A New Multi-Dimensional Hybrid Deep Neural Network Based Spectrum Inference for Cognitive Radio Network
Bibliographic record
Abstract
In wireless communications, cognitive radio (CR) technology has significantly enhanced radio spectrum efficiency. Spectrum sensing is a key process in CR along with other major functions namely spectrum decision, sharing and mobility. Minimizing the processing delays, energy consumption of these functions and enhancing spectrum utilization is a major challenge. Spectrum inference has been proposed as a viable solution to overcome these problems. Many machine learning-based spectrum inference techniques using artificial neural networks (ANNs) and deep neural networks have been proposed in literature. In this paper we aimed to determine whether hybrid deep neural network based spectrum inference model outperform single model in time and frequency domains for spectrum occupancy dataset. Radial basis function (RBF) neural network tend to excel in extracting spatial features of spectrum data whereas bidirectional long short-term memory (BiLSTM) work very well for temporal dependencies of this data. Spectrum dataset exhibit both short-and-long term temporal/spectral dependencies. In this paper we have proposed spectrum inference based on a hybrid deep neural network RBF and BiLSTM. The proposed algorithm has been simulated using real time spectrum measurement data with time dimension ranging from (1 to 7 days), spectral range (0.7 GHz to 2.7 GHz) across three geographically varying locations Pune, Solapur and Kalaburagi in India. Hybrid deep neural network integration of RBF and BiLSTM is built, tested and compared with single models LSTM, BiLSTM for accuracy and speed. The hybrid method has outperformed single models to achieve Precision, Recall, F1 scores of 0.9959, 0.9575, 0.9763 respectively and training time improvement of 57.60% for GSM and whole band in frequency and time dimensions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".