Learning to Compete for Resources in Wireless Stochastic Games

Fu, Fang; Schaar, Mihaela van der

doi:10.1109/tvt.2008.2002917

Cited by 88 publications

(4 citation statements)

References 22 publications

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“…In [16], Huang et al proposed single-sided spectrum auction mechanisms for power allocation among secondary users subject to an interference temperature constraint at a single measurement point. The behavior of participants in spectrum auctions was studied in [17] for a single-sided spectrum auction and in [18] for a double-sided spectrum auction. In [19], Sengupta and Chatterjee proposed a knapsack auction in which the spectrum auction was modeled as a knapsack problem and both first-price and secondprice mechanisms were considered in their auction design.…”

Section: Related Workmentioning

confidence: 99%

Spectrum redistribution for cognitive radios using discriminatory spectrum double auction

Jiang

Lin

et al. 2011

Wireless Communications

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With the reformation of spectrum policy and the development of cognitive radio, secondary users will be allowed to access spectrums licensed to primary users. Spectrum auctions can facilitate this secondary spectrum access in a market‐driven way. To design an efficient auction framework, we first study the supply and demand pressures and the competitive equilibrium of the secondary spectrum market, considering the spectrum reusability. In well‐designed auctions, competition among participants should lead to the competitive equilibrium according to the traditional economic point of view. Then, a discriminatory price spectrum double auction framework is proposed for this market. In this framework, rational participants compete with each other by using bidding prices, and their profits are guaranteed to be non‐negative. A near‐optimal heuristic algorithm is also proposed to solve the auction clearing problem of the proposed framework efficiently. Experimental results verify the efficiency of the proposed auction clearing algorithm and demonstrate that competition among secondary users and primary users can lead to the competitive equilibrium during auction iterations using the proposed auction framework. Copyright © 2011 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

Spectrum redistribution for cognitive radios using discriminatory spectrum double auction

Jiang

Lin

et al. 2011

Wireless Communications

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“…Two types of RRM can be distinguished as static RRM [1][2][3][4] and dynamic RRM [2,[5][6][7][8][9][10]. This classification is based on whether the resource is managed dynamically.…”

Section: Introductionmentioning

confidence: 99%

Traffic characteristics based dynamic radio resource management in heterogeneous wireless networks

et al. 2014

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The traffic with tidal phenomenon in Heterogeneous Wireless Networks (HWNs) has radically increased the complexity of radio resource management and its performance analysis. In this paper, a Simplified Dynamic Hierarchy Resource Management (SDHRM) algorithm exploiting the resources dynamically and intelligently is proposed with the consideration of tidal traffic. In network-level resource allocation, the proposed algorithm first adopts wavelet neural network to forecast the traffic of each sub-area and then allocates the resources to those sub-areas to maximise the network utility. In connection-level network selection, based on the above resource allocation and the pre-defined QoS requirement, three typical network selection policies are provided to assign traffic flow to the most appropriate network. Furthermore, based on multidimensional Markov model, we analyse the performance of SDHRM in HWNs with heavy tailed traffic. Numerical results show that our theoretical values coincide with the simulation results and the SDHRM can improve the resource utilization.

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“…Therefore, it is essential to have a spectrum sharing scheme that defines an arbitration strategy between the SUs. While several studies have focused on devising efficient spectrum sharing mechanisms over resource-constrained CRNs, [10][11][12] it is interesting that most of the proposed mechanisms are problem-specific and lack generality. For example, a certain mechanism could be designed to deal with a certain limited resource, completely overlooking other types of resources that can arise dynamically in the network.…”

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confidence: 99%

A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints

Halloush

Almalkawi

et al. 2019

Trans Emerging Tel Tech

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Algorithms for spectrum sharing over resource‐limited cognitive radio networks are often designed to solve specific problems. This means that a certain algorithm deals specifically with a certain limited resource, and is not suitable for other resources. This limitation violates the software‐defined networking philosophy, where a scheme has to be reprogrammable to cope with different limited resources that can dynamically arise depending on network conditions. In this work, we investigate the problem of spectrum sharing in resource‐limited cognitive radio networks. Specifically, we introduce a novel spectrum sharing algorithm that is compatible with software‐defined networks, in the sense that it can be reprogrammed to support multiple constraints on resources of different types. A main feature of the proposed scheme is that computations could be distributed across multiple processing units to reduce computational complexity on each unit. In addition, the proposed algorithm is equipped with a fairness scheme. Simulation results demonstrate the efficacy of the proposed scheme.

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Learning to Compete for Resources in Wireless Stochastic Games

Cited by 88 publications

References 22 publications

Spectrum redistribution for cognitive radios using discriminatory spectrum double auction

Spectrum redistribution for cognitive radios using discriminatory spectrum double auction

Traffic characteristics based dynamic radio resource management in heterogeneous wireless networks

A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints

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