Q-learning based Radio Resource Adaptation for Improved Energy Performance of 5G Base Stations

Peesapati, S. Krishna Gowtam; Olsson, Magnus; Masoudi, Meysam; Andersson, Sören; Çavdar, Çiçek

doi:10.1109/pimrc50174.2021.9569420

Cited by 14 publications

(9 citation statements)

References 16 publications

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“…Recent work in [129] proposes a ML mechanism that provides new sleep mode functionalities, where the greedy algorithm can save up to almost 80 % of the energy. The work in [128], [112] and [129] do, however; not explain where in the network, the energy savings are achieved.…”

Section: A Technology Improvementsmentioning

confidence: 97%

“…Hence, the current 3GPP NR architecture introduces the Network Data Analytics Function (NWDAF) and the Management Data Analytics Function (MDAF) [23]. Within reinforcement learning, the Q-model is used in [126], [112], [127] and [128]. In [128], the authors utilise the Q-model to adapt a base station's resources according to the traffic demand, by optimising the use of sleep modes.…”

Section: A Technology Improvementsmentioning

confidence: 99%

“…Within reinforcement learning, the Q-model is used in [126], [112], [127] and [128]. In [128], the authors utilise the Q-model to adapt a base station's resources according to the traffic demand, by optimising the use of sleep modes. Results in [128] reports on up to 16% energy consumption savings.…”

Section: A Technology Improvementsmentioning

confidence: 99%

“…In [128], the authors utilise the Q-model to adapt a base station's resources according to the traffic demand, by optimising the use of sleep modes. Results in [128] reports on up to 16% energy consumption savings. Authors in [127] use a framework for effective strategy selection and optimisation policies and obtains up to 20% energy savings in a high load scenario.…”

Section: A Technology Improvementsmentioning

confidence: 99%

See 3 more Smart Citations

Toward Greener 5G and Beyond Radio Access Networks—A Survey

Larsen

Christiansen

Ruepp

et al. 2023

IEEE Open J. Commun. Soc.

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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.

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Section: A Technology Improvementsmentioning

confidence: 97%

Section: A Technology Improvementsmentioning

confidence: 99%

Section: A Technology Improvementsmentioning

confidence: 99%

Section: A Technology Improvementsmentioning

confidence: 99%

See 2 more Smart Citations

Toward Greener 5G and Beyond Radio Access Networks—A Survey

Larsen

Christiansen

Ruepp

et al. 2023

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

“…However, they train their network on a fixed traffic pattern. The authors in [21] use reinforcement learning to adjust the BSs' configurations, e.g., bandwidth and MIMO parameters, to increase the sleeping time of BS. Since control and signaling can limit the energy saving of ASMs, the study in [22] proposes a control/data plane separation in 5G BSs which allows implementing SMs with longer durations.…”

mentioning

confidence: 99%

Digital Twin Assisted Risk-Aware Sleep Mode Management Using Deep Q-Networks

Masoudi

Soroush²,

Zander

et al. 2023

IEEE Trans. Veh. Technol.

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Base stations (BSs) are the most energy-consuming segment of mobile networks. To reduce BS energy consumption, different components of BSs can sleep when BS is not active. According to the activation/deactivation time of the BS components, multiple sleep modes (SMs) are defined in the literature. In this study, we model the problem of BS energy saving utilizing multiple sleep modes as a sequential Markov decision process (MDP) and propose an online traffic-aware deep reinforcement learning approach to maximize the long-term energy saving. However, there is a risk that BS is not sleeping at the right time and incurs large delays to the users. To tackle this issue, we propose to use a digital twin model to encapsulate the dynamics underlying the investigated system and estimate the risk of decision-making (RDM) in advance. We define a novel metric to quantify RDM and predict the performance degradation. The RDM calculated by DT is compared with a tolerable threshold set by the mobile operator. Based on this comparison, BS can decide to deactivate the SMs, re-train when needed to avoid taking high risks, or activate the SMs to benefit from energy savings. For deep reinforcement learning, we use long-short term memory (LSTM), to take into account the long and short-term dependencies in input traffic, and approximate the Q-function. We train the LSTM network using the experience replay method over a real traffic data set obtained from an operator's BS in Stockholm. The data set contains data rate information with very coarse-grained time granularity. Thus, we propose a scheme to generate a new data set using the real network data set which 1) has finer-grained time granularity and 2) considers the bursty behavior of traffic data. Simulation results show that using proposed methods, considerable energy saving is obtained, compared to the baselines at cost of negligible number of delayed users. Moreover, the proposed digital twin model can predict the performance of the DQN proactively in terms of RDM hence preventing the performance degradation in the network in anomalous situations.

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Stochastic Modeling of a Base Station in 5G Wireless Networks for Energy Efficiency

Aggarwal,

Selvamuthu,

Gangey

et al. 2024

Int J Communication

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The potential benefits of 5G networks, such as faster data speeds and improved user experiences, come with a critical challenge—efficiently preserving energy in base stations (BSs). Addressing this challenge is crucial, necessitating a focus on maximizing the energy efficiency of these stations. BSs play a vital role in providing coverage and capacity by using different frequency bands adapting to diverse communication needs. The choice of a specific frequency band can impact network performance, data rates, coverage range, and signal propagation. This study emphasizes the crucial challenge of preserving energy in 5G BSs and underscores the significance of strategic frequency band selection for optimizing energy efficiency and network performance. For this, stochastic models are introduced for BSs, specifically the Markov and semi‐Markov models, which help choose frequency bands based on system traffic. Closed‐form solutions for steady‐state system size probabilities in these models are derived. A sensitivity analysis is conducted to assess power consumption in various scenarios. Furthermore, a comparison between analytical findings and simulation outcomes regarding power consumption is presented. The convergence of results in both methodologies emphasizes a significant level of agreement; that is, the introduced stochastic models provide insights and are validated by a close alignment between analytical and discrete‐event simulation outcomes. We have shown the behavior of power consumption with respect to three different distributions named deterministic, exponential, and hypo‐exponential. This research highlights the importance of strategic frequency band selection for 5G BSs to optimize energy efficiency and meet the demands of evolving communication networks.

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Q-learning based Radio Resource Adaptation for Improved Energy Performance of 5G Base Stations

Cited by 14 publications

References 16 publications

Toward Greener 5G and Beyond Radio Access Networks—A Survey

Toward Greener 5G and Beyond Radio Access Networks—A Survey

Digital Twin Assisted Risk-Aware Sleep Mode Management Using Deep Q-Networks

Stochastic Modeling of a Base Station in 5G Wireless Networks for Energy Efficiency

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