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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Spectrum Occupancy Prediction via Bidirectional Long Short-Term Memory Network
verfasst von : Lijie Feng, Xiaojin Ding, Gengxin Zhang
Erschienen in: Communications, Signal Processing, and Systems
Verlag: Springer Singapore
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Abstract
In a satellite system, the ability to generate future spectrum occupancy can play an important role in increasing spectrum efficiency, and spectrum prediction is emerging as an efficient approach for increasing spectrum efficiency. In order to predict spectrum occupancy more accurately, we propose a bidirectional long short-term memory network (BiLSTM)-based spectrum prediction (SP) scheme, which can be performed in two stages. Specifically, in the first stage, the historical spectrum data may be pre-processed, and in the second stage, the pre-processed data should be sent to BiLSTM model, which will perform training and generate the optimized hyperparameters firstly. Then, BiLSTM will be activated to perform prediction via the optimized hyperparameters. Performance evaluations show that the BiLSTM-based SP scheme outperforms the LSTM-oriented SP scheme in terms of both accuracy and learning speed.