A low complexity turbo detection for coded DS-CDMA systems in multipath channels at rake computational load
Bibliographic record
Abstract
An efficient, low complexity, at close to Rake computational load, turbo detection receiver for joint detection and decoding for coded DS-CDMA signals in multipath channels is derived. The new scheme is based on an asymptotic approximation of the maximum likelihood (ML) detector as we believe that for a number of iterations more than three, the summation over 2/sup vK-1/ possible sequences (where K is the number of users and v is the channel length) will drop to a single factor that can be effectively represented by the expected values of the multiple access interference (MAI) and intersymbol interference (ISI) using a priori information from the decoder. Following the SISO detector is a bank of a full codeword MAP decoder (SISO channel decoder). Both stages are separated by interleaves and deinterleavers so that they iteratively exchange soft information of "likelihood" nature. The detection stage can be viewed as SISO detector that estimates soft MAI and ISI contribution in the received signal and determine after words the likelihood of the code bit of interest of a given user and feeds it to the appropriate decoder which in turn delivers an update of these likelihoods and so on in an iterative manner. Simulation results for performance evaluation are conducted under most interesting scenarios including asynchronous multipath channels, near far problem, time varying channels, channel estimation miss match and especially multirate systems.
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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 | 0.001 | 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".