A fast spectrum sensing for CP-OFDM cognitive radio based on adaptive thresholding

Berbra, Kamel; Barkat, M.; Gini, Fulvio; Greco, Maria; Stinco, Pietro

doi:10.1016/j.sigpro.2016.04.008

Cited by 31 publications

(9 citation statements)

References 28 publications

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“…In a cognitive radio with intelligent antenna, a transmitter/receiver is designed to intelligently detect whether a particular segment of the radio spectrum is currently in use or not and to access (and out of, as necessary) the temporarily unused spectrum very rapidly, without interfering with the transmissions of other authorized users [14,15]. Smart Antenna (also known as adaptive array antenna, multiple antennas and MIMO) are antenna arrays with smart processing algorithm used to identify special signal signature such as the direction of arrival of the signal to track and locate the antenna beam on the target [16,17,18].…”

Section: Cognitive Radio and Intelligent Antennamentioning

confidence: 99%

Dynamic Resource Allocation and Priority Based Scheduling for Heterogeneous Services in Cognitive Radio Networks

Santhamurthy¹,

Parasuraman²

2016

IJIES

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As a futuristic solution to the serious scarcity of spectrum and radio resources in today's scenario, cognitive radio offers great solutions. Cognitive radio is a groundbreaking approach in improving the utilization of precious natural resources. Cognitive radio network (CRN) is an intelligent wireless communication system that is aware of its environment. The problem of spectrum sensing by heterogeneous nodes, with varying computing power and sensing range, and spectrum allocation in CRN where there exist multiple primary users and secondary users have been considered. Focus has been laid on optimum resource allocations and scheduling for primary user (PU) and secondary user (SU) to maximize the performance of the whole network by reducing the interference at PU and also maximizing the throughput and minimizing the risk of overlapping the coverage of CRNs. The effectiveness of DRAPBS-CRN algorithm is compared with DRA-CRN and is found to be 26% more efficient using ns-2 simulation.

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Section: Cognitive Radio and Intelligent Antennamentioning

confidence: 99%

Dynamic Resource Allocation and Priority Based Scheduling for Heterogeneous Services in Cognitive Radio Networks

Santhamurthy¹,

Parasuraman²

2016

IJIES

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“…Traditional spectrum sensing is mainly done by mathematical methods of signal processing, including energy detection [11], matched filter detection [12], and cyclostationary feature detection [13]. [14] proposed a spectrum sensing method based on correlation detection, the correlation of cyclic prefix (CP) was used in OFDM, and the sampled data was subjected to correlation operation. In [15], the signal and noise were estimated simultaneously by the time domain correlation function, and the estimated threshold was continuously adjusted by the estimated value to complete the spectrum sensing of an OFDM signal.…”

Section: Introductionmentioning

confidence: 99%

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

Pan

Lin

2020

International Journal of Digital Multimedia Broadcasting

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In a cognitive radio network (CRN), spectrum sensing is an important prerequisite for improving the utilization of spectrum resources. In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. Firstly, we analyze the cyclic autocorrelation of an OFDM signal and the cyclic spectrum obtained by the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM), and the cyclic spectrum is normalized to gray scale processing to form a cyclic autocorrelation gray scale image. Then, we learn the deep features of layer-by-layer extraction by the improved CNN classic LeNet-5 model. Finally, we input the test set to verify the trained CNN model. Simulation experiments show that this method can complete the spectrum sensing task by taking advantage of the cycle spectrum, which has better spectrum sensing performance for OFDM signals under a low signal-noise ratio (SNR) than traditional methods.

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“…A licensed primary network (PN) can use the frequency band at any time for signal transmission, whereas the secondary network (SN) senses the frequency band to make opportunistic transmissions. An underlay protocol was also proposed [4,5,6]. With this protocol, the SN can share the frequency band and the operation time with the PN, as long as its signal does not affect the signal of the PN.…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Allocation of Relay Sensor Node Capable of Energy Harvesting in Cooperative Cognitive Radio Network

Har

Cho

Kong

2017

Sensors

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A cooperative cognitive radio scheme exploiting primary signals for energy harvesting is proposed. The relay sensor node denoted as the secondary transmitter (ST) harvests energy from the primary signal transmitted from the primary transmitter, and then uses it to transmit power superposed codes of the secrecy signal of the secondary network (SN) and of the primary signal of the primary network (PN). The harvested energy is split into two parts according to a power splitting ratio, one for decoding the primary signal and the other for charging the battery. In power superposition coding, the amount of fractional power allocated to the primary signal is determined by another power allocation parameter (e.g., the power sharing coefficient). Our main concern is to investigate the impact of the two power parameters on the performances of the PN and the SN. Analytical or mathematical expressions of the outage probabilities of the PN and the SN are derived in terms of the power parameters, location of the ST, channel gain, and other system related parameters. A jointly optimal power splitting ratio and power sharing coefficient for achieving target outage probabilities of the PN and the SN, are found using these expressions and validated by simulations.

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A fast spectrum sensing for CP-OFDM cognitive radio based on adaptive thresholding

Cited by 31 publications

References 28 publications

Dynamic Resource Allocation and Priority Based Scheduling for Heterogeneous Services in Cognitive Radio Networks

Dynamic Resource Allocation and Priority Based Scheduling for Heterogeneous Services in Cognitive Radio Networks

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

Optimal Power Allocation of Relay Sensor Node Capable of Energy Harvesting in Cooperative Cognitive Radio Network

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