Mobility Management With Transferable Reinforcement Learning Trajectory Prediction

Zhao, Zhongliang; Karimzadeh, Mostafa; Pacheco, Lucas; Santos, Hugo; Rosário, Denis; Braun, Torsten; Cerqueira, Eduardo

doi:10.1109/tnsm.2020.3034482

Cited by 13 publications

(5 citation statements)

References 39 publications

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“…Due to the large coverage of an eNBs node, such a broad trajectory is not conducive to accurate prediction. [23] considered using migration learning to customize a trajectory prediction model for each user. But as the number of users increases, The costs resulting from the model will be a part that cannot be ignored.…”

Section: Related Workmentioning

confidence: 99%

Service migration in MEC: an Approach Based on Deep Reinforcement Learning and Fuzzy Logic

Zhang,

Bu,

Wang

2023

Preprint

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As a new computing paradigm, Mobile Edge Computing (MEC) sinks resources to the network edge to support real-time responses of services. Facing the demand for migrating services caused by the frequent movement of the mobile device, it is a great challenge to reduce the operation costs of migrating services with user requirements satisfied, especially for the continuous optimization of services deployed in hot spot areas. In order to address the above challenge, we propose the mechanism of dynamically migrating and optimally deploying services, which optimizes the latency and energy consumption of the service migration. Firstly, we introduce Markov Decision Process (MDP) to model changes of deployment locations of the single-user service, and devise the approach to determine whether to migrate a service at the current network status based on the dueling Deep Q-Network (DQN). Secondly, we propose the service pre-migration decision approach to determine where to migrate a service by combining the Long Short-Term Memory (LSTM) network and the attention mechanism, so as to determine the future moving area of the user and perform the migration decision in advance with the impact of migration service on service quality considered. Finally, we devise the distance-weighted matching algorithm based on the fuzzy logic to further optimize the selection of the most appropriate services to migrate in hotspot areas and the corresponding service migration regions, thus, the load pressure of the edge computing node caused by the imbalanced deployment density of service among multiple regions. Simulation results show that the proposed mechanism has significant improvements in overall operating costs compared with the cur-rent state of the art.

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Section: Related Workmentioning

confidence: 99%

Service migration in MEC: an Approach Based on Deep Reinforcement Learning and Fuzzy Logic

Zhang,

Bu,

Wang

2023

Preprint

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“…DL-based approaches can be an alternative solution, as shown in Figure 10 . Zhao et al [ 63 ] proposed a mobile user trajectory prediction algorithm by combining LSTM with RL. LSTM is used to predict the trajectories of mobile users, whereas RL is used to improve the model training time of LSTM by finding the most accurate neural network architecture for the given problem without human intervention.…”

Section: Artificial Intelligence (Ai)-enabled 6g Networkmentioning

confidence: 99%

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Ismail

Buyya

2022

Sensors

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The recent upsurge of smart cities’ applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations’ development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications’ systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications.

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“…Simultaneously, with more mobility datasets available, it becomes possible and feasible to deploy location prediction services in many network applications to enable proactive mobility management, handover optimization, content migration, and resource management. Zhao et al [23] proposed a proactive mobility management approach based on group user trajectory prediction by combining LSTM with Reinforcement Learning (RL) to automate the model training procedure. Ding et al [24] proposed a multi-user multi-order Markov model and a multi-modal user mobility pattern prediction approach.…”

Section: B User Mobility and Traffic Flow Predictionmentioning

confidence: 99%

Predictive UAV Base Station Deployment and Service Offloading With Distributed Edge Learning

Zhao

Pacheco

Santos

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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In modern networks, edge computing will be responsible for processing and learning from the critical networkand user-generated data, such as wireless link usage, mobility information, application requests, and many others. The presence of Artificial Intelligence-based (AI) applications at the edge of the network will enable the network to predict necessary user behavior and its impact on network infrastructure, such as base station overloading. One of the main strategies for offloading users and base stations is to deploy UAV base stations, or flying base stations, which can dynamically provide service and connectivity. In this article, we introduce a framework for distributed learning over Multi-access Edge Computing (MEC), which manages data applications in a fully distributed setting across edge servers, thus reducing the cost of collecting user information in a centralized server. We couple the proposed distributed learning with a novel similarity metric for user trajectories, which can aggregate neural network models with similar costs as other model aggregation techniques. However, the aggregation technique can achieve much higher accuracy. Furthermore, we apply the proposed distributed learning scheme to manage and deploy flying base stations to areas that experience high demand or poor user connectivity, thus optimizing connectivity in terms of user satisfaction, delay, and network throughput.

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Mobility Management With Transferable Reinforcement Learning Trajectory Prediction

Cited by 13 publications

References 39 publications

Service migration in MEC: an Approach Based on Deep Reinforcement Learning and Fuzzy Logic

Service migration in MEC: an Approach Based on Deep Reinforcement Learning and Fuzzy Logic

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Predictive UAV Base Station Deployment and Service Offloading With Distributed Edge Learning

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