Prediction of Spatial Spectrum in Cognitive Radio using Cellular Simultaneous Recurrent Networks

Glandon, Alexander; Ullah, Sharif; Vidyaratne, Lasitha; Alam, Mahbubul; Xin, Chunsheng; Iftekharuddin, Khan M.

doi:10.1109/ijcnn.2018.8489412

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Since LSTMs and GRUs can learn the long-term dependencies seen in RF signals, they are the most commonly used types of RNNs in RFML. They have shown incredible promise for their usage in applications such as RF fingerprinting [9], spectrum prediction with Cognitive Radios [10,11], and modulation classification [12][13][14], among others.…”

Section: Introductionmentioning

confidence: 99%

“…While RNNs have seen use in applications such as RF fingerprinting [9], spectrum prediction [10,11], and signal classification [12][13][14], the scope of this paper's analysis is an RNN-based Automatic Modulation Classifier (AMC). Although much work has been done on AMCs, it is still an important topic in spectrum sensing with recent applications in MIMO systems [15,16], while modulation classification will be used for the initial proof of concept, the results are expected to generalize to other areas, and a similar analysis for signal detection can be found in Appendix 2 of [17].…”

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confidence: 99%

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Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications

Moore

Buehrer

Headley

2022

Sensors

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Recurrent neural networks have been shown to outperform other architectures when processing temporally correlated data, such as from wireless communication signals. However, compared to other architectures, such as convolutional neural networks, recurrent neural networks can suffer from drastically longer training and evaluation times due to their inherent sample-by-sample data processing, while traditional usage of both of these architectures assumes a fixed observation interval during both training and testing, the sample-by-sample processing capabilities of recurrent neural networks opens the door for alternative approaches. Rather than assuming that the testing and observation intervals are equivalent, the observation intervals can be “decoupled” or set independently. This can potentially reduce training times and will allow for trained networks to be adapted to different applications without retraining. This work illustrates the benefits and considerations needed when “decoupling” these observation intervals for spectrum sensing applications, using modulation classification as the example use case. The sample-by-sample processing of RNNs also allows for the relaxation of the typical requirement of a fixed time duration of the signals of interest. Allowing for variable observation intervals is important in real-time applications like cognitive radio where decisions need to be made as quickly and accurately as possible as well as in applications like electronic warfare in which the sequence length of the signal of interest may be unknown. This work examines a real-time post-processing method called “just enough” decision making that allows for variable observation intervals. In particular, this work shows that, intuitively, this method can be leveraged to process less data (i.e., shorter observation intervals) for simpler inputs (less complicated signal types or channel conditions). Less intuitively, this works shows that the “decoupling” is dependent on appropriate training to avoid bias and ensure generalization.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications

Moore

Buehrer

Headley

2022

Sensors

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“…Given this, RNNs have often been used in machine translation and natural language processing applications. Recently, RNNs have shown incredible promise for their usage in Radio Frequency Machine Learning (RFML) applications such as RF fingerprinting [2], spectrum prediction with Cognitive Radios [3], [4], and modulation classification [5]- [7], among others.…”

Section: Introductionmentioning

confidence: 99%

When is Enough Enough? "Just Enough" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning

Moore,

Clark,

Buehrer

et al. 2020

Preprint

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Prior work has demonstrated that recurrent neural network architectures show promising improvements over other machine learning architectures when processing temporally correlated inputs, such as wireless communication signals. Additionally, recurrent neural networks typically process data on a sequential basis, enabling the potential for near real-time results. In this work, we investigate the novel usage of "just enough" decision making metrics for making decisions during inference based on a variable number of input symbols. Since some signals are more complex than others, due to channel conditions, transmitter/receiver effects, etc., being able to dynamically utilize just enough of the received symbols to make a reliable decision allows for more efficient decision making in applications such as electronic warfare and dynamic spectrum sharing. To demonstrate the validity of this concept, four approaches to making "just enough" decisions are considered in this work and each are analyzed for their applicability to wireless communication machine learning applications.

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Spatio-temporal Spectrum Load Prediction using Convolutional Neural Network and Bayesian Estimation

Ren

Mosavat-Jahromi

Cai

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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Prediction of Spatial Spectrum in Cognitive Radio using Cellular Simultaneous Recurrent Networks

Cited by 5 publications

References 19 publications

Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications

Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications

When is Enough Enough? "Just Enough" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning

Spatio-temporal Spectrum Load Prediction using Convolutional Neural Network and Bayesian Estimation

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