Optimal random access and random spectrum sensing for an energy harvesting cognitive radio

Shafie, Ahmed El; Sultan, Ahmed

doi:10.1109/wimob.2012.6379105

Cited by 25 publications

(62 citation statements)

References 20 publications

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“…The optimal spectrum sensing time and channel access probability are derived to minimise the access delay. In [14], Shafie and Sultan designed access schemes incorporating random spectrum sensing and random access for a secondary user (SU). The sensing and access probabilities are obtained such that the throughput of the SU is maximised under the condition that both the PU and SU queues are stable and that the PU queuing delay is kept lower than a specified value.…”

Section: Joint Optimisation Of Spectrum Sensing and Random Access In mentioning

confidence: 99%

“…Unlike [11][12][13][14], this paper characterises energy consumption and imposes an energy constraint for each sensor. In addition, the proposed PDI algorithm is also designed with the consideration of low overhead.…”

Section: Joint Optimisation Of Spectrum Sensing and Random Access In mentioning

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: Joint Optimisation Of Spectrum Sensing and Random Access In mentioning

confidence: 99%

Section: Joint Optimisation Of Spectrum Sensing and Random Access In mentioning

confidence: 99%

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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“…7 for N = 4 under different channel conditions. Under offline setting, the harvested energies for primary and secondary user considered are E p = [2,3,2,2] T J and E s = [4,5,5,3] T J respectively. The battery capacity is E max = 6 J and energy transfer efficiency, α is assumed to be 0.8.…”

Section: B Multi-slot Scenariomentioning

confidence: 99%

“…The SU not only accesses the channel in absence of PU but also transmits along with PU with a probability p. The authors obtained the optimal probability of access p that maximizes SU's throughput. In [5], authors considered an energy harvesting SU that senses and accesses the primary channel randomly, making use of primary's automatic repeat request (ARQ) feedback. The authors obtained optimal sensing and access probabilities maximizing the SU throughput under queue stability constraints of both the users and QoS constraint of PU.…”

Section: Introductionmentioning

confidence: 99%

On energy cooperation in energy harvesting underlay cognitive radio network

Pathak

Banerjee

2016

2016 Twenty Second National Conference on Communication (NCC)

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Abstract-In this paper, we consider an energy harvesting cognitive radio network (EH-CRN), where a primary and a secondary user coexist in underlay mode. Both the transmitters have energy harvesting capability and are equipped with finite capacity battery to store the harvested energy. In addition, the secondary user (SU) has an independent energy transfer unit such that it can transfer some portion of it's harvested energy to the primary user (PU). We obtain an optimal transmit power and energy transfer policy for single-slot and a suboptimal policy for multi-slot scenario maximizing the number of bits transmitted by SU under the primary sum-rate constraint in an offline setting. For both cases, the effect of energy cooperation on the system performance is studied and it is observed that energy cooperation results in higher SU throughput.

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Throughput of a cooperative energy harvesting secondary user in cognitive radio networks

Shafie

Khattab

2016

Trans. Emerging Tel. Tech.

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This paper investigates the maximum throughput of a rechargeable secondary user (SU) sharing the spectrum with a primary user (PU). The SU is assumed to be an energy harvesting node, which harvests energy packets from the environmental energy sources (e.g. solar, wind and acoustics) with a certain harvesting rate. The PU is equipped with a data buffer to store the randomly arrived data packets at its receiver. In addition to its own data buffer, the SU is equipped with another data buffer for storing the unsuccessfully decoded packets at the primary destination in a given time slot due to channel fading. Moreover, it has an energy buffer for storing the energy packets harvested from the environment. We propose a new cooperative cognitive relaying protocol that allows the SU to gain channel access when it relays a fraction of the undelivered primary packets. We consider an interference channel model [or a multipacket reception (MPR) channel model], where simultaneous transmissions can be decoded correctly at the corresponding destinations with certain decoding probabilities characterised by the complement of channel outages under interference. Our proposed protocol exploits the PU's data burstiness because of the random and sporadic packet arrival at its queue. Moreover, the proposed protocol takes advantage of the receivers' MPR capabilities. In addition, it efficiently expends the secondary energy packets under the objective of secondary throughput maximisation. Our numerical results show the benefits of cooperation, receivers' MPR capabilities and secondary energy queue arrival rate on the system's performance from a network layer standpoint.

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Optimal random access and random spectrum sensing for an energy harvesting cognitive radio

Cited by 25 publications

References 20 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

On energy cooperation in energy harvesting underlay cognitive radio network

Throughput of a cooperative energy harvesting secondary user in cognitive radio networks

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