Optimal Spectrum Sensing Interval in Cognitive Radio Networks

Xing, Xiaoshuang; Jing, Tao; Li, Hongjuan; Huo, Yan; Cheng, Xiuzhen; Znati, Taieb

doi:10.1109/tpds.2013.155

Cited by 45 publications

(6 citation statements)

References 19 publications

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“…As a compromise, the period between two consecutive SS operations is configured based on the accepted energy efficiency of the system and the tolerable collision time with PU. Another parameter impacting the period between two consecutive SS operations is the probability that the PU returns active [73,74]. When the PU is more likely to be absent for a long time, continuous monitoring is not efficient.…”

Section: Transmit-sensementioning

confidence: 99%

“…This map guides the spectrum access and enhances the SS performance [105]. Using the prediction of the spectrum vacancy, SU can wisely select the communication channels to reduce handover rate and avoid interference [73,106,107]. By contrast, when a handover happens, SU may target a channel where PU is expected to be absent to make SS before accessing [108].…”

Section: Local Spectrum Sensingmentioning

confidence: 99%

See 1 more Smart Citation

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Nasser

Hassan

Chaaya

et al. 2021

Sensors

106

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Spectrum Sensing (SS) plays an essential role in Cognitive Radio (CR) networks to diagnose the availability of frequency resources. In this paper, we aim to provide an in-depth survey on the most recent advances in SS for CR. We start by explaining the Half-Duplex and Full-Duplex paradigms, while focusing on the operating modes in the Full-Duplex. A thorough discussion of Full-Duplex operation modes from collision and throughput points of view is presented. Then, we discuss the use of learning techniques in enhancing the SS performance considering both local and cooperative sensing scenarios. In addition, recent SS applications for CR-based Internet of Things and Wireless Sensors Networks are presented. Furthermore, we survey the latest achievements in Spectrum Sensing as a Service, where the Internet of Things or the Wireless Sensor Networks may play an essential role in providing the CR network with the SS data. We also discuss the utilisation of CR for the 5th Generation and Beyond and its possible role in frequency allocation. With the advancement of telecommunication technologies, additional features should be ensured by SS such as the ability to explore different available channels and free space for transmission. As such, we highlight important future research axes and challenging points in SS for CR based on the current and emerging techniques in wireless communications.

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Section: Transmit-sensementioning

confidence: 99%

Section: Local Spectrum Sensingmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Nasser

Hassan

Chaaya

et al. 2021

Sensors

106

View full text Add to dashboard Cite

show abstract

“…The parameters of the HMM are determined by solving the following optimization problem, given as [25,26] λ = 𝜋, 𝐴, 𝐵 = arg max (𝜋,𝐴,𝐵)…”

Section: Algorithm For Hmm Parameter Estimationmentioning

confidence: 99%

“…However, according to works in Refs. [23]- [25], activities of PUs are not random and the duration of a PU's activity usually lasts a number of time slots. Therefore, performing optimization at each slot not only wastes energy, but also loses opportunities for the SU to transmit signal (the SU can use the time devoted to performing optimization to transmit data).…”

Section: Introductionmentioning

confidence: 99%

Optimal satisfaction degree in energy harvesting cognitive radio networks

Li¹,

Liu²,

Si³

et al. 2015

Chinese Phys. B

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A cognitive radio (CR) network with energy harvesting (EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model (HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree (WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user (SU) and the interference to the primary user (PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming (MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution (DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service (Qos). Numerical results are given to verify our analysis.

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“…In a CR-based network (CRN), there are two kinds of users: (1) primary users (PUs) and (2) CR users or secondary users (SUs). CRs are only allowed to use the spectrum unused by the PU [30,31,32]. Without loss of generality, we keep the same terms of PUs and CRs for our cognitive IoT-enabled smart building system, i.e., the mobile users, IoT devices and networking monitors (NMs) deployed inside the building are referred to as CRs or SUs and the few mobile terminals as PUs.…”

Section: Introductionmentioning

confidence: 99%

Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques

Aslam

Hasan

Shahid

et al. 2016

Sensors

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The Internet of Things (IoT) has gained an incredible importance in the communication and networking industry due to its innovative solutions and advantages in diverse domains. The IoT’ network is a network of smart physical objects: devices, vehicles, buildings, etc. The IoT has a number of applications ranging from smart home, smart surveillance to smart healthcare systems. Since IoT consists of various heterogeneous devices that exhibit different traffic patterns and expect different quality of service (QoS) in terms of data rate, bit error rate and the stability index of the channel, therefore, in this paper, we formulated an optimization problem to assign channels to heterogeneous IoT devices within a smart building for the provisioning of their desired QoS. To solve this problem, a novel particle swarm optimization-based algorithm is proposed. Then, exhaustive simulations are carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrate the supremacy of our proposed algorithm over the existing ones in terms of throughput, bit error rate and the stability index of the channel.

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Optimal Spectrum Sensing Interval in Cognitive Radio Networks

Cited by 45 publications

References 19 publications

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Optimal satisfaction degree in energy harvesting cognitive radio networks

Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques

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