Energy-Aware Joint User Association and Resource Allocation for Coded Cache-Enabled HetNets

Yin, Fang‐Fang; Wang, Anyue; Liu, Danpu; Zhang, Zhilong

doi:10.1109/access.2019.2923630

Cited by 11 publications

(14 citation statements)

References 35 publications

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“…For a cluster sleeping method, Chang et al [ 11 ] utilized a genetic algorithm to achieve dynamic matching of energy consumption and Li et al [ 12 ] proposed a Gauss–Seidel method to optimize resources in HetNets. In [ 13 ], a low complexity algorithm based on the many-to-many matching game between the virtual SMSs, and the users were proposed to solve the problems of exponential growth of mobile data traffic and energy saving. Anany et al [ 14 ] also utilized matching game and proposed an association algorithm which jointly considered the rate and power of each wireless device to get optimal association between the wireless devices and the best BSs according to a well-designed utility function.…”

Section: Related Workmentioning

confidence: 99%

A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

Wang

et al. 2020

Entropy

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In order to maximize energy efficiency in heterogeneous networks (HetNets), a turbo Q-Learning (TQL) combined with multistage decision process and tabular Q-Learning is proposed to optimize the resource configuration. For the large dimensions of action space, the problem of energy efficiency optimization is designed as a multistage decision process in this paper, according to the resource allocation of optimization objectives, the initial problem is divided into several subproblems which are solved by tabular Q-Learning, and the traditional exponential increasing size of action space is decomposed into linear increase. By iterating the solutions of subproblems, the initial problem is solved. The simple stability analysis of the algorithm is given in this paper. As to the large dimension of state space, we use a deep neural network (DNN) to classify states where the optimization policy of novel Q-Learning is set to label samples. Thus far, the dimensions of action and state space have been solved. The simulation results show that our approach is convergent, improves the convergence speed by 60% while maintaining almost the same energy efficiency and having the characteristics of system adjustment.

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Section: Related Workmentioning

confidence: 99%

A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

Wang

et al. 2020

Entropy

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“…Wu et al [60] Ruan et al [61] John et al [62] Wu et al [63] Jin and van Dijk [64] Yu et al [66] Abbass et al [68] Chica et al [69] Hu et al [6] Zhu et al [70] Ding et al [71] Zhang et al [72] Aloqaily et al [74] Rishwaraj et al [75] Pouryazdan et al [76] Chen et al [77] Lorenzo et al [78] Li et al [79] Aroyo et al [80] Moghadam and Modares [81] Cui et al [28] Yin et al [26] Chen et al [27] Li et al [14] According to whether the players cooperate, the game is divided into cooperative game and non-cooperative game. The common goal of both games can maximize network utility and improve network security.…”

Section: Security Game Trust Game Othersmentioning

confidence: 99%

“…The introduction of the game model improves the security of the network and against external and internal attacks [14,70,72,74,77]. There are also some game models that provide great help for network or system strategy selection [27,71,78,79,81], or addressed the problem of resource allocation [26]. In [28], the introduction of game theory optimizes the probability of successful transmission probabilities of data and the caching probabilities in some networks.…”

Section: Security Game Trust Game Othersmentioning

confidence: 99%

“…How to choose a reliable node for transmission, and how to isolate malicious nodes out from the network quickly and accurately, are crucial [21][22][23]. To find the optimal choice in distributed network, game theory is an effective solution [24][25][26]. Combining a trust relationship between nodes and games can solve problems such as path selection in distributed networks.…”

Section: Introductionmentioning

confidence: 99%

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Towards Security Joint Trust and Game Theory for Maximizing Utility: Challenges and Countermeasures

Huang

Jia

Fang

et al. 2019

Sensors

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The widespread application of networks is providing a better platform for the development of society and technology. With the expansion of the scope of network applications, many issues need to be solved. Among them, the maximization of utility and the improvement of security have attracted much attention. Many existing attacks mean the network faces security challenges. The concept of trust should be considered to address these security issues. Meanwhile, the utility of the network, including efficiency, profit, welfare, etc., are concerns that should be maximized. Over the past decade, the concepts of game and trust have been introduced to various types of networks. However, there is a lack of research effort on several key points in distributed networks, which are critical to the information transmission of distributed networks, such as expelling malicious nodes quickly and accurately and finding equilibrium between energy assumption and high transmission rate. The purpose of this paper is to give a holistic overview of existing research on trust and game theory in networks. We analyzed that network utility can be maximized in terms of effectiveness, profits, and security. Moreover, a possible research agenda is proposed to promote the application and development of game theory and trust for improving security and maximizing utility.

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“…The use of MDS codes in heterogeneous wireless networks was studied in [9]- [11]. The authors in [9] formulated the op-timal MDS encoding caching scheme as a convex optimization to minimize the backhaul rate.…”

Section: Introductionmentioning

confidence: 99%

Joint Redundant MDS Codes and Cluster Cooperation Based Coded Caching in Fog Radio Access Networks

Wang

Jiang

Zheng

et al. 2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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In this paper, we investigate maximum-distance separable (MDS) codes and cluster based coded caching in fog radio access networks (F-RANs). In order to minimize the fronthaul rate, multicast opportunities need to be constructed at the cloud server. Firstly, a redundant MDS codes based coded placement scheme is proposed to provide redundant coded packets and symmetrical cache contents. The redundant coded packets can be used to construct multicast opportunities for requests on the same file. Furthermore, based on the symmetrical cache contents, we propose a cluster cooperation based coded delivery scheme, which can induce considerable multicast opportunities between any two clusters regardless of whether the requests are on the same file or not. Finally, by utilizing the redundant coded packets and the symmetry of cache contents, a joint redundant MDS codes and cluster cooperation based coded caching policy is proposed to minimize the fronthaul rate. Simulation results show that our proposed policy can provide 30% savings of the fronthaul rate compared to the MDS-based uncoded delivery policy.

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Energy-Aware Joint User Association and Resource Allocation for Coded Cache-Enabled HetNets

Cited by 11 publications

References 35 publications

A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

Towards Security Joint Trust and Game Theory for Maximizing Utility: Challenges and Countermeasures

Joint Redundant MDS Codes and Cluster Cooperation Based Coded Caching in Fog Radio Access Networks

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