Cooperative Caching and Transmission in CoMP-Integrated Cellular Networks Using Reinforcement Learning

Lin, Pinpin; Song, Qingyang; Jun, Song; Jamalipour, Abbas; Yu, F. Richard

doi:10.1109/tvt.2020.2981657

Cited by 54 publications

(21 citation statements)

References 31 publications

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“…Single and joint transmission of nodes are considered in [117] where storage-and transmission-level cooperation is exploited to optimize content caching and updating for video delivery. The authors formulate the problem, as an MDP where the reward is mapped to the level of delay reduction.…”

Section: ) Delay Reductionmentioning

confidence: 99%

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

et al. 2022

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Mobile networks experience a tremendous increase in data volume and user density due to the massive number of coexisting users and devices. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting cache-aided edge nodes, such as fixed and mobile access points, and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers new opportunities for network optimization when traditional optimization approaches fail or incur high complexity. Among the various machine learning categories, reinforcement learning provides autonomous operation without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching solutions are presented and classified, based on the networking architecture and optimization target. As sixth generation (6G) networks will be characterized by high heterogeneity, fixed cellular, fog, cooperative, vehicular, and aerial networks are studied. The discussion of these works reveals that there exist reinforcement learning-aided caching schemes with varying complexity that can surpass the performance of conventional policy-based approaches. Finally, several open issues are presented, stimulating further interest in this important research field.INDEX TERMS 6G, edge caching, heterogeneous networks, machine learning, mobile edge networks, reinforcement learning.

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Section: ) Delay Reductionmentioning

confidence: 99%

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

et al. 2022

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“…Its structure is mostly multilayer perceptron with multiple hidden layers, which is easy to find the deeper rules hidden in the data and has strong feature extraction ability [51]. Typical deep learning [52] models include Convolutional Neural Network (CNN), deepbelief net and stacked autoencoder network. In the medical field, ML technology can predict and diagnose diseases, which largely avoids the high error, low efficiency and the emergence of major diseases of artificial diagnosis.…”

Section: ) Artificial Intelligence (Ai)mentioning

confidence: 99%

Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

et al. 2020

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With the booming development of medical informatization and the ubiquitous connections in the fifth generation mobile communication technology (5G) era, the heterogeneity and explosive growth of medical data have brought huge challenges to data access, security and privacy, as well as information processing in Internet of Medical Things (IoMT). This article provides a comprehensive review of how to realize the timely processing and analysis of medical big data and the sinking of high-quality medical resources under the constraints of the existing medical environment and medical-related equipment. We mainly focus on the advantages brought by the cloud computing, edge computing and artificial intelligence technologies to the IoMT. We also explore how to rationalize the use of medical resources and the security and privacy of medical data, so that high-quality medical services can be provided to patients. Finally, we discuss the current challenges and possible future research directions in the edge-cloud computing and artificial intelligence related IoMT. INDEX TERMS Internet of medical things (IoMT), deep learning, edge of computing, computation offloading.

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“…In [22], the authors have presented a joint framework which composed of mobile edge computing (MEC) and cached-enabled D2D to optimize the energy cost. In [23], the authors have formulated the caching problem as a Markov decision process (MDP) and proposed the RLbased cooperative caching strategy to learn the optimal policy to minimize the delay. In [24], the authors have proposed a model-free reinforcement learning-(RL-) based algorithm.…”

Section: Related Workmentioning

confidence: 99%

Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

Jing

Song

et al. 2021

Wireless Communications and Mobile Computing

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Caching in device-to-device (D2D) networks is emerging a promising trend, which enables to reduce backhaul traffic. Moreover, social interaction among users influences the performance of overall system network. Therefore, it is crucial to consider social attributes in the D2D networks to develop a caching strategy to resolve the problem of unbalanced content distributed. In this paper, we consider two types of users according to their activeness, i.e., active users and inactive users. Inactive users assist active users cache contents during off-peak periods and provide the contents to the active users during peak periods to relieve the pressure of base station (BS). In addition, caching system model is divided into physical domain model and social domain model. In physical domain, the quality of communication links is judged by the delay between D2D users. In social domain, based on a real-world dataset, CiaoDVD, we calculate user similarity in three dimensions and obtain user trust by a trust topology to measure user relationships. Finally, in order to maximize the cache hit ratio, a joint action deep Q -networks (JADQN) framework is proposed to pair the active users with inactive users and distribute the contents to inactive users. Simulation results indicate that the proposed strategy improves the cache hit ratio by 42.9% and reduces the download delay by 48.8% compared with least frequency used (LFU) algorithm, which validates the effectiveness of our method.

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Cooperative Caching and Transmission in CoMP-Integrated Cellular Networks Using Reinforcement Learning

Cited by 54 publications

References 31 publications

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

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