Guangliang et al., 2025 - Google Patents
Multi-channel multi-step spectrum prediction using transformer and stacked Bi-LSTMGuangliang et al., 2025
View PDF- Document ID
- 9784316052904005306
- Author
- Guangliang P
- Jie L
- Minglei L
- Publication year
- Publication venue
- China Communications
External Links
Snippet
Spectrum prediction is considered as a key technology to assist spectrum decision. Despite the great efforts that have been put on the construction of spectrum prediction, achieving accurate spectrum prediction emphasizes the need for more advanced solutions. In this …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhou et al. | Deep-learning-based spatial–temporal channel prediction for smart high-speed railway communication networks | |
| Zhang et al. | An indoor positioning method based on CSI by using features optimization mechanism with LSTM | |
| Pan et al. | Deep stacked autoencoder-based long-term spectrum prediction using real-world data | |
| Guangliang et al. | Multi-channel multi-step spectrum prediction using transformer and stacked Bi-LSTM | |
| Cui et al. | Collision prediction for a low power wide area network using deep learning methods | |
| Guo et al. | SemiAMR: Semi-supervised automatic modulation recognition with corrected pseudo-label and consistency regularization | |
| Pandya | Predictive Analytics in Smart Grids: Leveraging Machine Learning for Renewable Energy Sources | |
| Gao et al. | Joint multi-channel multi-step spectrum prediction algorithm | |
| CN113570032A (en) | Spectrum sensing method for limited data based on semi-supervised deep neural network | |
| Wu et al. | Received power prediction for suburban environment based on neural network | |
| CN115802401A (en) | Wireless network channel state prediction method, device, equipment and storage medium | |
| Chen et al. | ACT‐GAN: Radio map construction based on generative adversarial networks with ACT blocks | |
| Wang et al. | Deep learning models for spectrum prediction: A review | |
| Zhang et al. | Accurate spectrum prediction based on joint LSTM with CNN toward spectrum sharing | |
| Benelmir et al. | A novel mmwave beam alignment approach for beyond 5g autonomous vehicle networks | |
| Radhakrishnan et al. | Performance analysis of long short-term memory-based Markovian spectrum prediction | |
| Zhang et al. | MASSnet: deep learning-based multiple-antenna spectrum sensing for cognitive radio-enabled internet of things | |
| CN117560046A (en) | Beam tracking method and device, equipment and storage medium | |
| Shubo et al. | Network traffic prediction based on the multi-time granularity GRU-BP neural network | |
| Xue et al. | Deep learning based channel prediction for massive MIMO systems in high-speed railway scenarios | |
| Zhao et al. | Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning | |
| Pan et al. | Spectrum prediction with deep 3D pyramid vision transformer learning | |
| Xu et al. | Spectrum prediction for mobile Internet of Things based on a DB-LSTM algorithm | |
| CN120452054A (en) | A skeleton sign language recognition method based on a two-stream spatiotemporal dynamic graph convolutional network integrated with residual learning | |
| Wu et al. | Long-short term memory networks aided fault detection of power facilities |