Energy-efficient transmission with cooperative spectrum sensing in cognitive radio networks

Gao, Yan; Xu, Wenjun; Yang, K. H.; Niu, Kai; Lin, Jiaru

doi:10.1109/wcnc.2013.6554530

Cited by 14 publications

(6 citation statements)

References 10 publications

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“…In Li et al, 12 the authors have presented a comprehensive review on spectrum sensing and sensing time optimization. Cooperative sensing is used to optimize the sensing duration, the decision threshold level of an energy detector (ED), and the number of cooperative users in Gao et al 13 Various transmission strategies are investigated in context with EE optimization problems for a CR system. [14][15][16][17][18][19] In Pei et al, 14 multiband sensing is used, and transmission duration and SU power are optimized for a given number of channels while restricting interference to the PU.…”

Section: Prior Work and Motivationmentioning

confidence: 99%

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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Section: Prior Work and Motivationmentioning

confidence: 99%

Energy‐efficient framework for throughput enhancement of cognitive radio network

Bala

Ahuja

2021

Int J Communication

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“…The authors of [3] proved that EE is a concave function with respect to the transmission power, that an optimum always exists on the power axis, and that the optimum can be obtained by an iterative bisection search. Other contributions applied a golden section method to select an optimal sensing time ratio [21,22] and also to jointly determine the optimal transmission power allocation [21]. In addition, the authors of [1,23] investigated the PU's activities, especially towards the probability of the reactivation of the PU during SUs' transmission period, which might bring about additional collision risks between PU and SUs.…”

Section: Literature Analysismentioning

confidence: 99%

“…In addition, EE and sensing performance are also interconnected, and the sensing metrics and thresholds must be strictly respected while designing the CRN system with the aim of achieving a higher EE. Regarding this complex optimization problem, more detailed analyses of a PU's transmission power and its influences on SUs' sensing and transmission phases are presented in Section 1 by referring to some recently proposed methods [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Table 1 lists some common abbreviations that are used in this paper.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Primary User Power Impact for Joint Optimization of Energy Efficiency in Cognitive Radio Networks

et al. 2021

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Energy efficiency (EE) is of great concern in cognitive radio networks since the throughput and energy consumption of secondary users (SUs) vary with the sensing time. However, the conditions of the detection probability and false alarm probability should be respected to better protect primary users (PUs) and to improve the sensing performance of SUs. Additionally, the PUs’ minimum averaged power provision should also be regarded as a key problem of interactive linking to SUs. Therefore, an integrated design between the PU and SUs is desired for the coordination of the whole cognitive radio system, especially regarding the satisfaction of EE and performance metrics. This study formulates sensing constraints in a unified way and calculates the minimum SNR of SUs, based on which the essential PU power provision is computed. Furthermore, EE is proved as a decreasing function with the PU’s active ratio, where the maximum EE is obtained corresponding to the minimum QoS requirements of the sensing process. Hence, a bisection-based method is proposed to maximize EE, which is considered as a concave function of SUs’ sensing time and has only one unique optimum. EE’s optimization was analyzed under different fusion rules for diverse SNR conditions. The optimum was also studied with sensing performance targets for various cases of PU power provision.

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“…However, the aggregate value is relatively small compared with the power consumption of BS, whose radio frequency (RF) transmit power is often very large due to the large-scale pathloss fading. As a result, the power consumption of end devices is not considered in formula (12), in which the total system energy consumption is approximated as the power consumption at the secondary BS [7, 9–12].…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…Meanwhile, energy efficiency (EE) is also a key metric for 5G, in which energy consumption needs to be reduced on the order of several magnitudes [6]. Note that extra power consumption incurred by spectrum sensing makes the power saving issue more critical in CR systems [7, 8]. Until now, large amounts of researches have been conducted to study energy-efficient transmission in OFDM-based CR systems.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Layered Video Multicast over OFDM-Based Cognitive Radio Systems

Xu¹,

Li²,

Xu³

et al. 2015

International Journal of Distributed Sensor Networks

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An energy-efficient layered video multicast (LVM) scheme for “bandwidth-hungry” video services is studied in OFDM-based cognitive radio (CR) systems, where the video data is encoded into a base layer and several enhancement layers with the former intended for all subscribers to guarantee the basic quality of reconstructed video and the latter aiming at the quality improvement for the promising users with good channel conditions. Moreover, in order to balance user experience maximization and power consumption minimization, a novel performance metricenergy utility(EU) is proposed to measure the sum achieved quality of reconstructed video at all subscribers when unit transmit power is consumed. Our objective is to maximize the system EU by jointly optimizing the intersession/interlayer subcarrier assignment and subsequent power allocation. For this purpose, we first perform subcarrier assignment for base layer and enhancement layers using greedy algorithm and then present an optimal power allocation algorithm to maximize the achievable EU using fractional programming. Simulation results show that the proposed algorithms can adaptively capture the state variations of licensed spectrum and dynamically adjust the video transmission to exploit the scarce spectrum and energy resources adequately. Meanwhile, the system EU obtained in our algorithms is greatly improved over traditional spectrum efficiency (SE) and energy efficiency (EE) optimization models.

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Energy-efficient transmission with cooperative spectrum sensing in cognitive radio networks

Cited by 14 publications

References 10 publications

Energy‐efficient framework for throughput enhancement of cognitive radio network

Energy‐efficient framework for throughput enhancement of cognitive radio network

Evaluation of Primary User Power Impact for Joint Optimization of Energy Efficiency in Cognitive Radio Networks

Energy-Efficient Layered Video Multicast over OFDM-Based Cognitive Radio Systems

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