Bibliographic record
Abstract
Maximum length shift register (MLSR) sequences are utilized for joint channel coding and data spreading. Compared to uncoded spreading, more than 5 dB coding gain is obtained at BER of 10/sup -3/ in additive white Gaussian noise (AWGN) channels. The coding gain increases up to 22 dB in Rayleigh fading channels. In the proposed algorithm, information bits are segmented into blocks and coded by cyclic MLSR coding. The code word output of the systematic MLSR encoder (m-sequence) is then used for spreading the uncoded information bits in each data block. At the receiver, the autocorrelation property of the m-sequences is used for mutual despreading and decoding of the received signal. An optimum soft decision decoder is implemented by a parallel bank of correlators, which are matched to each spreading sequence. Despreading and channel decoding operations are performed concurrently. The ingenuity of the algorithm lies in the fact that all the redundant channel coding bits are carried by spreading sequences, and thus avoids energy per information bit loss due to channel coding. Utilizing the same spreading code for all the data bits in the block provides diversity, which improves the performance substantially in fading channels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".