QoE Driven Decentralized Spectrum Sharing in 5G Networks: Potential Game Approach

Zhang, Ning; Zhang, Shan; Zheng, Jianchao; Fang, Xiaojie; Mark, Jon W.; Shen, Xuemin

doi:10.1109/tvt.2017.2682236

Cited by 77 publications

(32 citation statements)

References 31 publications

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“…Denote a report not necessarily truthful as ρ l i . 15 All other users report truthfully. Denote the resulting matrix W l when user i reports ρ l i as W l (ρ l i ).…”

Section: F Proof Of Theoremmentioning

confidence: 93%

Network Utility Maximization Based on an Incentive Mechanism for Truthful Reporting of Local Information

Gao

Zhao

Shen

2018

IEEE Trans. Veh. Technol.

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Classic network utility maximization problems are usually solved assuming all information is available, implying that information not locally available is always truthfully reported. This may not be practical in all scenarios, especially in distributed/semi-distributed networks. In this paper, incentive for truthful reporting in network optimizations with local information is studied. A novel general model for extending network utility maximization (NUM) problems to incorporate local information is proposed, which allows each user to choose its own objective locally and/or privately.Two specific problems, i.e., a user-centric problem (UCP) and a network-centric problem (NCP), are studied. In the UCP, a network center aims to maximize the collective benefit of all users, and truthful reporting from the users regarding their local information is necessary for finding the solution. We show that the widely-adopted dual pricing cannot guarantee truthful information reporting from a user unless the resource is over-supplied or the price is too high for this user to afford. In the NCP, the network center has its own objective and preferred solution, and incentive needs to be provided for the users to adopt the solution it prefers. Truthful reporting from users is necessary for the center to determine the incentives and achieve its solution. For two-user and multiuser cases, we propose two mechanisms to motivate truthful reporting from users while guaranteeing nonnegative utility gains for both the users and the center. A case study on underlay D2D communication illustrates the application of the UCP and NCP. Simulations are conducted for the D2D application to validate the analytical results and demonstrate the proposed mechanisms.

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“…Denote a report not necessarily truthful as ρ l i . 15 All other users report truthfully. Denote the resulting matrix W l when user i reports ρ l i as W l (ρ l i ).…”

Section: F Proof Of Theoremmentioning

confidence: 93%

Network Utility Maximization Based on an Incentive Mechanism for Truthful Reporting of Local Information

Gao

Zhao

Shen

2018

IEEE Trans. Veh. Technol.

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“…To solve this, a fast heuristic algorithm is proposed to reduce computational complexity resulting desired QoS such as latency. In [149], a game theory and interference graph based optimization problem considering user scheduling, power allocation and spectrum access is presented with an aim to maximize user satisfaction across the network. Two algorithms including spatial adaptive play iterative (SAPI) learning are proposed to achieve Nash equilibrium.…”

Section: J Cran and Other Aspectsmentioning

confidence: 99%

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

Parvez

Rahmati

Güvenç

et al. 2018

IEEE Commun. Surv. Tutorials

766

395

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The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: RAN, core network, and caching. We also present a general overview of 5G cellular networks composed of software defined network (SDN), network function virtualization (NFV), caching, and mobile edge computing (MEC) capable of meeting latency and other 5G requirements.

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“…A jointly design of spectrum sensing and access policies for multi-user QoEoriented video delivery within cognitive radio networks is presented in (He et al, 2016), in which the goal is to achieve fairness among the users while maximizing the average QoE (which is based on throughput). Zhang et al (2017) addressed networks where different types of base stations are deployed to exploit a heterogeneous spectrum pool, containing licensed and harvested spectrum, in order to propose a game-theoretic approach that solves the problem of optimizing the global users' satisfaction (based on their throughput requirement) by jointly optimizing spectrum sharing, user scheduling, and power allocation in a decentralized manner. A framework is devised in (Piran et al, 2017) that has the goal of managing the inevitable spectrum handoff within cognitive networks while providing seamless multimedia content streaming and QoE enhancement (namely by taking into consideration the delay and the quality of the multimedia content).…”

Section: Heterogeneous Cognitive Radio Relay and Multi-mentioning

confidence: 99%

“…Uplink (Essaili et al, 2011;Wu et al, 2012;Condoluci et al, 2017;Liu et al, 2018;Ranjan et al, 2018) Multi-cell (Zheng et al, 2014;Cho et al, 2015;Kim et al, 2015;Miller et al, 2015) Heterogeneous networks (Toseef et al, 2011;Jailton et al, 2013;Seyedebrahimi and Peng, 2015;Morel and Randriamasy, 2017;Abbas et al, 2017) Device-to-device communications (Zhu et al, 2015a;Hong et al, 2017;Biswash and Jayakody, 2018) Vehicular networks (Xu et al, 2013;Yaacoub et al, 2015;Ding et al, 2018) Cognitive radio networks (Jiang et al, 2012;Wu et al, 2013;He et al, 2016;Zhang et al, 2017;Piran et al, 2017;Lin et al, 2017) Relay networks (Reis et al, 2010;Wu et al, 2013;Bethanabhotla et al, 2016;Xiang et al, 2017;Fan and Zhao, 2018) Multi-user MIMO networks (Cao et al, 2012;Bethanabhotla et al, 2016;Chen et al, 2017;Huang and Zhang, 2018) Base stations energy consumption (Ma et al, 2012;Li et al, 2012;Gabale and Subramanian, 2014;Draxler et al, 2014;Sapountzis et al, 2014;…”

Section: Scope Referencesmentioning

confidence: 99%

A survey on QoE-oriented wireless resources scheduling

Sousa

Queluz

Rodrigues

2020

Journal of Network and Computer Applications

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Future wireless systems are expected to provide a wide range of services to more and more users. Advanced scheduling strategies thus arise not only to perform efficient radio resource management, but also to provide fairness among the users. On the other hand, the users' perceived quality, i.e., Quality of Experience (QoE), is becoming one of the main drivers within the schedulers design. In this context, this paper starts by providing a comprehension of what is QoE and an overview of the evolution of wireless scheduling techniques. Afterwards, a survey on the most recent QoE-based scheduling strategies for wireless systems is presented, highlighting the application/service of the different approaches reported in the literature, as well as the parameters that were taken into account for QoE optimization. Therefore, this paper aims at helping readers interested in learning the basic concepts of QoE-oriented wireless resources scheduling, as well as getting in touch with its current research frontier.

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QoE Driven Decentralized Spectrum Sharing in 5G Networks: Potential Game Approach

Cited by 77 publications

References 31 publications

Network Utility Maximization Based on an Incentive Mechanism for Truthful Reporting of Local Information

Network Utility Maximization Based on an Incentive Mechanism for Truthful Reporting of Local Information

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

A survey on QoE-oriented wireless resources scheduling

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