Diversity combining options for spread spectrum OFDM systems in frequency selective channels
Bibliographic record
Abstract
This paper compares diversity combining schemes for the downlink of spread spectrum orthogonal frequency division multiplexing (SS-OFDM) systems in frequency selective fading channels. In particular, symbol-level combining after despreading is compared to chip-level combining under maximal ratio combining (MRC) of signals from different diversity branches and minimum mean-square error (MMSE) equalization of spreading sequences. Symbol-level combining takes place after the operations of MMSE equalization and despreading, whereas the operations of equalization and despreading occur after MRC if chip-level combining is used. Chip-level combining combines diversity samples in an efficient manner while reducing inter-code interference (self-interference) that results from the loss of orthogonality of spreading sequences due to a frequency selective channel. This method is shown to be superior to symbol-level combining when the diversity branches are uncorrelated, and when the branches differ only due to subcarrier interleaving. Novel expressions for the bit error rate (BER) of the two methods, as well as the extension of the analysis to partially loaded systems are presented. The results are relevant to antenna diversity as well as temporal diversity achieved though re-transmission within an ARQ scheme.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".