Cooperative channel allocation and scheduling in multi-interface wireless mesh networks

Deng, Xiaoheng; Luo, Jie; He, Lifang; Liu, Qiang; Li, Xu; Cai, Lin

doi:10.1007/s12083-017-0619-8

Cited by 49 publications

(34 citation statements)

References 26 publications

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“…For the same multicast content dissemination, this pair of cellular channels will be interleaved on the relaying path from the base station to the final target DCH, which is not used by other multicast content distribution services, and thus, the cochannel interference will not occur. erefore, in this paper, we will ignore the cochannel interference in formulas (5) and (6) to simplify the calculation process of cellular multicast power and intercluster D2D multicast power.…”

Section: E Solving Methods For D2d Multicast Transmission Powermentioning

confidence: 99%

“…For t � 1 to T do (5) If k � argmax i∈ 1,...,K { } (g it ) then m tk � 1 End if (6) End for (7) For k � 1 to K do (8) x…”

Section: E Solving Methods For D2d Multicast Transmission Powermentioning

confidence: 99%

“…It is generally accepted that the ultradense network (UDN) can make each user enjoy a very high data rate with good quality of service (QoS) [4,5]. is is because that the UDN is formed by deploying many small base stations within the traditional macro cell coverage area.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Energy Efficiency of Multimedia Content Dissemination by Adaptive Clustering and D2D Multicast

Long

Gui

Zeng

2019

Mobile Information Systems

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While achieving desired performance, there exist still many challenges in current cellular networks to support the multimedia content dissemination services. The conventional multimedia transmission schemes tend to serve all multicast group members with the data rate supported by the receiving user with the worst channel condition. The recent work discusses how to provide satisfactory quality of service (QoS) for all receiving users with different quality of experience (QoE) requirements, but the energy efficiency improvement of multimedia content dissemination is not its focus. In this paper, we address it based on adaptive clustering and device-to-device (D2D) multicast and propose an energy-efficient multimedia content dissemination scheme under a consistent QoE constraint. Our scheme extends the recent work with the proposed K-means-based D2D clustering method and the proposed game-based incentive mechanism, which can improve energy efficiency of multimedia content dissemination on the premise of ensuring the desired QoE for most multicast group members. In the proposed scheme, we jointly consider the cellular multicast, intracluster D2D multicast, and intercluster D2D multicast for designing the energy-efficient multimedia content dissemination scheme. In particular, we formulate the energy-efficient multicast transmission problem as a Stackelberg game model, where the macro base station (MBS) is the leader and the candidate D2D cluster heads (DCHs) are the followers. Also, the MBS acts as the buyer who buys the power from the candidate DCHs for intracluster and intercluster D2D multicast communications, and the candidate DCHs act as the sellers who earn reward by helping the MBS with D2D multicast communications. Through analyzing the above game model, we derive the Stackelberg equilibrium as the optimal allocation for cellular multicast power, intracluster D2D multicast power, and intercluster D2D multicast power, which can maximize the MBS’s utility function. Finally, the proposed scheme is verified through the simulation experiments designed in this paper.

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Section: E Solving Methods For D2d Multicast Transmission Powermentioning

confidence: 99%

“…For t � 1 to T do (5) If k � argmax i∈ 1,...,K { } (g it ) then m tk � 1 End if (6) End for (7) For k � 1 to K do (8) x…”

Section: E Solving Methods For D2d Multicast Transmission Powermentioning

confidence: 99%

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Improving Energy Efficiency of Multimedia Content Dissemination by Adaptive Clustering and D2D Multicast

Long

Gui

Zeng

2019

Mobile Information Systems

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“…The terminal handover in SDNbased WLANs can effectively improve the QoS and the resource utilization of WLANs. As a new computing paradigm, software-defined networking can also be applied to cellular networks [10], D2D heterogeneous networks [11], MESH networks [12], cloud computing [13], and edge computing [14] to facilitate resource allocation, provide service quality assurance, and improve user experience.…”

Section: Background and Motivationmentioning

confidence: 99%

Terminal handover in software-defined WLANs

Chen

Duan

Zhang

2020

J Wireless Com Network

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With the development of wireless LAN (WLAN) technology, wireless access points (APs) are deployed increasingly more and the number of mobile terminals is increasing. It is normal for a mobile terminal to hand over its AP. The traditional terminal handover method cannot effectively control the handover delay and balance the network load due to the inherent drawbacks such as closed encapsulation and the lack of centralized control. In this paper, we introduce the innovative network architecture of software-defined networking (SDN), propose a software-defined WLAN architecture, and propose a seamless handover method and a load balancing handover method of the softwaredefined WLAN. Experimental results show that the proposed handover method effectively reduces the handover delay, balances the network load, and improves the quality of service (QoS) and resource utilization of the network.

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“…In recent years, cognitive method is widely used in some areas of network, such as wireless mesh networks [15,16] and smart grid [17] to improve the adaptability to the changing environment. As one type of the cognitive network, the cognitive VN is different from traditional networks.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Virtual Network Embedding Algorithm Based on Weighted Relative Entropy

2019

KSII TIIS

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Current Internet is designed by lots of service providers with different objects and policies which make the direct deployment of radically new architecture and protocols on Internet nearly impossible without reaching a consensus among almost all of them. Network virtualization is proposed to fend off this ossification of Internet architecture and add diversity to the future Internet. As an important part of network virtualization, virtual network embedding (VNE) problem has received more and more attention. In order to solve the problems of large embedding cost, low acceptance ratio (AR) and environmental adaptability in VNE algorithms, cognitive method is introduced to improve the adaptability to the changing environment and a cognitive virtual network embedding algorithm based on weighted relative entropy (WRE-CVNE) is proposed in this paper. At first, the weighted relative entropy (WRE) method is proposed to select the suitable substrate nodes and paths in VNE. In WRE method, the ranking indicators and their weighting coefficients are selected to calculate the node importance and path importance. It is the basic of the WRE-CVNE. In virtual node embedding stage, the WRE method and breadth first search (BFS) algorithm are both used, and the node proximity is introduced into substrate node ranking to achieve the joint topology awareness. Finally, in virtual link embedding stage, the CPU resource balance degree, bandwidth resource balance degree and path hop counts are taken into account. The path importance is calculated based on the WRE method and the suitable substrate path is selected to reduce the resource fragmentation. Simulation results show that the proposed algorithm can significantly improve AR and the long-term average revenue to cost ratio (LTAR/CR) by adjusting the weighting coefficients in VNE stage according to the network environment. We also analyze the impact of weighting coefficient on the performance of the WRE-CVNE. In addition, the adaptability of the WRE-CVNE is researched in three different scenarios and the effectiveness and efficiency of the WRE-CVNE are demonstrated.

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Cooperative channel allocation and scheduling in multi-interface wireless mesh networks

Cited by 49 publications

References 26 publications

Improving Energy Efficiency of Multimedia Content Dissemination by Adaptive Clustering and D2D Multicast

Improving Energy Efficiency of Multimedia Content Dissemination by Adaptive Clustering and D2D Multicast

Terminal handover in software-defined WLANs

Cognitive Virtual Network Embedding Algorithm Based on Weighted Relative Entropy

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