Performance of convolutional and RS codes in DS-CDMA systems using space-time MMSE multiuser detection
Bibliographic record
Abstract
In this paper we investigate the performance of a minimum mean square error (MMSE) multi-user detector (MUD) using antenna array processing and forward error correction channel codes (FEC) in a synchronous direct-sequence code-division multiple access (DS-CDMA) system over both additive white Gaussian noise (AWGN) and flat Rayleigh fading channels. Two different channel codes are considered in a binary phase shift-keyed (BPSK) modulation system; Reed-Solomon (RS) and convolutional codes. Through computer simulations, a comparison between the two codes shows that convolutional codes and soft decision decoding can offer at least 2 dB coding gain over the hard decision RS decoding in an AWGN channel and approximately a 5 dB gain in a flat fading channel at a BER of 10/sup -4/. In the paper, such results are compared with these in the text (Wicker 1995) for the single user case and AWGN 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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".