Analysis of Cognitive Radio Networks with Channel Aggregation

Abstract-In cognitive radio networks with multiple channels, various channel assembling strategies may be applied to secondary users, resulting in different achieved capacity. However, there is no previous work on determining the capacity upper bound of channel assembling for secondary users under given system configurations. In this paper, we derive the maximum capacity for cognitive radio networks with channel assembling through Markov chain modeling, considering that PU activities are relatively static compared with SU services. We first deduce a closed-form expression for the maximum capacity in a dynamic channel assembling strategy, and then demonstrate that no other channel assembling strategy can provide higher capacity than the one achieved by this dynamic strategy.

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“…Note that L(r) m=1 P r−1,n,m = 1 and , l)). If we sum up all these equations of states with r SU services, (8) holds.…”

Section: Capacity Of General Strategies In the Qsrmentioning

confidence: 99%

Capacity Upper Bound of Channel Assembling in Cognitive Radio Networks With Quasistationary Primary User Activities

Jiao

Song

Pla

et al. 2013

IEEE Trans. Veh. Technol.

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“…In this context, channel aggregation (also known as channel assembling) means that two or more idle channels are combined together as one channel to provide higher data rate and, thus, reduce the total transmission time of secondary elastic traffic calls [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. On the contrary, elastic traffic may flexibly adjust downward the number of assembled channels to provide, for instance, immediate access to secondary real-time traffic when needed [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. 1 Additionally, other relevant mechanisms are identified in the literature to overcome the impact of service interruption of SUs in CRNs [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…Surprisingly, transmission delay has not been previously considered as performance metric [10][11][12][13][14][15][16][17][18][19][20][21][22][23]28]. Moreover, previous studies that address CRNs with heterogeneous traffic and spectrum adaptation [10][11][12][13][14][15][16][17][18][19][20][21][22][23] have not considered the use of call buffering, which is a relevant mechanism to exploit the elasticity of best effort traffic in benefit of both real-time and elastic secondary sessions. Exception of this are [28,33,34]; however, in these works, neither Erlang capacity nor transmission delay is considered.…”

Section: Introductionmentioning

confidence: 99%

Analysis and performance evaluation of resource management mechanisms in heterogeneous traffic cognitive radio networks

Castellanos-Lopez¹,

Cruz-Perez

Hernandez-Valdez³

et al. 2017

J Wireless Com Network

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In this paper, the Erlang capacity achieved by the separate or joint use of several resource management mechanisms commonly considered in the literature (spectrum aggregation, spectrum adaptation, call buffering, channel reservation, selective interruption, and preemptive prioritization) to mitigate the effects of secondary call interruptions in cognitive radio networks (CRNs) is evaluated and compared. Heterogeneous traffic is considered, and service differentiation between real-time and elastic (data) traffic is done in terms of their different delay tolerance characteristics. The aim of our investigation is to identify the most relevant resource management mechanisms to improve the performance of the considered networks. As such, both the individual and joint effect of each resource management mechanisms on system performance are evaluated with the objective of comparing the gains in capacity achieved by each resource management mechanism studied in this work. For this purpose, the different resource management mechanisms studied are carefully combined and, for each resulting strategy, optimization of its configuration is presented to maximize the achievable Erlang capacity. For the performance evaluation of the considered strategies in heterogeneous traffic CRNs, a general teletraffic analysis is developed. Numerical results show that spectrum adaptation and call buffering are the mechanisms that best exploit the elasticity of delay-tolerant traffic in heterogeneous traffic CRNs and, therefore, most significantly improve system performance.

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“…The first phase is MAC protocol design itself, which aims at proposing feasible schemes to make CRNs access spectrum more efficiently [2][3][4]. The second phase is to build analytical models in order to help us better understand the dynamics behind these schemes and evaluate the performance of different strategies [5][6][7][8][9]. In this study, we mainly focus on the second phase and analyze the performance of channel aggregation represented by two new strategies, i.e., the Greedy strategy and the Dynamic strategy, when spectrum adaptation is enabled.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in [5][6][7], the performance of an SU network when a channel for Primary Users (PUs) can be divided into several channels for SUs is analyzed based on a Continuous Time Markov Chain (CTMC) model. In [8,9], the performance of several channel aggregation strategies when spectrum adaptation is not enabled is studied through CTMC models. However, none of them analyze channel aggregation with spectrum adaptation systematically through mathematical modeling.…”

Section: Introductionmentioning

confidence: 99%

Greedy versus Dynamic Channel Aggregation Strategy in CRNs: Markov Models and Performance Evaluation

Jiao

Pla

2011

NETWORKING 2011 Workshops

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Abstract. In cognitive radio networks, channel aggregation techniques which aggregate several channels together as one channel have been proposed in many MAC protocols. In this paper, we consider elastic data traffic and spectrum adaptation for channel aggregation, and propose two new strategies named as Greedy and Dynamic respectively. The performance of channel aggregation represented by these strategies is evaluated using continuous time Markov chain models. Moreover, simulation results based on various traffic distributions are utilized in order to evaluate the validity and preciseness of the mathematical models.

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Analysis of Cognitive Radio Networks with Channel Aggregation

Cited by 35 publications

References 9 publications

Capacity Upper Bound of Channel Assembling in Cognitive Radio Networks With Quasistationary Primary User Activities

Capacity Upper Bound of Channel Assembling in Cognitive Radio Networks With Quasistationary Primary User Activities

Analysis and performance evaluation of resource management mechanisms in heterogeneous traffic cognitive radio networks

Greedy versus Dynamic Channel Aggregation Strategy in CRNs: Markov Models and Performance Evaluation

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