Subchannel and resource allocation in cognitive radio sensor network with wireless energy harvesting

Liu, Zhixin; Zhao, Mingye; Yuan, Yazhou; Guan, Xinping

doi:10.1016/j.comnet.2019.107028

Cited by 25 publications

(12 citation statements)

References 30 publications

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“…Allocation of the M2M Communication System in 5G Era [9]. In the process of evolving from the third generation mobile communication system to the fourth generation mobile communication system, the LTE standard based on orthogonal frequency division multiplexing and multiple input multiple output is formed [10].…”

Section: Algorithm Methods Of Radio Resourcementioning

confidence: 99%

Radio Resource Allocation Algorithm of the M2M Communication System in 5G Era

She

2022

Wireless Communications and Mobile Computing

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M2M is a concept and a general term for all technologies that enhance the communication and network capabilities of machines and equipment. It is a combination of different types of communication technologies. M2M enables all devices and users to share information, saving operation time and improving work efficiency. M2M is not a simple data transmission, but it is an intelligent and interactive communication between machines. Even if no real-time signal is sent, the machine will actively communicate according to the established program and select intelligently according to the obtained data. The communication system refers to the general term of the technology that completes the information transmission function. The modern communication system is mainly realized by the propagation of electromagnetic waves in free space or the transmission mechanism in the guiding medium. When the wavelength of the electromagnetic wave reaches the light wave range, such a telecommunication system is called an optical communication system, and the communication system in the other electromagnetic wave range is called an electromagnetic communication system, abbreviated as a telecommunication system. The purpose of this paper is to explore the study of wireless data sharing method for M2M mobile telecommunication systems in the 5G era. It is expected that the new algorithm will make the wireless resource allocation more reasonable and improve the resource utilization rate. The performance of the deterministic measurement matrix proposed in this paper is close to or even better than that of the random measurement matrix. The M-SP algorithm greatly improves the performance of the SP algorithm. The experimental results of this paper show that when the number of microbase stations is 10, the energy consumption of the direct algorithm is 175000 W, the energy consumption of the indirect algorithm is 350000 W, and the energy consumption of the direct algorithm is much smaller than that of the indirect algorithm.

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Section: Algorithm Methods Of Radio Resourcementioning

confidence: 99%

Radio Resource Allocation Algorithm of the M2M Communication System in 5G Era

She

2022

Wireless Communications and Mobile Computing

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“…However, each node is configured with 2 antennas for EH and data delivery, respectively, which is not applicable to single-antenna CRSNs nodes. A sub-channel and resource allocation strategy based on spectrum lease is proposed for CRSNs, and outage probability is taken into consideration to ensure network robustness in complex environment 31 . However, it is only suitable for single-hop CRSNs.…”

Section: Related Workmentioning

confidence: 99%

“…Vol. :(0123456789) www.nature.com/scientificreports/ When SCA is applied, a feasible point ω of the original problem (P8) is randomly generated, and the first order Taylor expansion of (31) at ω is leveraged to construct its approximate function. According to the first order condition of convex function, its first order Taylor expansion at a given point is globally lower bounded.…”

Section: Subsurface Partition-based Eh Scheme For Irs-aided Eh-crsnsmentioning

confidence: 99%

RF energy harvesting schemes for intelligent reflecting surface-aided cognitive radio sensor networks

Wang

2022

Sci Rep

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Energy harvesting (EH) is a potential solution to enhance the node sustainability and prolong the network lifetime of cognitive radio sensor networks (CRSNs). However, CRSNs nodes can only harvest energy from the direct link with energy sources, and severe path loss results in low energy utilization ratio. To solve the above problem, intelligent reflecting surface (IRS) is introduced, and a shared reflection coefficient matrix-based EH scheme is proposed for IRS-aided CRSNs in this paper. An optimization problem with the objective of maximizing the total amount of energy harvested by all CRSNs nodes is formulated, and by optimally adjusting the IRS reflection coefficient, CRSNs nodes can harvest energy from both the direct link and the cascaded reflection link via IRS, which increases the amount of harvested energy. In addition, a subsurface partition-based EH scheme is proposed to reduce the additional computational complexity brought by increasing IRS elements or CRSNs nodes. Simulation results show that the proposed schemes can both dramatically improve energy utilization ratio, and the subsurface partition-based EH scheme will bring in less than 1 percent performance loss when compared with the other scheme, i.e., reasonable subsurface partition can achieve a balance between harvested energy and computational complexity.

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“…The new idea of a crucial technology, Cognitive Radio, solved this first and primarily essential problem (CR). Cognitive radio is a smart radio network that outperforms traditional radio in terms of capabilities [4][5]. These CR features may help wireless networks address problems including congested spectrum communication in ISM bands, decreased interference, and interference avoidance.…”

Section: Introductionmentioning

confidence: 99%

Design of an adaptive CRSN using OFDM

Kandasamy¹,

Ravi²

2022

Wireless Netw

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Cognitive Radio (CR) is a novel concept that enables wireless devices to detect and adapt to their surroundings in order to enhance communication quality. The cognitive radio sensor network (CRSN) has proved to be a cost-effective solution to the spectrum constraints that wireless sensor networks (WSN). Optimizing the optimum packet size is regarded to be an essential energy constrained issue to address the practical implementation of CRSN out of all the difficulties. Small packets generate data traffic in device-to-device communication, a flexible way for transferring data in wireless networks, while big packets may cause data bit corruption, requiring retransmission at a greater frequency. This will not allow access from the secondary network to the main network, since it may cause further disturbance. To maximise the WSN's energy efficiency, the optimum packet size for CRSN should be utilised while keeping the same degree of interference as the primary licenced users (PU). The purpose of this article is to examine formulations for small, medium, and large packet sizes in order to determine the optimum packet size for adaptive CRSN. To do so, CR requires a flexible physical layer capable of carrying out the necessary tasks. This article examines the performance of CR systems that use the Orthogonal Frequency Division Multiplexing (OFDM) technique, which is a possible transmission technology for CR. Interference delays are minimised, and the channels are ultimately utilised effectively. This article shows that medium-sized packets are the optimum choice for achieving the greatest performance on the Cognitive Radio Sensor Network (CRSN). The Jellyfish Search Optimization algorithm (JSO) and the hybrid Momentum Search Algorithm (MSA) are hybridised, and results are achieved. This makes it possible to calculate precise packet sizes. The suggested approach decision outperforms existing methods like the Group Sparse Optimization algorithm and the Throughput Maximization Algorithm. The MATLAB / SIMULINK Platform were used to get the results.

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Subchannel and resource allocation in cognitive radio sensor network with wireless energy harvesting

Cited by 25 publications

References 30 publications

Radio Resource Allocation Algorithm of the M2M Communication System in 5G Era

Radio Resource Allocation Algorithm of the M2M Communication System in 5G Era

RF energy harvesting schemes for intelligent reflecting surface-aided cognitive radio sensor networks

Design of an adaptive CRSN using OFDM

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