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frequency-Hopping frequency Reconnaissance and prediction for Non-cooperative Communication Network

China Communications 2021年第12期18卷 51-64页

作者： Gao Li Wei Wang Guoru Ding Qihui Wu Zitong Liu Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China College of Communications Engineering Army Engineering UniversityNanjing 210007China

The continuous change of communica-tion frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication net-works.Since the frequency-hopping(FH)sequence is usually generated by a certain model with certain regularity,the FH frequency is thus predictable.In this paper,we investigate the FH frequency reconnais-sance and prediction of a non-cooperative communi-cation network by effective FH signal detection,time-frequency(TF)analysis,wavelet detection and fre-quency estimation.With the intercepted massive FH signal data,long short-term memory(LSTM)neural network model is constructed for FH frequency pre-diction.Simulation results show that our parameter es-timation methods could estimate frequency accurately in the presence of certain noise.Moreover,the LSTM-based scheme can effectively predict FH frequency and frequency interval.

关键词： non-cooperative communication network frequency-hopping frequency prediction LSTM

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Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

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Frontiers in Energy 2013年第4期7卷 468-478页

作者： Simarjit KAUR Yajvender Pal VERMA Sunil AGRAWAL Department of Electrical & Electronics Engineering UIET Panjab University Chandigarh 160014 India

In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling （GS）. In this paper, the GS problem is solved to perform the unit commitment （UC） based on frequency prediction by using artificial neural network （ANN） with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff （ABT） signifies the importance of frequency in GS. Under- prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error （APE） and the mean absolute percentage error （MAPE）. The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.

关键词： artificial neural network （ANN） frequency prediction availability-based tariff （ABT） generation scheduling （GS）

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