Cooperative Spectrum Sensing Based on LSTM-CNN Combination Network in Cognitive Radio System

Li, Liuwen; Xie, Wei; Zhou, Xuebing

doi:10.1109/access.2023.3305483

Cited by 16 publications

(1 citation statement)

References 38 publications

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“…Baselines and Evaluation Metrics: For comparison, 10 prediction models based on deep learning are considered as baselines, which are LSTM [ 18 ], GRU [ 32 ], BiLSTM [ 33 ], CNN [ 34 ], CNN-LSTM [ 35 ], CNN-BiLSTM [ 25 ], CNN-BiLSTM-attention [ 36 ], seq2seq-LSTM [ 37 ], seq2seq-LSTM-attention [ 38 ], and seq2seq-Bi-ConvLSTM [ 39 ]. In order to fairly compare the performance of models, the input information and output information of all prediction models are all the same and the baseline models use the multi-step principle to output results.…”

Section: Methodsmentioning

confidence: 99%

A Deep Long-Term Joint Temporal–Spectral Network for Spectrum Prediction

Wang,

Hu,

Jiang

et al. 2024

Sensors

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Spectrum prediction is a promising technique to release spectrum resources and plays an essential role in cognitive radio networks and spectrum situation generating. Traditional algorithms normally focus on one-dimensional or predict spectrum values in a slot-by-slot manner and thus cannot fully perceive the spectrum states in complex environments and lack timeliness. In this paper, a deep learning-based prediction method with a simple structure is developed for temporal–spectral and multi-slot spectrum prediction simultaneously. Specifically, we first analyze and construct spectrum data suitable for the model to simultaneously achieve long-term and multi-dimensional spectrum prediction. Then, a hierarchical spectrum prediction system is developed that takes advantage of the advanced Bi-ConvLSTM and the seq2seq framework. The Bi-ConvLSTM captures time–frequency characteristics of spectrum data, and the seq2seq framework is used for long-term spectrum prediction. Furthermore, the attention mechanism is used to address the limitations of the seq2seq framework that compresses all inputs into fixed-length vectors, resulting in information loss. Finally, the experimental results have shown that the proposed model has a significant advantage over the benchmark schemes. Especially, the proposed spectrum prediction model achieves 6.15%, 0.7749, 1.0978, and 0.9628 in MAPE, MAE, RMSE, and R2, respectively, which is better than all the baseline deep learning models.

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