Machine-Learning Approach for User Association and Content Placement in Fog Radio Access Networks

Yan, Shi; Jiao, Minghan; Zhou, Yangcheng; Peng, Mugen; Daneshmand, Mahmoud

doi:10.1109/jiot.2020.2973339

Cited by 19 publications

(25 citation statements)

References 36 publications

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“…Joint user association and content placement for network payoff maximization is the topic of [107], in a two-tier network, consisting of a massive MIMO macro BS and a group of F-APs. Here, network payoff is defined as the ergodic rate performance utility minus the fronthaul cost for cache replacement.…”

Section: Spectral Efficiencymentioning

confidence: 99%

“…In general, most works on F-RANs develop joint RLaided resource allocation and content update schemes, outperforming policy-based caching. Hybrid schemes offer high offloading efficiency through unsupervised learning-based popularity prediction and DRL-based content placement [107]. Also, distributed learning employs F-APs to independently determine the optimal caching policy, thus reducing network coordination overheads.…”

Section: Spectral Efficiencymentioning

confidence: 99%

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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: Spectral Efficiencymentioning

confidence: 99%

Section: Spectral Efficiencymentioning

confidence: 99%

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

et al. 2022

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“…ese challenges are tinted due to the complexity of different approaches used to find optimal solutions. In [13], authors considered optimization problems as mixed-integer nonlinear programming (MINL). A hierarchical game theory approach is applied, and a series of deep reinforcement learning (DRL) based algorithms are designed for user association, content popularity prediction, and content placement to enhance the FAPs.…”

Section: Literature Reviewmentioning

confidence: 99%

Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework

Hasan

Ahmad

Rizwan

et al. 2022

Computational Intelligence and Neuroscience

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Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of Things (IoT) based scenarios. Therefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the prioritized contents are stored in QMM using a Two-Level Spin Quantum Phenomenon (TLSQP). After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. These videos are considered a high priority for trending according to the input parameters provided in the dataset. Similarly, the light-blue color region is also mapped to get medium-prioritized content. After the SOMs algorithm’s training, the topographic error (TE) value together with quantization error (QE) value (i.e., 0.0000235) plotted the most appropriate map after 750,000 iterations. In addition, the power of QC is due to the inherent quantum parallelism (QP) associated with the superposition and entanglement principles. A quantum computer taking “n” qubits that can be stored and execute 2n presumable combinations of qubits simultaneously reduces the utilization of resources compared to conventional computing. It can be analyzed that the cache hit ratio will be improved by ranking the content, removing redundant and least important content, storing the content having high and medium prioritization using QP efficiently, and delivering precise results. The experiments for content prioritization are conducted using Google Colab, and IBM’s Quantum Experience is considered to simulate the quantum phenomena.

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“…The user association problem is tackled with a reinforcement learning method that considers content placement profiles and frontal constraints [262][263][264].…”

Section: Rlmentioning

confidence: 99%

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mahdi

Ahmad

Qassim³

et al. 2021

Applied Sciences

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Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

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Machine-Learning Approach for User Association and Content Placement in Fog Radio Access Networks

Cited by 19 publications

References 36 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 Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

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