Energy harvesting with adaptive transmit power for cognitive radio networks

Halima, Nadhir Ben; Boujemâa, Hatem

doi:10.1007/s11235-019-00548-w

Cited by 4 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Halima and Boujemâa (11) derived a new expression for Packet Error Probability (PEP) of several relay techniques when unlicensed users harvest licensed user RF signals and also presented the use of adaptive power to avoid interference. In this method, the proposed model adds several intermediary decision-making relays to the network; in each time frame of information transmission, one part of a slot is dedicated to harvesting energy and the other to sensing the network.…”

Section: Related Workmentioning

confidence: 99%

Optimal Node Selection for Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks with Energy Harvesting

Al-bosham,

Ebrahimzadeh,

Bagheri

2024

IJE

View full text Add to dashboard Cite

5G communication technology supports the Internet of Things, remote health care centers, and cloud computing by tuning their communication services over a very wide range of frequency bands with lowcost, low-battery consumption, and low latency. However, the development of such wireless technology is highly dependent on radio frequency spectra. The Cognitive Radio Sensor Network (CRSN) is an excellent candidate to improve radio spectrum utilization and manage the heavy communication data traffic in 5G wireless networks. CRSN can sense the frequency channels, making it possible for secondary users (who are denied service) to use the free channels. Despite the outstanding features of CRSNs, some limitations overshadow their performance. The most critical limitation is energy and its optimal consumption to increase the network's lifetime. Recent research has shown that energy harvesting can be an effective way to increase the lifetime of CRSNs. However, the sensors should sense the frequency spectrum with a high success rate. In this paper, several optimal sensor nodes using energy harvesting with the approach of increasing the network's lifetime are proposed to solve the mentioned challenge. This way, the sensor nodes are divided into two independent groups for simultaneous spectrum sensing and energy harvesting in each time frame. We will solve this problem based on mathematical optimization and the use of proposed solutions for convex problems. Finally, simulations are developed to evaluate the ability of the proposed solution, assuming the systems use IEEE802.15.4/Zigbee and IEEE802.11af.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimal Node Selection for Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks with Energy Harvesting

Al-bosham,

Ebrahimzadeh,

Bagheri

2024

IJE

View full text Add to dashboard Cite

show abstract

“…However, the technique suffers from poor detection rates at low PFA and signal to noise ratio (SNR) due to only one PU branch used. Energy harvesting with adaptive transmit power for cognitive radio network is proposed in [25] to address the problem of energy constraint in a cognitive radio network using radio frequency energy harvesting and adaptive power transmission (APT). Channel state information (CSI) and secondary user (SU) transmitting power were adjusted based on channel gain, a CSI function using APT.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of spectrum sensing technique with energy harvesting for cognitive radio network

Okediran

Ojo

et al. 2022

IJEECS

View full text Add to dashboard Cite

Spectrum Sensing (SS) is cognitive radio's (CR’s) fundamental and essential mechanism to locate idle spectrum. Multipath effects in wireless channels, on the other hand, reduce CR's sensing accuracy, resulting in a significant risk of missing data. Optimal Spectrum Sensing Technique with Energy Harvesting (OSSTEH) used to solve this problem suffers from poor performance due to only one Primary User (PU) branch used. Hence, in this paper, an enhanced SS with energy harvesting technique is proposed using Maximal Ratio Combiner (MRC). The multiple copies of the PU signal were received using 'N' number of secondary users (SU) antennas and divided into two equal fractions. The first fraction was used for spectrum sensing, while the latter was used for energy harvesting. The first set of SU antennas received a PU signal, which was combined using MRC, and the output of the combiner was used as an input to the energy detector to calculate the received signal's energy. To assess the existence or absence of the PU signal, the acquired energy was compared to the threshold limit. The enhanced technique was evaluated using the probability of detection and signal strength compared with conventional OSSTEH. The enhanced technique gave a high detection rate with an increase in charging rate than the conventional OSSTEH.

show abstract

Energy harvesting with adaptive transmit power for multi-antenna multihop cognitive radio networks

Halima

Boujemâa²

2021

Sustainable Computing: Informatics and Systems

View full text Add to dashboard Cite

Energy harvesting with adaptive transmit power for cognitive radio networks

Cited by 4 publications

References 32 publications

Optimal Node Selection for Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks with Energy Harvesting

Optimal Node Selection for Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks with Energy Harvesting

Enhancement of spectrum sensing technique with energy harvesting for cognitive radio network

Energy harvesting with adaptive transmit power for multi-antenna multihop cognitive radio networks

Contact Info

Product

Resources

About