Artificial Neural Network Based Approach for Spectrum Sensing in Cognitive Radio

Yelalwar, Rajendra; Ravinder, Yerram

doi:10.1109/wispnet.2018.8538729

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Artificial neural network (ANN) based spectrum sensing (SS) is advanced to evaluate the database and to prepare the ANN to distinguish between signal and noise [51]. In cognitive radio, spectrum sensing has been considered as an ideal method to sense the availability of primary users in the network.…”

Section: Background Methodologymentioning

confidence: 99%

Improved Convolutional Neural Network Based Cooperative Spectrum Sensing For Cognitive Radio

2021

KSII TIIS

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Cognitive radio systems are being implemented recently to tackle spectrum underutilization problems and aid efficient data traffic. Spectrum sensing is the crucial step in cognitive applications in which cognitive user detects the presence of primary user (PU) in a particular channel thereby switching to another channel for continuous transmission. In cognitive radio systems, the capacity to precisely identify the primary user's signal is essential to secondary user so as to use idle licensed spectrum. Based on the inherent capability, a new spectrum sensing technique is proposed in this paper to identify all types of primary user signals in a cognitive radio condition. Hence, a spectrum sensing algorithm using improved convolutional neural network and long short-term memory (CNN-LSTM) is presented. The principle used in our approach is simulated annealing that discovers reasonable number of neurons for each layer of a completely associated deep neural network to tackle the streamlining issue. The probability of detection is considered as the determining parameter to find the efficiency of the proposed algorithm. Experiments are carried under different signal to noise ratio to indicate better performance of the proposed algorithm. The PU signal will have an associated modulation format and hence identifying the presence of a modulation format itself establishes the presence of PU signal.

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Section: Background Methodologymentioning

confidence: 99%

Improved Convolutional Neural Network Based Cooperative Spectrum Sensing For Cognitive Radio

2021

KSII TIIS

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“…Simulated datasets are the most commonly used in current RFML literature as they are the most straightforward to compile and label using publicly available toolsets such as GNU Radio [127], liquid-dsp [128], and MATLAB [129] among others, and therefore lends itself well to initial development [18], [26], [28], [30], [33]- [42], [44]- [56], [58], [60], [65]- [79], [83]- [88], [92], [100], [102], [118], [119], [130]- [135]. Unlike in image processing [136], the same equations and processes used to transmit waveforms in real systems can be used directly in simulation, due to their man-made nature [18].…”

Section: Dataset Creationmentioning

confidence: 99%

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

Wong,

Clark,

Flowers

et al. 2020

Preprint

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While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications. In particular, recent research has shown deep machine learning to be an enabling technology for cognitive radio applications as well as a useful tool for supplementing expertly defined algorithms for spectrum sensing applications such as signal detection, estimation, and classification (termed here as Radio Frequency Machine Learning, or RFML). A major driver for the usage of deep machine learning in the context of wireless communications is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware/software issues, that must be taken into account before deploying deep machine learning systems in real-world wireless communication applications. This paper provides an overview and survey of prior work related to these major research considerations. In particular, we present their unique considerations in the RFML application space, which are not generally present in the image, audio, and/or text application spaces.

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“…Ravinder reported a printed synaptic transistor to create E-skin with positive mind and opened up novel reformation: inserting Neuro layer into E-skin, possessing own Neuro and can immediately respond to external stimuli in absence of instruction. This project would invoke the reformation of wearable technology, exible electronic and new generation computer 26 . Furthermore, graphene was selected as channel semiconductor for high mobility (chemical vapour deposited (CVD) graphene with mobility approaching 200 000 cm 2 V − 1 s − 1 for the carrier density below 5 × 10 9 cm − 2 at a low temperature 27 ), high transparency (the white light absorbance of a suspended graphene monolayer is 2.3% (or transmittance of 97.7%) with a negligible re ectance of < 0.1% 28), excellent thermal stability and conductivity (3000 5000 W m − 1 K − 1 ) 29 , high Young's modulus ( 1 TPa) 30 and large theoretical speci c surface area (SSA, 2630 m 2 •g − 1 ) 31 .…”

Section: Introductionmentioning

confidence: 99%

Self-powered Bifunctional Sensor Based on Tribotronic Planar Graphene Transistors

Meng

Gao

Zhu

2021

Preprint

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With the development of material science, micro-nano-fabrication, microelectronics, the higher level requirements are posed on the electronic skins (E-skin). The lower energy consumption and multiple functions are the imperative requirements to spurred scientists and mechanists to make joint effort to meet. To achieve lower energy consumption, a promising energy-harvesting style of triboelectric nanogenerators (TENG) is incorporated into the field effect transistors (FETs) to play the important role for sensor. For bifunctional sensor, to harness the difficult for reflecting the magnitude of frequency, we resorted to synaptic transistors to achieve more intelligentization. Furthermore, with regards to the configuration of FET, we continued previous work: using the electrolyte gate dielectrics of FET - ion gel as the electrification layer to achieve high efficient, compact and extensively adoption for mechanosensation. The working principle of the GFET was based on the coupling effects of the FET and the TENG. This novel self-powered sensor would offer a new platform for lower power consumption sensor for human-machine interface and intelligent robot.

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Artificial Neural Network Based Approach for Spectrum Sensing in Cognitive Radio

Cited by 5 publications

References 9 publications

Improved Convolutional Neural Network Based Cooperative Spectrum Sensing For Cognitive Radio

Improved Convolutional Neural Network Based Cooperative Spectrum Sensing For Cognitive Radio

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

Self-powered Bifunctional Sensor Based on Tribotronic Planar Graphene Transistors

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