Learning‐based predictive dynamic spectrum access framework: a practical perspective for enhanced QoE of secondary users

Agarwal, Anirudh; Gangopadhyay, R.; Dubey, Shivangi; Debnath, Soumitra; Khan, Mohd Adnan

doi:10.1049/iet-com.2018.5407

Cited by 4 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Predictive channel access has been widely explored for communications with a few examples extended to radar. Machine learning (ML) approaches such as neural networks and Support Vector Machines have been applied to DSA for communications [34], [35], [36]. Others have explored modelbased approaches in the form of binary time series analysis [37] and stochastic methods [38], [39], [40].…”

Section: B Spectral Predictionmentioning

confidence: 99%

Evaluation of Real-Time Predictive Spectrum Sharing for Cognitive Radar

Kovarskiy

Kirk

Martone

et al. 2021

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

The growing demand for radio frequency (RF) spectrum access poses new challenges for nextgeneration radar systems. To operate in a crowded electromagnetic environment, radars must coexist with other RF emitters while maintaining system performance. This work evaluates the performance of a spectrum sharing cognitive radar system which predicts and avoids RF interference in real-time. The system applies a cognitive perceptionaction cycle which senses RF interference (RFI), learns RFI behavior over time, and adapts the radar's frequency band of operation. Through cognition, the system learns a stochastic model describing RF activity. This model allows the cognitive radar to predict RF activity in real-time and share the spectrum with emitters such as communication systems. A set of synthetic and measured interference signals are used to evaluate the performance of this predictive spectrum sharing scheme. This work assesses the impact of RF interference on the cognitive radar's range profile with respect to variation in RF environment. The system demonstrates accurate avoidance of deterministic RFI with a degradation in spectrum sharing efficiency as variability over time increases.

show abstract

Section: B Spectral Predictionmentioning

confidence: 99%

Evaluation of Real-Time Predictive Spectrum Sharing for Cognitive Radar

Kovarskiy

Kirk

Martone

et al. 2021

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

show abstract

“…Activity prediction can deduce future environmental states based on observations to improve the accuracy of environmental knowledge, which has been extensively studied in the communication field [13][14][15], and a few research results have been extended to radar applications [8,9,[16][17][18][19][20][21][22]. These prediction methods can be divided into model-based and model-independent methods, and the main techniques involve Markov process-based prediction [8][9][10]16,18], stochastic process-based prediction [19,20] and machine learningbased prediction [21,22].…”

Section: Introductionmentioning

confidence: 99%

Online Sequential Extreme Learning Machine-Based Active Interference Activity Prediction for Cognitive Radar

et al. 2022

View full text Add to dashboard Cite

For anti-active-interference-oriented cognitive radar systems, the mismatch between the acquired and actual interference information may result in serious degradation of cognitive anti-active-interference performance. To yield more effective knowledge of the electromagnetic environment and eliminate the mismatch effect, the electromagnetic activity prediction technique, which deduces future electromagnetic behaviors based on current observations, has received increasing attention. However, high computational complexities limit the application of conventional electromagnetic activity prediction methods in dynamic active interference prediction with high real-time requirements. In this paper, the online sequential extreme learning machine (OS-ELM)-based method, which is dedicated to high-efficiency active interference activity prediction, is proposed. The advancement includes two aspects. First, benefiting from the single-hidden-layer network structure and recursive-formula-based output weight updating, the proposed OS-ELM-based frequency prediction (OS-ELM-FP) and OS-ELM-based angle prediction (OS-ELM-AP) models can predict the interference state and update the prediction model parameters with much higher computational efficiency. Second, the better generalization performance enables the proposed method to achieve smaller interference activity prediction errors compared with conventional methods. Numerical examples and prediction results based on measured jamming data demonstrate the advantages of the proposed method.

show abstract