Joint Scheduling of Proactive Pushing and On-Demand Transmission Over Shared Spectrum for Profit Maximization

Hui, Haiming; Chen, Wei

doi:10.1109/twc.2022.3191361

Cited by 17 publications

(12 citation statements)

References 38 publications

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“…Then, an efficient BSUM algorithm is proposed to solve the dual problem (17). Commonly, BSUM adopts an approximation obtained by Taylor's expansion [28].…”

Section: Proposed Solution By An Efficient Bsum Algorithmmentioning

confidence: 99%

“…A three-tier architecture of coordinated caching was introduced and optimization problem to minimize the content delivery latency was formulated, where graph feature embedding and searching were designed to approximate the solutions [16]. In [17], with the aim of improving the spectrum efficiency for profit maximization, nonlinear spectrum pricing schemes were adopted to schedule the caching of proactive pushing contents where an algorithm of modified-value iteration was applied to solve the formulated problem of Markov decision process. A federated learning method was proposed to address the problem of content popularity prediction in caching networks [18].…”

Section: Introduction 1existing Surveys and Contributionsmentioning

confidence: 99%

“…The proposed efficient BSUM algorithm Input: Random times K; search accuracy ϵ; step-size parameter ν. Output: w * 1 for i ← 1 to K do Approximating ō in(17) by ô in Propositon 2;…”

mentioning

confidence: 99%

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An Efficient Block Successive Upper-Bound Minimization Algorithm for Caching a Reconfigurable Intelligent Surface-Assisted Downlink Non-Orthogonal Multiple Access System

Zhou

2024

Electronics

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With the booming rollout of 5G communication, abundant new technologies have been proposed for quality of service requirements. In terms of the betterment in transmission coverage, mobile edge caching (MEC) has shown potential in reducing the transmission outage. The performance of MEC, meanwhile, can be promisingly enhanced by reconfigurable intelligent surfaces (RIS). Under this context, we explore a system comprising a small base-station (SBS) with limited cache capacity, two users, and one RIS. The SBS transmits the contents from the cache or fetches them from the remote backhaul hub to communicate with users through directional and possibly reflective channels. In this point-to-multipoint connection, non-orthogonal multiple access (NOMA) is applied, improving the capacity of the system. To minimize the outage probability, we first propose a caching policy from entropy perspective, based on which we investigate the beamforming and power allocation problem. The issue, however, is non-convex and involves multi-dimensional optimization. To address this, we introduce an efficient block successive upper-bound minimization algorithm, grounded in Gershgorin’s circle theorem. This algorithm aims to find the globally optimal solution for power allocation and RIS beamformer, considering both the channel condition and content popularity. Numerical studies are performed to verify the effectiveness of the proposed algorithm.

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“…Then, an efficient BSUM algorithm is proposed to solve the dual problem (17). Commonly, BSUM adopts an approximation obtained by Taylor's expansion [28].…”

Section: Proposed Solution By An Efficient Bsum Algorithmmentioning

confidence: 99%

Section: Introduction 1existing Surveys and Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Block Successive Upper-Bound Minimization Algorithm for Caching a Reconfigurable Intelligent Surface-Assisted Downlink Non-Orthogonal Multiple Access System

Zhou

2024

Electronics

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“…• Sigmoid function: This function maps any input to a value between 0 and 1, which is useful for binary classification problems, given by [4,18,19]…”

Section: Activation Fuctionsmentioning

confidence: 99%

“…Another area of research in power grid networks is the use of smart grid technologies. Smart grids incorporate advanced communication and control systems that enable real-time monitoring and optimization of the power grid network [4][5][6]. This allows for more efficient energy distribution, reduced energy waste, and improved grid reliability [7,8].…”

Section: Introductionmentioning

confidence: 99%

Research on the Performance of Text Mining and Processing in Power Grid Networks

Zhou

Lin

et al. 2023

EAI Endorsed Scal Inf Syst

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This paper employs deep learning technique to perform the research of text mining for power grid networks, focusing on fundamental elements such as loss and activation functions. Through some analysis and formulas, we explain how these functions contribute to deep learning. We also introduce major deep learning training models, including CNN and RNN, and provide visual aids to aid understanding. To demonstrate the impact of various factors on deep learning training, we employ control variable experiments to analyze the influence of factors such as learning rate, batch size, and data noise on model training trends. While the influence of hyperparameters and data noise are covered in this paper, other factors such as CPU and memory frequency, as well as GPU performance, also play a crucial role in deep learning training. Therefore, continuous adjustments to various factors are necessary to achieve optimal training results for deep learning models in power grid networks.

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Energy-efficient edge intelligence for task-dependency MEC power grid networks

Yang,

Xie,

et al. 2024

Wireless Netw

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Joint Scheduling of Proactive Pushing and On-Demand Transmission Over Shared Spectrum for Profit Maximization

Cited by 17 publications

References 38 publications

An Efficient Block Successive Upper-Bound Minimization Algorithm for Caching a Reconfigurable Intelligent Surface-Assisted Downlink Non-Orthogonal Multiple Access System

An Efficient Block Successive Upper-Bound Minimization Algorithm for Caching a Reconfigurable Intelligent Surface-Assisted Downlink Non-Orthogonal Multiple Access System

Research on the Performance of Text Mining and Processing in Power Grid Networks

Energy-efficient edge intelligence for task-dependency MEC power grid networks

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