Two‐stage detection of partitioned random CDMA
Bibliographic record
Abstract
Abstract Random Code Division Multiple Access (CDMA) with low complexity two‐stage joint detection/decoding is considered. A sequence partitioning approach is used for modulation, where every spreading sequence is divided into M sections (partitions) which are interleaved prior to transmission. This setup, called partitioned CDMA, can be understood as a generalisation of (chip) interleave division multiple access (IDMA). An analysis of a low‐complexity iterative cancellation receiver is presented for arbitrary received power distributions. It is shown that for equal rate and equal power users the asymptotic performance of partitioned CDMA is equal to the performance of CDMA with optimal a posteriori probability (APP) detection for system loads K/N < 1.49. Effects of asynchronous signal transmission are quantified for standard pulse shaping filters and it is shown that the signal‐to‐noise ratios achievable in an asynchronous system are improved with respect to fully synchronous transmission. The effect of unequal received powers is examined and considerable gains in performance are obtained by judicious choices of power distributions. For certain power distribution, partitioned CDMA with iterative detection can achieve arbitrary system loads, that is detection is no longer fundamentally interference limited. The practical near‐far resistance of the proposed system is illustrated using an example of a receiver with a circular receive footprint and uniformly distributed transmitters (single cell system). Copyright © 2008 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".