Admission Control and Channel Allocation for Supporting Real-Time Applications in Cognitive Radio Networks

Wang, Feng; Zhu, Jian‐Gang; Huang, Jianwei; Zhao, Yuping

doi:10.1109/glocom.2010.5684259

Cited by 14 publications

(27 citation statements)

References 14 publications

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“…(8) has been verified in Subsection IV-A, now we show the effectiveness of the proposed method as follows. 4 shows the blocking probabilities calculated by Eq.…”

Section: B Determination Of the Optimal Number Of Reserved Channelsmentioning

confidence: 97%

“…The simulated blocking probabilities and the numerical ones calculated by (8) are shown in Fig. 3, where the number of reserved channels T is set to be 3.…”

Section: A Verification Of the Analysismentioning

confidence: 99%

“…Most of the existing works on CAC in CRNs can be classified into two types. One is to design a CAC policy to maximize a predefined revenue parameter (such as average profit) through semi-Markov decision process (SMDP) [7] or Markov decision process (MDP) [8], [9]. The other is to find the optimal number of reserved channels which satisfies a certain call level QoS requirement, e.g., minimizing the dropping probability with a hard constraint on the blocking probability [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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Low-complexity channel reservation design scheme in cognitive radio networks

Wang

Huang

2012

2012 International Conference on Wireless Communications and Signal Processing (WCSP)

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In this paper, a low-cost channel reservation design scheme is proposed for cognitive radio networks (CRNs) to achieve call level quality of service (QoS) provisioning. An optimization problem is formed to minimize the handoff call dropping probability with a hard constraint on the new call blocking probability, and then is rewritten to another problem where only the new call blocking probability is involved. An analytical expression of the new call blocking probability is derived through modeling the channel occupancy situation of the system (including primary user network and CRN) through a one-dimension continuous time Markov chain instead of the commonly used two-dimension one. Then, the optimal number of reserved channels is determined by solving the aforementioned rewritten problem. The computational complexity of the proposed method is very low which enables its applications in high dynamic scenarios like CRNs. Simulation results verify the accuracy of the analytical expression and the effectiveness of the optimization.

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“…(8) has been verified in Subsection IV-A, now we show the effectiveness of the proposed method as follows. 4 shows the blocking probabilities calculated by Eq.…”

Section: B Determination Of the Optimal Number Of Reserved Channelsmentioning

confidence: 97%

“…The simulated blocking probabilities and the numerical ones calculated by (8) are shown in Fig. 3, where the number of reserved channels T is set to be 3.…”

Section: A Verification Of the Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low-complexity channel reservation design scheme in cognitive radio networks

Wang

Huang

2012

2012 International Conference on Wireless Communications and Signal Processing (WCSP)

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“…Thereafter, the ME-based DSAs cannot help the SUs further improve their rate performances since there is no extra idle spectrum for the PUs to lease. In such a situation, the PUs have the following two choices, i.e., (1) releasing some of the SUs through the user admission control mechanisms, e.g., [4], which may lead to the loss of the SUs, and, as a result, the long-term revenue loss of the PUs, and (2) resorting to the resource-exchange (RE) based DSAs, e.g., [5].…”

Section: Imentioning

confidence: 99%

Fair and efficient spectrum splitting for cooperative cognitive radio networks

Zhang

Yang

et al. 2012

2012 IEEE Global Communications Conference (GLOBECOM)

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T AbstractT -This paper considers the network situation where the primary users (PUs) in a cognitive radio network have leased out the idled spectrum to the secondary users (SUs) via pricing-based dynamic spectrum allocations (DSAs). We take into account that a SU can serve as a cooperative relay for a PU, and, then, stimulate the PU to split more spectrum while maintaining the minimum transmission rate of the PU. Without using pricingbased mechanisms again, a resource-exchange based bargaining game is proposed to develop the incentive mechanism. Considering multiple SUs should compete with each other for the newly-obtained spectrum from a PU, the novelty of the game scheme is in taking explicitly account of that each PU and SU have their own minimum rate demands. Simulation results show the game guarantees the minimum rate requirement for the PU, and, at the same time, ensures each SU can get a fair rate-reward from the PU according to the level of contribution that it can make to compensate the PU's rate-loss.

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“…In general, game theory plays a major role in studying the economical effects that CRNs could bring to the Service Providers (SPs), as well as the optimal radio resource management for the SPs when designing spectrum sharing rules and algorithms. Some examples of the applications of game theory in CRNs include auctionbased spectrum sharing [3], data pricing [4], power control and allocation [5] [6], Quality of Service (QoS) management [7] [8], and security [9].…”

Section: Introductionmentioning

confidence: 99%

Prospect Pricing in Cognitive Radio Networks

Yang

Park

Mandayam

et al. 2015

IEEE Trans. Cogn. Commun. Netw.

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Abstract-Advances in cognitive radio networks have primarily focused on the design of spectrally agile radios and novel spectrum sharing techniques that are founded on Expected Utility Theory (EUT). In this paper, we consider the development of novel spectrum sharing algorithms in such networks taking into account human psychological behavior of the end-users, which often deviates from EUT. Specifically, we consider the impact of end-user decision making on pricing and management of radio resources in a cognitive radio enabled network when there is uncertainty in the Quality of Service (QoS) guarantees offered by the Service Provider (SP). Using Prospect Theory (a NobelPrize-winning behavioral economic theory that captures human decision making and its deviation from EUT), we design data pricing and channel allocation algorithms for use in cognitive radio networks by formulating a game theoretic analysis of the interplay between the price offerings, bandwidth allocation by the SP and the service choices made by end-users. We show that, when the end-users under-weight the service guarantee, they tend to reject the offer which results in under-utilization of radio resources and revenue loss. We propose prospect pricing, a pricing mechanism that can make the system robust to decision making and improve radio resource management. We present analytical results as well as preliminary human subject studies with video QoS.

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Admission Control and Channel Allocation for Supporting Real-Time Applications in Cognitive Radio Networks

Cited by 14 publications

References 14 publications

Low-complexity channel reservation design scheme in cognitive radio networks

Low-complexity channel reservation design scheme in cognitive radio networks

Fair and efficient spectrum splitting for cooperative cognitive radio networks

Prospect Pricing in Cognitive Radio Networks

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