Research on Spectrum Prediction Technology Based on B-LTF

Jia, Min; Chen, Qian; Yu, Xiaoyang

doi:10.3390/electronics12010247

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…To solve the above problems, Ling Yu et al [ 19 ] introduced the Taguchi method and LSTM for time domain spectrum prediction, effectively reducing the time and computational power requirements. Xue Wang et al [ 20 ] used the Back Propagation-LSTM (BP-LSTM) network model for spectrum prediction, which has better prediction performance than BP, LSTM, and GRU. Nevertheless, the spectrum states are interrelated in the time and frequency domains.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

Wang,

Hu,

Jiang

et al. 2024

Sensors

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show abstract

“…The realization of path planning is a prerequisite for the application of autonomous navigation functions. Therefore, scholars worldwide have realized the application in different environments from the perspectives of communications technology, satellite navigation technology, image processing, visual recognition, SLAM (Simultaneous Localization and Mapping) technology, geo-fencing, and obstacle-avoidance processing [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Mecanum Wheel Chassis Based on Improved A* Algorithm

Wang

Zhao

et al. 2023

Electronics

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This study is concerned with path planning in a structured greenhouse, in contrast to much of the previous research addressing applications in outdoor fields. The prototype mainly comprises an independently driven Mecanum wheel, a lidar measuring module, a single-chip microcomputer control board, and a laptop computer. Environmental information collection and mapping were completed on the basis of lidar and laptop computer connection. The path planning algorithm used in this paper expanded the 8-search-neighborhood of the traditional A* algorithm to a 48-search-neighborhood, increasing the search direction and improving the efficiency of path planning. The Floyd algorithm was integrated to smooth the planned path and reduced the turning points in the path. In this way, the problems of the traditional A* algorithm could be solved (i.e., slow the path planning speed and high numbers of redundant points). Tests showed that the turning points, planning path time, and distance of the improved algorithm were the lowest. Compared with the traditional 8-search-neighborhood A* algorithm, the turning point was reduced by 50%, the planning time was reduced by 13.53%, and the planning distance was reduced by 13.96%. Therefore, the improved method of the A* algorithm proposed in this paper improves the precision of the planning path and reduces the planning time, providing a theoretical basis for the navigation, inspection, and standardization construction of greenhouses in the future.

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Communication spectrum prediction method based on convolutional gated recurrent unit network

Yuan,

Nie,

Hao

2024

Sci Rep

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In modern wireless communication systems, the scarcity of spectrum resources poses challenges to the performance and efficiency of the system. Spectrum prediction technology can help systems better plan and schedule resources to respond to the dynamic changes in spectrum. Dynamic change in the spectrum refers to the changes in the radio spectrum in a wireless communication system. It means that the available spectrum resources may change at different times and locations. In response to this current situation, this study first constructs a communication collaborative spectrum sensing model using channel aliasing dense connection networks. Then, combining convolutional neural network and gated cyclic unit network in deep learning technology, a communication spectrum prediction model is built. It aims to achieve accurate perception and prediction of spectrum resources through the aforementioned spectrum sensing and prediction models. The results confirm that the proposed perception model has inconsistent perception accuracy under different number of secondary users, with a maximum of 0.99. It is verified that the proposed spectrum prediction model achieves a high prediction accuracy of 0.95 within 208 s and its performance outperforms current similar models. The results are based on the model's deep learning analysis of massive historical communication data, in which the optimized shuffle dense net model plus convolutional gated recurrent unit model is the key to achieve fast and accurate prediction. On the contrary, the highest spectrum prediction accuracy of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks-Long Short-Term Memory (ConvLSTM) models are 0.86, 0.90, and 0.85, respectively. And the model needs to run for a longer period of time, up to 324, for ConvLSTM to reach the prediction accuracy value of 0.95. In summary, the perception and prediction model built by this research has good performance, and its application in the field of wireless communication can assist staff in better monitoring spectral changes, thereby making more efficient use of spectral resources.

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Research on Spectrum Prediction Technology Based on B-LTF

Cited by 6 publications

References 23 publications

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

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

Path Planning of Mecanum Wheel Chassis Based on Improved A* Algorithm

Communication spectrum prediction method based on convolutional gated recurrent unit network

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