Opportunistic spectrum access for energy-constrained cognitive radios

Hoang, Anh Tuan; Liang, Ying-Chang; Wong, David Tung Chong; Zeng, Yonghong; Zhang, Rui

doi:10.1109/twc.2009.080763

Cited by 132 publications

(67 citation statements)

References 9 publications

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“…In [16], Najimi et al generalised [15] by addressing the selection of best sensing nodes (with low complexity despite of the NP-complete nature of the problem) while minimising the energy consumption for spectrum sensing in CR networks. In [17], Hoang et al proposed an energy-aware spectrum-sensing policy that takes into account the dynamics of PUs and determines the spectrum sensing duration in order to optimise SUs' performance. In [18], Chen et al designed distributed spectrum sensing and access strategies for dynamic spectrum access under an energy constraint on the SU.…”

Section: Energy-aware Algorithms In Cr Networkmentioning

confidence: 99%

“…To achieve this, CRSNs have to perform spectrum sensing besides the information collection, and to design access schemes which produce strictly limited interference to primary users (PUs). On the other hand, different from CR networks [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26], CRSNs inherit the fundamental limitations of traditional WSNs whose lifetime and capabilities (e.g. computation, communication and storage) are strictly limited due to constrains in energy resources and low-cost hardwares.…”

Section: Introductionmentioning

confidence: 99%

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Utility‐based opportunistic spectrum access for cognitive radio sensor networks: joint spectrum sensing and random access control

et al. 2016

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This study formulates a novel optimisation problem for joint spectrum sensing and random access control (JS 2 RAC) in cognitive radio sensor networks (CRSNs). The JS 2 RAC is formulated as a network utility maximisation problem, which aims to maximise the sum of utilities over all links in the network but subject to the primary user protection constraint, the energy constraint, and the physical constraint. Due to the non-separable and non-convex nature of the JS 2 RAC problem, the authors propose a primal-decomposition-based iterative (PDI) algorithm which decomposes the JS 2 RAC problem into a spectrum sensing subproblem and a random access control subproblem, and solve the two subproblems iteratively. Then, the authors prove the convergence of the PDI algorithm and show its distributed implementation in practice. Simulations demonstrate the fast convergence and the near-optimal nature of the PDI algorithm and show its significant improvement in the network utility of CRSNs in comparison with the method of optimising spectrum sensing and random access separately.

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Section: Energy-aware Algorithms In Cr Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Utility‐based opportunistic spectrum access for cognitive radio sensor networks: joint spectrum sensing and random access control

et al. 2016

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“…Partially-Observed Markov Decision Process (POMDP) framework has been used in [1] and [2] to propose channel sensing and transmission strategies, under the assumption of slotted Primary and Secondary networks. This assumption requires synchronization between the Primary and Secondary transmission slot structures.…”

Section: Related Workmentioning

confidence: 99%

Opportunistic channel access scheme for cognitive radio system based on residual white space distribution

Sharma

Sahoo

2010

21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

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Abstract-We propose an opportunistic channel access scheme for cognitive radio-enabled secondary networks. In our work, we model the channel occupancy due to Primary User (PU) activity as a 2-state Alternating Renewal Process, with alternating busy and idle periods. Once a Secondary Node (SN) senses the channel idle, the proposed scheme uses the residual idle time distribution to estimate the transmission duration in the remaining idle time, subject to an acceptable PU interference constraint. The SN transmits the frames within the transmission duration without further sensing the channel, thereby reducing the average sensing overhead per transmitted frame. The analytical formulation used by the scheme does not require the SN to keep track of the start of the idle period. We validate the analytical formulations using simulations, and compare the performance of the proposed scheme with a Listen-Before-Talk (LBT) scheme.

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“…Selection of sensing parameters raises several tradeoffs involving achievable throughput, energy consumption and interference caused to PUs [4][5][6][7][8][9][10][11]. In general, more frequent and longer sensing periods reduce the achievable throughput, but increase the reliability of sensing and lower the interference that is caused to PUs.…”

Section: Introductionmentioning

confidence: 99%

“…This has brought energy consumption into the equation and some studies treated energy efficiency as a fundamental part of spectrum sensing [7,8,10,11]. As a matter of fact, energy efficiency has become a major issue in communication networks, not only for battery operated devices but for all type of devices and infrastructures [13].…”

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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Opportunistic spectrum access for energy-constrained cognitive radios

Cited by 132 publications

References 9 publications

Utility‐based opportunistic spectrum access for cognitive radio sensor networks: joint spectrum sensing and random access control

Utility‐based opportunistic spectrum access for cognitive radio sensor networks: joint spectrum sensing and random access control

Opportunistic channel access scheme for cognitive radio system based on residual white space distribution

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

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