Experimental Results on the Impact of Memory in Neural Networks for Spectrum Prediction in Land Mobile Radio Bands
Bibliographic record
Abstract
Aim: We set out to investigate the benefit of the “memory” of long short term memory (LSTM) networks in predicting spectrum occupancy in multiple time horizons in Land Mobile Radio (LMR) bands. Background: ANNs are a popular choice for spectrum prediction. Traditionally, ARIMA models have been at the forefront of forecasting. However, recurrent ANNs have demonstrated good prediction performance. Methodology: We train and evaluate four prediction models: a baseline which simply delays the time series, a seasonal ARIMA model, a TDNN and an LSTM. We test their performance on an hourly dataset in LMR bands collected in Ottawa, Canada between the dates of October 2016 and April 2017. Results: We find that LSTMs provide an improvement in prediction performance compared to the other models. We also compare the computational complexity of our models. Conclusions: The LSTM networks that remember long term dependencies and designed to work with time series provide an improvement accurately predicting spectrum occupancy in LMR bands.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".