Power Consumption Minimization in Cache-Enabled Mobile Networks

Dong, Fang; Wang, Tianyu

doi:10.1109/tvt.2019.2914023

Cited by 5 publications

(6 citation statements)

References 35 publications

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“…In other words, the channel selection decision can be efficiently solved with the help of DQN. In [29], the total system power consumption minimization problem in a cache-enabled mobile network is considered, the authors decouple the optimization task into several subproblems and solve them with the idea of associating the users with the SBS. In our work, we use a deep RL algorithm to solve the file selection problem, and the optimal caching files can be predicted directly.…”

Section: A Prior Workmentioning

confidence: 99%

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

Tang

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

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Mobile edge caching (MEC) and device to device (D2D) communications are two potential technologies to resolve traffic overload problems in internet of things (IoT). Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this paper, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy where Markov decision process (MDP) is applied to model the requesting behaviours. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, Q-learning (QL) algorithm and deep Q-network (DQN) algorithm are respectively applied to user devices and SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files and status changing into consideration, the proposed RL algorithm enables user devices and SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

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Section: A Prior Workmentioning

confidence: 99%

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

Tang

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

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“…For a two-tier HetNet, a caching resource and spectrum allocation scheme was proposed to determine the optimal SBS density and improve energy efficiency [131]. Another strategy optimally allocated constrained spectrum and storage resources at the cache-quipped BSs to minimize energy consumption [132]. A centralized framework for MEC and caching was proposed in [133], which jointly optimized computation and spectrum resources to reduce the overall latency of the system.…”

Section: B Energy-efficient Resource Allocationmentioning

confidence: 99%

“…HetNet [131], Mobile network [132], MEC network [133] Stochastic geometry and numerical analysis [131], Decoupling of optimization problem and utilization of block nested procedures [132], Solved optimization problem using Branch and Bound method and Benders decomposition [133].…”

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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“…However, when the network size becomes very large, it becomes very challenging to apply as it involves learning a huge state-action table and also massive memory to store the learnt table [20]. Heuristic approaches [21], [22], on the other hand, are easier to implement, but have poor generalization ability and cannot adapt to dynamic network environment such as is obtained in 5G and beyond networks. As such, even though some of them are computationally efficient and can be applied to a large networks, they mostly result in sub-optimal solutions which could result in degradation in the quality of service (QoS) of the network [23].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Cell Switching and Traffic Offloading Scheme for Energy Optimization in Ultra-Dense Heterogeneous Networks

Abubakar¹,

Mollel²,

Öztürk³

et al. 2021

Preprint

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One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in tremendous increase in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network but most of the proposed methods are computationally demanding which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEll swItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed to be optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that the THESIS produces a significant reduction in the energy consumption of the UDHN and is able to reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.

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Power Consumption Minimization in Cache-Enabled Mobile Networks

Cited by 5 publications

References 35 publications

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

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

A Review on Green Caching Strategies for Next Generation Communication Networks

A Lightweight Cell Switching and Traffic Offloading Scheme for Energy Optimization in Ultra-Dense Heterogeneous Networks

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