Energy efficient resource allocation for cognitive radio networks with imperfect spectrum sensing

Gao, Yan; Xu, Wenjun; Li, Shengyu; Niu, Kai; Lin, Jiaru

doi:10.1109/pimrc.2013.6666720

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…In the primary network, the central location of the PU is defined as c=16. Assume the activity probability POj equals 0.8·normalenormalxnormalp|-|j-c2/50 [14]. For ease of analysis, the detection Qdj is uniformly distributed over 0.90,0.99.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Therefore, considering power amplifier efficiency ρ∈][|0,1, the total circuit power consumption can be expressed as where PCS, PCR are denoted as the ST and SR circuit consumption which can be a constant. Like [14–18, 23], the EE measured by the “throughput-per-Joule” metric is defined as ratio of total throughput and total power; that is, normalEnormalEfalse(boldθfalse)≜RT|boldPboldS,boldPboldR/PTC. Hence, maximizing the average EE metric for the CORL system can be written as where |boldPboldS∗,boldPboldR∗≜}{|Ps,k∗,Pr,k∗∣k∈scriptKscriptU represents the optimal power allocation on ST and SR.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…Note that [13] also studied the EE optimization problem in CR system with imperfect spectrum sensing, but they all focus on the frame design including optimal sensing duration and data-transmission duration, as well as the optimal transmission power instead of the power allocation among each subcarrier. Besides, the authors in [14–16] analyze the EE performance of CRNs with imperfect spectrum sensing while relay strategies are not applied. Although a solution for EE-maximization problem in relay-aided CRN is proposed in [17], the authors only consider the ideal situation, that is, ignoring sensing errors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy-Efficient Power Optimization with Spectrum Sensing Errors in OFDMA Cognitive Opportunistic Relay Links

Feng

Kuang

et al. 2016

International Journal of Distributed Sensor Networks

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We propose a novel algorithm to optimize the energy efficiency (EE) of OFDM-based cognitive opportunistic relaying links (CORL) under secondary users (SUs) incorrectly sensing the unlicensed spectrum. We formulate an optimization problem with imperfect sensing that satisfies a specified power budget for the secondary users (SUs), while restricting the interference to primary user (PU) in a statistical manner. Unlike all related works in the literature, we consider the effect of subcarrier transmission mode on the relaying links and we additionally consider the effect of limited sensing capabilities of the SUs. The optimization problem is nonconvex and it is transformed to an equivalent problem using the concept of fractional programming. With the aid of the fractional programming method, an EE-oriented power allocation policy with low complexity is proposed which adopts the bisection method to speed up the search of the optimum. Simulation results show that the EE deteriorates as the channel sensing error increases. Comparisons with relevant works from the literature show that the EE is slightly deteriorated if the SU does not account for spectrum sensing errors.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: System Model and Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Efficient Power Optimization with Spectrum Sensing Errors in OFDMA Cognitive Opportunistic Relay Links

Feng

Kuang

et al. 2016

International Journal of Distributed Sensor Networks

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“…In [15], resource allocation problem with the imperfect spectrum sensing is considered to maximize capacity of SU. Considering OFDM-based CRNs under the spectrum sensing uncertainties, PC problem is studied in [16,17]. In [16], the authors investigate the energy efficient resource allocation strategy to maximize energy efficiency of CR system subject to total transmission power budget and each PU interference constraints.…”

Section: Introductionmentioning

confidence: 99%

“…Considering OFDM-based CRNs under the spectrum sensing uncertainties, PC problem is studied in [16,17]. In [16], the authors investigate the energy efficient resource allocation strategy to maximize energy efficiency of CR system subject to total transmission power budget and each PU interference constraints. In [17], for an OFDM-based heterogeneous CRN including single network and multihoming network, the resource allocation problem with the imperfect spectrum sensing is solved to maximize system capacity and the joint subcarrier, and PC problem is formulated under total transmission power constraint, interference constraint, and QoS constraint.…”

Section: Introductionmentioning

confidence: 99%

Power control for cognitive relay networks with sensing uncertainties

Zhao

2016

J Wireless Com Network

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Power control (PC) is a key solution to enable spectrum sharing between secondary users (SUs) and primary users (PUs). However, previous research lacks sensing uncertainties for the status of PUs. In this article, we focus on the PC problem for a cognitive relay network under the spectrum sensing uncertainties to minimize the total bit error rate (BER) of SUs under the constraints of maximum transmit power budgets, signal-to-interference-and-noise ratio (SINR) constraints, and interference requirements to provide protection for PUs. We first formulate the interference model by taking sensing uncertainties into account, while the worst-channel-state-information (worst-CSI) PC algorithm is introduced to limit the BER of SUs, which only needs to operate the algorithm in one link whose CSI is worst. And a cooperative spectrum sensing (CSS) strategy is considered to optimize the sensing performance. To deal with the optimization problem, the original min-max BER problem is converted into an equivalent max-min SINR problem solved by Lagrange dual decomposition method. Finally, simulation results are presented to indicate that our proposed algorithm can obtain good BER performance and guarantee quality of service of PU.

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Energy‐efficient framework for throughput enhancement of cognitive radio network

Bala

Ahuja

2021

Int J Communication

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Summary Recently, cognitive radio (CR) has evolved as a next‐generation communication technology that has the potential to use the radio frequency (RF) spectrum in most efficient manner as compared to the other existing cellular communication technologies. The performance of CR system largely depends upon the accuracy of the sensing results. However, the sensing ambiguities such as false alarm and miss detection cause spectrum underutilization and severe interference to the primary user (PU), respectively. In this paper, both issues are addressed by modifying the conventional frame structure by introducing two sensing slots along with a transmission slot to overcome sensing ambiguities. For the targeted probability of detection, the first sensing slot is kept small and fixed, and sensing results are stored in a flag bit up to that duration. The second sensing slot (optional) is used whenever the flag bit status varies in the current frame from the previous frame. Considering sensing throughput trade‐off issue of CR, the second sensing slot is optimized to enhance the effective throughput and energy efficiency of the secondary communication system. The simulation results are presented to validate the effectiveness of the proposed scheme with a modified frame structure, which is capable to achieve 65% higher energy efficiency than the conventional scheme when PU is present in a channel, that is, P(H1) = 1.

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Energy efficient resource allocation for cognitive radio networks with imperfect spectrum sensing

Cited by 8 publications

References 12 publications

Energy-Efficient Power Optimization with Spectrum Sensing Errors in OFDMA Cognitive Opportunistic Relay Links

Energy-Efficient Power Optimization with Spectrum Sensing Errors in OFDMA Cognitive Opportunistic Relay Links

Power control for cognitive relay networks with sensing uncertainties

Energy‐efficient framework for throughput enhancement of cognitive radio network

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