Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Tang, Jie; Tang, Hengbin; Zhang, Xiu Yin; Cumanan, Kanapathippillai; Chen, Gaojie; Wong, Kai-Kit; Chambers, Jonathon A.

doi:10.1109/tii.2019.2954127

Cited by 58 publications

(30 citation statements)

References 37 publications

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“…In addition, a D2D-assisted edge caching strategy was designed for IoT systems. The novel tecnique not only reduced transmission energy consumption, but also offloaded traffic to SBSs and user devices [136]. Another framework optimally allotted transmission power and activated D2D links in a single-hop D2D network [137].…”

Section: B Energy-efficient Resource Allocationmentioning

confidence: 99%

“…MEC system [134], Cellular network [135], HetNet [136] Solved non-convex optimization by using problem decomposition, iterative algorithm, and game theoretic approach [134]. Dinkelbach method, Modified branch and bound method, Lagrange dual decomposition [135].…”

Section: Content Centricmentioning

confidence: 99%

“…Dinkelbach method, Modified branch and bound method, Lagrange dual decomposition [135]. MDP, Q-Learning and deep Q-network algorithm [136]. [137] Optimal transmission power allotment and D2D link activation.…”

Section: Content Centricmentioning

confidence: 99%

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A Review on Green Caching Strategies for Next Generation Communication Networks

et al. 2020

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In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching.

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Section: B Energy-efficient Resource Allocationmentioning

confidence: 99%

Section: Content Centricmentioning

confidence: 99%

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A Review on Green Caching Strategies for Next Generation Communication Networks

et al. 2020

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“…The deep reinforcement learning (DRL) algorithm, which combines reinforcement learning with deep neural network, has been proved in [26] to have faster convergence rate and better performance. For example, the authors in [27] adopted the RL and DRL algorithm respectively in a content distribution network to reveal the popularity of files, so as to save the energy cost of transmission.…”

Section: Introductionmentioning

confidence: 99%

Multi-CAP Assisted Intelligent Mobile Edge Computing Networks for Internet of Things

et al. 2020

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In this paper, we investigate intelligent mobile edge computing networks (MEC) with multiple computational access points (CAPs) for Internet of Things (IoT), where the user with limited computational capability has some tasks to be computed with the help of the CAPs. We use the communication and computational latency to measure the system outage probability. In order to enhance the system performance, two CAP relay selection criteria are proposed, where one is based on the computational capability at the CAPs while the other criterion is based on the instantaneous channel parameters from the user to the CAPs. To further improve the system performance, the offloading ratio is optimized through minimizing the system outage probability, which determines how many parts of tasks to be computed at the CAPs. Numerical and simulation results are finally presented to verify the effectiveness of the proposed CAP selection criteria and offloading ratio strategy.

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“…Finally we use the results from this optimization to train a deep convolutional neural network (DCNN) to determine the power control coefficients based on the LSF. Recently, different machine learning techniques have been exploited to solve challenging research problems in various communications systems my papers and [23]- [28]. In particular, the DCNN has been widely used to design the power elements in the wireless communication networks [26]- [28].…”

mentioning

confidence: 99%

Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink

Bashar

Akbari

Cumanan

et al. 2020

IEEE J. Select. Areas Commun.

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A cell-free massive multiple-input multiple-output (MIMO) uplink is considered, where quantize-and-forward (QF) refers to the case where both the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combinequantize-and-forward (CQF), the APs send the quantized version of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing largescale-fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN.

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Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Cited by 58 publications

References 37 publications

A Review on Green Caching Strategies for Next Generation Communication Networks

A Review on Green Caching Strategies for Next Generation Communication Networks

Multi-CAP Assisted Intelligent Mobile Edge Computing Networks for Internet of Things

Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink

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