Performance Analysis of a Cognitive Radio Contention-Aware Channel Selection Algorithm

Mesοdiakaki, Agapi; Adelantado, Ferrán; Alonso, Luis; Verikoukis, Christos

doi:10.1109/tvt.2014.2341115

Cited by 38 publications

(24 citation statements)

References 29 publications

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“…The average sojourn time and the extended service time of the SU are analyzed, and the optimal spectrum handoff strategy is obtained through solving the optimization problem of minimizing both time metrics. In [3], a contention-aware channel selection algorithm is proposed, in which the secondary network detects the available licensed channels of SUs by exploiting cooperative spectrum sensing scheme, estimates the probability of channel collision, and then selects a less competitive channel for the SUs. In [4], a review of periodic sensing and handoff schemes is presented and the trade-off between the energy efficiency and the transmission reliability of SUs is examined.…”

Section: Spectrum Handoff Schemes Of Crnsmentioning

confidence: 99%

“…• Spectrum handoff scheme has been designed for single SU [2][3][4][5]. In this paper, we study the spectrum handoff problem of both single SU and multiple SUs.…”

Section: Introductionmentioning

confidence: 99%

“…• Spectrum handoff problem or resource allocation problem of SUs in CRNs has been studied independently in [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. In this paper, we jointly consider spectrum handoff and resource allocation problem of SUs in heterogeneous CRNs and design a joint optimal strategy for all the SUs in terms of handoff target channel selection and transmit power allocation.…”

Section: Introductionmentioning

confidence: 99%

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Energy efficiency-based joint spectrum handoff and resource allocation algorithm for heterogeneous CRNs

Chai

Qin

Chen

et al. 2016

J Wireless Com Network

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Cognitive radio networks (CRNs) are expected to improve spectrum utilization significantly by allowing secondary users (SUs) to opportunistically access the licensed spectrum of primary users (PUs). In an integrated network consisting of multiple heterogeneous CRNs, SUs with multiple interfaces may have to conduct inter-system or intra-system spectrum handoff due to the arrival of PUs or performance degradation on serving spectrum. In this case, designing an optimal spectrum handoff scheme which offers quality of service (QoS) guarantee and performance enhancement of the SUs is of particular importance. On the other hand, resource allocation strategy on target channel also plays an important role in affecting the transmission performance of handoff SUs. In this paper, we jointly design spectrum handoff and resource allocation strategy for handoff SUs in heterogeneously integrated CRNs. To achieve joint resource management among various CRNs, we propose a joint radio resource management architecture, based on which the proposed spectrum handoff and resource allocation scheme can be conducted. Jointly considering the transmission performance of the handoff SUs, we formulate the total energy efficiency of the SUs and design an optimization problem which maximizes the energy efficiency subject to spectrum handoff, QoS, and power constraints of the SUs. An iterative algorithm is proposed to solve the formulated nonlinear fractional optimization problem. Within each iteration, the optimization problem is transformed equivalently into two subproblems, i.e., power allocation subproblem of each SU-spectrum pair and spectrum handoff subproblem for all the SUs. The two subproblems are solved, respectively, through applying Lagrange dual method and the Kuhn-Munkres (K-M) algorithm. Numerical results demonstrate the efficiency of the proposed algorithm.

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Section: Spectrum Handoff Schemes Of Crnsmentioning

confidence: 99%

“…• Spectrum handoff scheme has been designed for single SU [2][3][4][5]. In this paper, we study the spectrum handoff problem of both single SU and multiple SUs.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy efficiency-based joint spectrum handoff and resource allocation algorithm for heterogeneous CRNs

Chai

Qin

Chen

et al. 2016

J Wireless Com Network

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“…To ensure non-interfering spectrum access, a CR operator can acquire spectrum availability information either through i) spectrum sensing or ii) by contacting a geo-location database [9]. In spectrum sensing technique, CR operator is responsible for spectrum sensing and managing its transmissions to avoid unnecessary interference.…”

Section: Introductionmentioning

confidence: 99%

Guaranteeing QoS Using Unlicensed TV White Spaces for Smart Grid Applications

Hassan

Tushar

Kerk³

et al. 2017

IEEE Wireless Commun.

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Abstract-In this paper, we consider the utilization of TV White Spaces (TVWS) by small Cognitive Radio (CR) network operators to support the communication needs of various smart grid applications. We first propose a multi-tier communication network architecture for smart metering applications in dense urban environments. Our measurement campaign, without any competition from other CR operators, reveals that the communication architecture can achieve more than 1Mbps data rates using the free unlicensed TVWS spectrum. However, anticipating stiff competition for the unlicensed TVWS spectrum among CR operators and to support smart grid applications with stringent Quality of Service (QoS) requirements, we further exploit the novel idea of high priority channels (HPC) that the CR operator can temporarily lease by paying a small fee. This poses several new challenges for the CR operators, such as, their economic viability while providing QoS guarantees. We develop a real-time decision support framework with several adjustable parameters for the CR operators that enables them to tradeoff HPC leasing cost and QoS. The developed algorithms are simple rules that provide significant opportunities to the CR operators to maintain a balance between spectrum cost and QoS depending on dynamic spectrum availability and smart grid application requirements.

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“…In that sense, the achievable throughput is a useful measure to assess how well a sensing strategy does in the STT, but it must be made clear that throughput figures so obtained are not really achievable. In real settings, coordination among SUs in the same network and coexistence mechanisms among different secondary networks will unavoidably reduce the maximum throughput that SUs can obtain [12].…”

Section: Introductionmentioning

confidence: 99%

Discrete time analysis of cognitive radio networks with imperfect sensing and saturated source of secondary users

Alfa

Pla

Martínez-Bauset

et al. 2016

Computer Communications

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Sensing is one of the most challenging issues in cognitive radio networks. Selection of sensing parameters raises several tradeoffs between spectral efficiency, energy efficiency and interference caused to primary users (PUs). In this paper we provide representative mathematical models that can be used to analyze sensing strategies under a wide range of conditions. The activity of PUs in a licensed channel is modeled as a sequence of busy and idle periods, which are represented as alternating Markov phase renewal processes. The representation of the secondary users (SUs) behavior is also largely general: the duration of transmissions, sensing periods and the intervals between consecutive sensing periods are modeled by phase type distributions, which constitute a very versatile class of distributions. Expressions for several key performance measures in cognitive radio networks are obtained from the analysis of the model. Most notably, we derive the distribution of the length of an effective white space; the distributions of the waiting times until the SU transmits a given amount of data, through several transmission epochs or uninterruptedly; and the goodput when an interrupted SU transmission has to be restarted from the beginning due to the presence of a PU.

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Performance Analysis of a Cognitive Radio Contention-Aware Channel Selection Algorithm

Cited by 38 publications

References 29 publications

Energy efficiency-based joint spectrum handoff and resource allocation algorithm for heterogeneous CRNs

Energy efficiency-based joint spectrum handoff and resource allocation algorithm for heterogeneous CRNs

Guaranteeing QoS Using Unlicensed TV White Spaces for Smart Grid Applications

Discrete time analysis of cognitive radio networks with imperfect sensing and saturated source of secondary users

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