Primary user characterization for cognitive radio wireless networks using a neural system based on Deep Learning

López, Danilo; Rivas, Edwin; Gualdrón, Óscar Eduardo

doi:10.1007/s10462-017-9600-4

Cited by 20 publications

(27 citation statements)

References 30 publications

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“…Equation 5indicates that as the number of input layer neurons increases, the total number of neurons is approximately twice the number of input layer neurons. 34 LSTM system operating model LSTM can be considered a differentiable function approximator that is usually trained with a descending gradient. 35 Although a truncated form of backpropagation through time (BPTT) was initially employed to approximate the error gradient, 36 a BPTT calculation without truncation, based on the discussion by Graves and Schmidhuber, 37 was used in this research.…”

Section: Lstmmentioning

confidence: 99%

“…The second one is the s t c neuron status, which indicates if the neuron is keeping the information or will forget it, and depends on the outputs from the forget gate and the input gate. 34 The neuron output b t c will indicate if new learning was generated or the stored information is kept. Now that the above is clear and based on Figure 3, it is concluded that cell status and output are given by equations (12)- (14).…”

Section: Lstmmentioning

confidence: 99%

“…Based on the mathematical analysis applied above, the following backward pass equations are obtained 34,35 Output gate…”

Section: Lstmmentioning

confidence: 99%

“…Table 4. 34 For qualitative purposes, results are presented for the LSTM algorithm when modeling and estimating the future behavior of a licensed user (for TC2), with a high fluctuation between presence and absence in the licensed channel. 35 The binary sequence that simulates channel use is made up of 50 digits.…”

Section: Evaluation and Validation Of The Proposed Lstm Algorithmmentioning

confidence: 99%

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Primary user characterization for cognitive radio wireless networks using long short-term memory

Hernández

López

Parra

2018

International Journal of Distributed Sensor Networks

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Cognitive radio is a paradigm that proposes managing the radio electric spectrum dynamically by integrating the spectrum sensing, decision-making, sharing, and mobility stages. In the decision-making stage, the best available channel is selected for transmitting secondary user data in an opportunistic fashion, and the success of that stage depends on the efficiency of the primary user characterization model. Use of the long short-term memory technique based on the deep learning concept is proposed in order to reduce the forecasting error present in the future estimation of primary users in the GSM and WiFi frequency bands. The results show that long short-term memory has the capacity needed to improve channel use forecasting significantly more than other methods such as multilayer perceptron neural networks, Bayesian networks, and adaptive neuro-fuzzy inference systems (ANFIS-Grid). It is concluded that although long shortterm memory exhibits better performance generating forecasts for time series, computing complexity is higher due to the existence of input, forget, and output gates within the neural structure; therefore, implementation is feasible in cognitive radio networks based on centralized network topologies.

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Section: Lstmmentioning

confidence: 99%

Section: Lstmmentioning

confidence: 99%

“…Based on the mathematical analysis applied above, the following backward pass equations are obtained 34,35 Output gate…”

Section: Lstmmentioning

confidence: 99%

Section: Evaluation and Validation Of The Proposed Lstm Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Primary user characterization for cognitive radio wireless networks using long short-term memory

Hernández

López

Parra

2018

International Journal of Distributed Sensor Networks

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“…Although it is not discriminated in figure 6, the traces of data or database (of historical use behavior) that enters the prediction system come from a spectral database, which underwent a digital treatment for its conversion to discrete sequences as described in [28].…”

Section: Figure 6 Descriptive Diagram Of the Rnnmentioning

confidence: 99%

Modeling and prediction primary nodes in wireless networks of cognitive radio using recurrent neural networks

Viveros¹,

Sarmiento²,

Parra³

2018

ces

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The cognitive radio is a methodology that proposes the management of the radio spectrum dynamically, by integrating the stages of sensing, decision making, sharing and spectral mobility. The spectral decision-making phase is in charge of deciding which is the best available channel to transmit the data of Secondary Users (SUs) in an opportunistic manner, and its success depends on how efficient is the Primary User characterization model (PUs). The use of Recurrent Neural Networks (RNNs) is proposed as a model to reduce the prediction error that is presented in the future estimation of channels in the frequency band of 2.4 GHz. The findings found that the RNNs have the necessary self-management to improve the forecast channels' use by PUs in the WiFi spectral band and with better levels of success than those delivered by the Multilayer Perceptron Neural Networks (MLPNN). 2514Leydy Johana Hernández Viveros et al.

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Long Boosted Memory Algorithm for Intelligent Spectrum Sensing in 5G and Beyond Systems

2022

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Primary user characterization for cognitive radio wireless networks using a neural system based on Deep Learning

Cited by 20 publications

References 30 publications

Primary user characterization for cognitive radio wireless networks using long short-term memory

Primary user characterization for cognitive radio wireless networks using long short-term memory

Modeling and prediction primary nodes in wireless networks of cognitive radio using recurrent neural networks

Long Boosted Memory Algorithm for Intelligent Spectrum Sensing in 5G and Beyond Systems

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