Optimal coding rate of punctured convolutional codes in indoor wireless TDMA cellular systems
Bibliographic record
Abstract
The microcellular link performance of future multimedia indoor wireless systems could be improved by using error-correcting punctured convolutional codes in conjunction with slow frequency hopping. However, the bandwidth expansion due to coding leads to a decrease in the signal-to-interference ratio of a FD-TDMA cellular radio link if system capacity is to be maintained for a given bandwidth allocation. This work determines the best compromise between the power of error correction due to coding and the strength of the self-induced system interference, in terms of numerous criteria for speech and data transmission. The aforementioned trade-off is evaluated in terms of the average bit error rate and the burst error distribution for voice transmission. For data transmission with a selective-repeat ARQ protocol, the criteria are throughput, round-trip acknowledgement transmission delay and buffering requirements at the transmitter and receiver. The study clearly highlights that punctured codes can significantly improve performance for indoor wireless data links in comparison with the rate 1/2 convolutional coding case or the no-coding case.>
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".