Application of neural network for sensing primary radio signals in a cognitive radio environment

Popoola, Jide Julius; Olst, Rex van

doi:10.1109/afrcon.2011.6072009

Cited by 30 publications

(24 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To enhance ANN classification accuracy, ANN is usually combined with the extracted features from the received signal, which allows the engine to have the capability to identify the modulation scheme at low SNR levels. Cyclic spectral analysis [17], wavelet cyclic features [21], temporal feature-based modulation [22,23], carrier frequency and baud rate [24], and continuous wavelet transform (CWT) [25] are some examples of these features. Dahap et al [26] proposed a digital recognition algorithm that uses six features extracted from instantaneous information and signal spectrum to discriminate between different modulated signals.…”

Section: Related Workmentioning

confidence: 99%

Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network

Alzaq

Ustundag

2017

J Wireless Com Network

View full text Add to dashboard Cite

A pattern-based cognitive communication system (PBCCS) that optimizes non-periodic RF waveforms for security applications is proposed. PBCCS is a cross-layer approach that merges the channel encoding and modulation. The transmitter encodes sequences of bits into continuous signal patterns by selecting the proper symbol glossaries. The cognitive receiver preprocesses the received signal by extracting a limited set of wavelet features. The extracted features are fed into an artificial neural network (ANN) to recover the digital data carried by the distorted symbol. The PBCCS system offers a flexible management for robustness against a high noise level and increases the spectral efficiency. In this study, the spectral efficiency and robustness of a PBCCS scheme for an additive white Gaussian noise (AWGN) channel is investigated. The results show that at an SNR of −5 dB, a 3-bit glossary achieves a bit error rate (BER) of 10 −5 . Also, the link spectral efficiency (LSE) of the proposed system is 2.61 bps/Hz.

show abstract

Section: Related Workmentioning

confidence: 99%

Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network

Alzaq

Ustundag

2017

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…The authors in [37] presented a general review of the main spectrum-sensing methods and then proposed their own automatic modulation classification detection method. The main idea of [37] is based on the fact that the secondary user is not supposed to have a priori information about the primary user's signal type and not supposed to address the issue of the hidden node.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…The main idea of [37] is based on the fact that the secondary user is not supposed to have a priori information about the primary user's signal type and not supposed to address the issue of the hidden node. The authors developed the digital classifier using an ANN that allows the user to detect all forms of primary radio signals whether weak, strong, pre-known, or unknown.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…For instance, game theory is used in decentralized multi-agent systems while reinforcement learning can be used in centralized and single-agent systems. Neural networks and SVM can be also used for some applications in decision-making, for instance, in [37], neural networks are used to adjust the parameters of the system so as to effectively adapt to the environment as it changes, and in [52], the SVM technique builds a model for determining the modulation scheme using the following inputs: bit error rate, SNR, and data rate. We remind the reader that the learning techniques and their applications in CR are summarized in Table 1.…”

Section: Matching Learning and Cr Tasksmentioning

confidence: 99%

See 1 more Smart Citation

Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Abbas

Nasser

Ahmad

2015

J Wireless Com Network

122

View full text Add to dashboard Cite

Cognitive radios are expected to play a major role towards meeting the exploding traffic demand over wireless systems. A cognitive radio node senses the environment, analyzes the outdoor parameters, and then makes decisions for dynamic time-frequency-space resource allocation and management to improve the utilization of the radio spectrum. For efficient real-time process, the cognitive radio is usually combined with artificial intelligence and machine-learning techniques so that an adaptive and intelligent allocation is achieved. This paper firstly presents the cognitive radio networks, resources, objectives, constraints, and challenges. Then, it introduces artificial intelligence and machine-learning techniques and emphasizes the role of learning in cognitive radios. Then, a survey on the state-of-the-art of machine-learning techniques in cognitive radios is presented. The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. This paper also discusses the cognitive radio implementation and the learning challenges foreseen in cognitive radio applications. IntroductionAccording to Cisco Visual Networking Index, the global IP traffic will reach 168 exabytes per month by 2019 [1], and the number of devices will be three times the global population. In addition, the resources in terms of power and bandwidth are scarce. Therefore, novel solutions are needed to minimize energy consumption and optimize resource allocation. Cognitive radio (CR) was introduced by Joseph Mitola III and Gerald Q. Maguire in 1999 for a flexible spectrum access [2]. Basically, they defined cognitive radio as the integration of model-based reasoning with software radio technologies [3]. In 2005, Simon Haykin had given a review of the cognitive radio concept and had treated it as brain-empowered wireless communications [4]. Cognitive radio is a radio or system that senses the environment, analyzes its transmission parameters, *Correspondence: nfa23@aub.edu.lb Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon and then makes decisions for dynamic time-frequencyspace resource allocation and management to improve the utilization of the radio electromagnetic spectrum.Generally, radio resource management aims at optimizing the utilization of various radio resources such that the performance of the radio system is improved. For instance, the authors in [5] proposed an optimal resource (power and bandwidth) allocation in cognitive radio networks (CRNs), specifically in the scenario of spectrum underlay, while taking into consideration the limitations of interference temperature limits. The optimization formulations provide optimal solutions for resources allocation at, sometimes, the detriment of global convergence, computation time, and...

show abstract

“…Other features exploited in AMC are the cumulants of 2nd, 3rd, 4th, 6th and 8th order [7][8][9][10][11], which have distinctive theoretical values among different modulation schemes and even though they demand a great amount of samples, they are easy to calculate. Using more sophisticated techniques, like the FFT algorithm, we can obtain the maximum value of the spectral power density of the normalized instantaneous amplitude, the standard deviation of the absolute value of the normalized centered instantaneous amplitude or even statistical metrics of the normalized Power Spectrum Density (PSD) of the received signal [8], [10], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Classification for adaptive Power Control in cognitive satellite communications

Tsakmalis

Chatzinotas

Ottersten

2014

2014 7th Advanced Satellite Multimedia Systems Conference and the 13th Signal Processing for Space Communications Workshop (ASM

View full text Add to dashboard Cite

Application of neural network for sensing primary radio signals in a cognitive radio environment

Cited by 30 publications

References 18 publications

Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network

Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network

Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Automatic Modulation Classification for adaptive Power Control in cognitive satellite communications

Contact Info

Product

Resources

About