Spectrum Leasing in Cognitive Radio Networks: A Matching Theory Approach

Vassaki, Stavroula; Poulakis, Marios I.; Panagopoulos, Athanasios D.

doi:10.1109/vtcspring.2015.7146101

Cited by 16 publications

(15 citation statements)

References 12 publications

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“…Based on the deferred acceptance (DA) algorithm, one-to-one and the one-to-many matching scheme were investigated in [26]. In one-to-one matching only, one PU collaborates with one SU resulting in a better gain for SUs.…”

Section: Background and Related Workmentioning

confidence: 99%

Efficient Channel Allocation using Matching Theory for QoS Provisioning in Cognitive Radio Networks

Rahim

Alfakeeh

Hussain

et al. 2020

Sensors

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The focus of research efforts in cognitive radio networks (CRNs) has primarily remained confined to maximizing the utilization of the discovered resources. However, it is also important to enhance the user satisfaction in CRNs by finding a suitable match between the secondary users and the idle channels available from the primary network while taking into consideration not only the quality of service (QoS) requirements of the secondary users but the quality of the channels as well. In this work, the Gale Shapley matching theory was applied to find the best match, so that the most suitable channels from the available pool were allocated that satisfy the QoS requirements of the secondary users. Before applying matching theory, two objective functions were defined from the secondary user’s perspective as well as from the channel’s perspective. The objective function of secondary users is the weighted sum of the data rate of the secondary users and the probability of reappearance of the primary user on the channel. Whereas, the objective function of the channel is the maximum utilization of the channel. The weight factors included in the objective functions allow for diverse service classes of secondary users (SUs) or varying channel quality characteristics. The objective functions were used in developing the preference lists for the secondary users and the idle channels. The preference lists were then used by the Gale Shapely matching algorithm to determine the most suitably matched SU-channel pairs. The performance of the proposed scheme was evaluated using Monte–Carlo simulations. The results show significant improvement in the overall satisfaction of the secondary users with the proposed scheme in comparison to other contemporary techniques. Further, the impact of changing the weight factors in the objective functions on the secondary user’s satisfaction and channel utilization was also analyzed.

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Section: Background and Related Workmentioning

confidence: 99%

Efficient Channel Allocation using Matching Theory for QoS Provisioning in Cognitive Radio Networks

Rahim

Alfakeeh

Hussain

et al. 2020

Sensors

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“…In Holfeld et al, 16 the stable matching theory is used in the downlink for cross-layer scheduling of a single cell. A spectrum leasing framework is proposed in Vassaki et al 20 where PU and CR are matched based on the preference list. Stable matching is also applied for physical layer security, 17 where transmitter and receiver are paired to a friendly jammer.…”

Section: Related Workmentioning

confidence: 99%

“…In Mochaourab et al, 19 channel allocation problem to CR is studied using matching theory and competitive markets where CR proposes to an administrator for channel access and administrator accepts or rejects the request of CR user to access the channel on the basis of the interference caused by CR to PU. A spectrum leasing framework is proposed in Vassaki et al 20 where PU and CR are matched based on the preference list. It proposes one-to-one and one-to-many matching between PU and CR.…”

Section: Related Workmentioning

confidence: 99%

Novel distributed algorithm for coalition formation in cognitive radio networks for throughput enhancement using matching theory

Tahir

Habaebi

Islam

2017

Int J Communication

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In this paper, a novel algorithm is proposed for increasing the throughput in cognitive radio networks by forming coalitions among cognitive radio users in AWGN and fading channel environment. Although there have been numerous studies exploring the benefits of coalition formation in cognitive radio networks from the game theory perspective, there are several limitations in their application. To overcome the limitations of game theory, concepts from matching theory are used. Specifically, the stable marriage problem is used to formulate the interactions among the cognitive radio users as a matching game for collaborative distributed spectrum sensing under target detection probability constraint. The utility function is defined as the average probability of false alarm per cognitive radio user. The advantage of stable marriage is that it always converges to a stable matching and is Pareto optimal when the preferences of cognitive radios are strict. In the proposed model, the stable matching problem is extended to propose a novel algorithm to form coalitions of varying sizes for improving the utility of CR (false alarm and throughput). The coalitions formed using the algorithm are stable and do not deviate from the final matching. Using simulations and mathematical analysis, it is shown that the proposed algorithm leads to stable coalitions and returns significant improvement in terms of reduced probability of false alarm and improved throughput per cognitive radio user as compared to the noncooperative scenario.

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“…Matching theory originally stems from economics, which is a suitable mathematical framework to analyze and optimize the allocation problem among users and spectrum resources [9]. The merit of matching theory lies in it can provide a distributed solution with tractable computation complexity.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Recently, matching theory [8] has emerged as a promising technique for allocation problem, which can overcome some limitations of the existing method [9], [10]. Matching theory is a Nobel Prize winning framework, which is suitable approach to model the interactions between agents in two distinct sets.…”

Section: Introductionmentioning

confidence: 99%