Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control

• URL: http://arxiv.org/abs/2101.08391v1
• Date: Thu, 21 Jan 2021 01:39:42 GMT
• Title: Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control
• Authors: Qiong Wu and Xu Chen and Zhi Zhou and Liang Chen and Junshan Zhang
• Abstract summary: 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. 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
• Score: 39.31623488192675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• TEAM: Topological Evolution-aware Framework for Traffic Forecasting--Extended Version [24.544665297938437]
  Topological Evolution-aware Framework (TEAM) for traffic forecasting incorporates convolution and attention. TEAM is capable of much lower re-training costs than existing methods are without jeopardizing forecasting accuracy.
  arXiv  Detail & Related papers  (2024-10-24T22:50:21Z)
• Deep Learning-driven Mobile Traffic Measurement Collection and Analysis [0.43512163406552007]
  In this thesis, we harness the powerful hierarchical feature learning abilities of Deep Learning (DL) techniques in both spatial and temporal domains. We develop solutions for precise city-scale mobile traffic analysis and forecasting.
  arXiv  Detail & Related papers  (2024-10-14T06:53:45Z)
• Towards Energy-Aware Federated Traffic Prediction for Cellular Networks [2.360352205004026]
  We propose a novel sustainability indicator that allows assessing the feasibility of machine learning (ML) models. We evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint.
  arXiv  Detail & Related papers  (2023-09-19T14:28:09Z)
• Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning [60.17407932691429]
  Open Radio Access Network systems, with their base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability. We propose an online learning algorithm that balances the effective throughput and vBS energy consumption, even under unforeseeable and "challenging'' environments. We prove the proposed solutions achieve sub-linear regret, providing zero average optimality gap even in challenging environments.
  arXiv  Detail & Related papers  (2023-09-04T17:30:21Z)
• AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles [61.21359293642559]
  The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies. We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
  arXiv  Detail & Related papers  (2022-03-05T10:54:05Z)
• A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization [14.455497228170646]
  Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. This paper first proposes a multi-agent deep deterministic policy gradient (MADDPG) method by extending the actor-critic policy gradient algorithms.
  arXiv  Detail & Related papers  (2021-07-13T14:11:04Z)
• Computation Offloading in Heterogeneous Vehicular Edge Networks: On-line and Off-policy Bandit Solutions [30.606518785629046]
  In a fast-varying vehicular environment, the latency in offloading arises as a result of network congestion. We propose an on-line algorithm and an off-policy learning algorithm based on bandit theory. We show that the proposed solutions adapt to the traffic changes of the network by selecting the least congested network.
  arXiv  Detail & Related papers  (2020-08-14T11:48:13Z)
• Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks [151.65541208130995]
  A drone base station (DBS) is dispatched to provide uplink connectivity to ground users whose demand is dynamic and unpredictable. In this case, the DBS's trajectory must be adaptively adjusted to satisfy the dynamic user access requests. A meta-learning algorithm is proposed in order to adapt the DBS's trajectory when it encounters novel environments.
  arXiv  Detail & Related papers  (2020-05-25T20:43:59Z)
• Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach [88.45509934702913]
  We design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed. We incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time.
  arXiv  Detail & Related papers  (2020-02-21T07:29:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.