution process of cellular network traffic by closure space model. Second, we give the logical characterizations of detection requirements by suitable Spatio-Temporal Logic of Closure Space (STLCS) formulas. Third, we verify the spatio-temporal properties in the closure space model by model checking algorithms. The experiments are illustrated on the Milan network traffic dataset and indicate that our approach can automatically and effectively detect desirable spatio-temporal properties of cellular network traffic.
... Several techniques have been used in literature to achieve EE in wireless networks, such as adaptive base station (BS) sleep control [3], beamforming [4], and energy-efficient user association and resource allocation (UARA) [5]. Similar to [5], this work focuses on improving the EE in the ITNTN through energy-efficient resource allocation. ...
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... 5G network and beyond architecture has been presented by discussing the potential key emerging technologies offered by the new infrastructure including massive MIMO, NFV, SDN, D2D, M2M, mmWaves, network slicing, ultra-dense, ultra-reliable, cloud-RAN, interferences managements, full duplex communication, energy harvesting, spectrum sharing, and resource allocation. 140 We provided a comprehensive investigation on security challenges in 5G key technologies. We discussed some of the open challenges and future directions for further research investigation. ...
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