A Cluster-Based Energy-Efficient Resource Management Scheme for Ultra-Dense Networks

Liang, Liang; Wang, Wen; Jia, Yunjian; Fu, Shu

doi:10.1109/access.2016.2614517

Cited by 74 publications

(38 citation statements)

References 22 publications

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“…However, as these mobile users encounter each other and frequently form new D2D links, there exists much more link-switching in D2D enabled networks than conventional cellular networks, 14 especially under the case where there are massive users with high traffic density requirements in next generation networks with large number of base stations. 15 Therefore, this poses an open issue to analyze the network performance under such new circumstance, in which there are many D2D links formed for traffic offloading and frequent link-switching caused by user mobility. In D2D enabled networks, 2 specific conditions need to be considered, (1) mobility based link-switching to consistent content transmission for mobile users, and (2) view-based link-switching by flexibly switching to new multicast links for new camera views.…”

Section: Introductionmentioning

confidence: 99%

A Markovian analytical framework for public‐safety video sharing by device‐to‐device communications

Zhao

Han

Mao

et al. 2017

Concurrency and Computation

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Monitoring video of city surveillance camera plays an important role in public security and disaster relief, which is often used by first responder teams, such as firefighters and police officers.The first responders in emergency situations require consistent connection with one another and request the remote real-time monitoring video for effective cooperation and coordination.However, the capacity and privacy of public wireless networks fail to satisfy the requirements in many emergency scenarios, which often leads to exceptionally high traffic loads and insecurity.Device-to-device (D2D) communications have been deemed a key solution for this problem, as responders can use D2D links for traffic offloading and secure communications. To investigate the D2D-based solution for public safety video sharing, this paper focuses on the Focus Geographical Area (FGA) video consisting of multiple camera streams requiring higher bandwidth consumption than that of the traditional single-camera stream, which attracts a large number of contents delivery requests in emergency situations. This paper develops a new Traffic Burden Switching Markovian (TBSM) model to evaluate the performance of transmitting real-time FGA video in wireless networks with D2D communications. First, a novel D2D area model is introduced to characterize link-switching in wireless D2D communication networks. Based on the proposed D2D area model, the state and transition matrix of TBSM model are then derived by jointly considering user mobility, link-switching, and FGA video view-switching. Thereafter, some key performance metrics including system offloading ratio, D2D link-switching ratio, and view-switching ratio are derived on the basis of coverage probability and ergodic rate. The performance results show the significant varying performance among D2D areas with different geographical locations in D2D enabled wireless networks, which is referred to as multi-D2D-area diversity. The excellent match between simulation and model results validates the accuracy of the TBSM model, which can be used to provide guidelines for the deployment and optimization of future wireless video networks with D2D communications. KEYWORDS Device-to-device area, Camera view switching, Markovian model, Traffic burden switching 1 tional bandwidth-limited wireless networks with the following characteristics: (1) provision of more than one camera stream, (2) permission Concurrency Computat: Pract Exper. 2017;29:e4078.wileyonlinelibrary.com/journal/cpe

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Section: Introductionmentioning

confidence: 99%

A Markovian analytical framework for public‐safety video sharing by device‐to‐device communications

Zhao

Han

Mao

et al. 2017

Concurrency and Computation

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“…Indeed, the previous research advances reveal that combining the clustering method with appropriate resource allocation has the potential of simultaneously mitigating both the intercell and intra-cell interference [32]- [34]. In the context of the user-centric overlapped clustering framework, there have also been some research efforts focused on the resource allocation problem.…”

Section: B Related Workmentioning

confidence: 99%

Graph-Based Joint User-Centric Overlapped Clustering and Resource Allocation in Ultradense Networks

Lin

Zhang

et al. 2018

IEEE Trans. Veh. Technol.

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Abstract-With the increase of access point (AP) density and the exponential growth of mobile devices supported by ultra dense networks (UDNs), user-centric overlapped clustering relying on AP cooperation is becoming a promising design principle for guaranteeing the quality of service (QoS) required by each UE. This ambitious goal can be achieved by the joint design of user-centric overlapped clustering and resource allocation. In this context, both the traffic-load balancing and the limited availability of orthogonal resource blocks (RBs) have to be carefully considered in UDNs. To tackle these challenges, we formulate a joint user-centric overlapped clustering and resource allocation problem with the goal of maximizing the system's spectral efficiency (SE). To efficiently solve this problem, it is decoupled into two subproblems, which can be solved independently with the aid of our graph-theoretical framework. We first develop a novel distributed three-stage user-centric clustering solution, which is aware of the APs' traffic-loads. Then, based on the overlapped clusters constructed, we propose a novel two-stage graph-based user-centric resource allocation scheme for mitigating the resultant inter-cluster interference, despite the limited availability of orthogonal RBs. Our numerical results confirm the benefits of the proposed framework and show that it outperforms the benchmark solutions in terms of both its per area aggregated user rate (PAAR) and user rate.

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“…Nevertheless, they are hardly able to solve the multi-objective optimization problems. The existing literature only considers the partial optimization objectives of UDNs, which cannot improve network performance well [14,15]. Especially, with the rise of the environmentfriendly UDNs and user experience, the tradeoff among SE, EE and fairness has become one of the biggest challenges in the field of resource allocation for future UDNs [16,17].…”

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confidence: 99%

A Model-Driven Deep Reinforcement Learning Heuristic Algorithm for Resource Allocation in Ultra-Dense Cellular Networks

Liao

Shi

et al. 2020

IEEE Trans. Veh. Technol.

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A model-driven deep reinforcement learning heuristic algorithm for resource allocation in ultra-dense cellular networks. IEEE Transactions on Vehicular Technology, 69(1), pp. 983-997.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.Abstract-Resource allocation in ultra dense network (UDN) is an multi-objective optimization problem since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) and fairness. The existing methods can not effectively solve this NP-hard nonconvex problem, especially in the presence of limited channel state information (CSI). In this paper, we investigate a novel model-driven deep reinforcement learning assisted resource allocation method. We first design a novel deep neural network (DNN)-based optimization framework consisting of a series of Alternating Direction Method of Multipliers (ADMM) iterative procedures, which makes the CSI as the learned weights. Then a novel channel information absent Q-learning resource allocation (CIAQ) algorithm is proposed to train the DNN-based optimization framework without massive labeling data, where the SE, the EE, and the fairness can be jointly optimized by adjusting discount factor. Our simulation results show that, the proposed CIAQ with rapid convergence speed not only well characterizes the extent of optimization objective with partial CSI, but also significantly outperforms the current random initialization method of neural network and the other existing resource allocation algorithms in term of the tradeoff among the SE, EE and fairness.

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A Cluster-Based Energy-Efficient Resource Management Scheme for Ultra-Dense Networks

Cited by 74 publications

References 22 publications

A Markovian analytical framework for public‐safety video sharing by device‐to‐device communications

A Markovian analytical framework for public‐safety video sharing by device‐to‐device communications

Graph-Based Joint User-Centric Overlapped Clustering and Resource Allocation in Ultradense Networks

A Model-Driven Deep Reinforcement Learning Heuristic Algorithm for Resource Allocation in Ultra-Dense Cellular Networks

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