Spectrum Allocation for Multiuser Terahertz Communication Systems: A Machine Learning Approach
Bibliographic record
Abstract
In this paper, we propose a novel spectrum allocation design, leveraging machine learning, for multiuser communication systems operating at the terahertz (THz) band. In this design, we propose to (i) change the bandwidth of sub-bands and (ii) underutilize edge spectra of transmission windows (TWs) where the molecular absorption (MA) coefficient is very high. Different from existing studies, our design is not limited to the scenario where the MA coefficient in the spectrum designated for allocation can be accurately modeled by simply using a piecewise exponential function. We establish a constrained optimization problem and introduce an unsupervised learning approach for its solution. Through offline training, we learn a deep neural network (DNN) using a loss function inspired by the Lagrangian of the established problem. The trained DNN is then employed to derive solutions when multiuser distance parameters are given. Based on numerical analysis, we show that when the MA coefficient in the spectrum designated for allocation exhibits highly non-linear variations, our proposed approach can achieve a higher data rate than that of existing approaches which only attain approximate solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".