Bibliographic record
Abstract
This paper introduces a novel Trellis Coded Multiple Access (TCMA) technique which takes advantage of Trellis-Coded Modulation (TCM) and signalling set redundancy to differentiate transmissions from different users. The proposed TCMA scheme exploits modulation with memory principles to provide the flexibility of accommodating an increasing number of users within the fixed time-frequency resource. In the scheme, users are assigned unique, code-based trellises employing distinct subsets of generic signals. This paper focuses on the applicability of chirp signalling to TCMA, even though the proposed system can use other types of narrowband modulations, such as FSK or QAM. Specifically, the MA scheme presented in this paper features the assignment of overlapping, dissimilar signal trellises that efficiently occupy the system bandwidth and allow for (i) user identification and (ii) reliable data recovery. Careful design of a chirp signalling scheme and the trellis arches guiding the frequency hopping patterns is conducted to reduce MAI. An effective demodulation algorithm is developed using the Viterbi decoder incorporated with a parallel, iterative MAI cancellation scheme. It is demonstrated with a theoretical analysis and simulations that the proposed scheme achieves an acceptable bit error rate performance for different system configurations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".