Multiuser Detection in MIMO DS-CDMA Systems Over Slow-Fading Channels
Bibliographic record
Abstract
Transmit diversity designed for fast-fading channels is examined in a multiuser direct-sequence code division multiple access (DS-CDMA) system over slowly-fading channels. The underlying space-time system employs two transmit antennas and M receive antennas at the user side and base-station receiver, respectively. The receiver employs a decorrelator multi-user detector. In our analysis, we derive a closed form expression for the bit error probability. These theoretical results, when compared to simulations, are shown to be very accurate. Both simulations and analytical results demonstrate that, regardless of the system load, the full diversity order of NM is always maintained and only a signal-to-noise ratio (SNR) loss is incurred when using a decorrelator detector at the receiver side. This SNR degradation is shown to be a function only of the number of users and independent of the number of transmit and/or receive antennas. Using our theoretical results, we show that the loss in SNR from the single-user bound can be well approximated by 10log(C/2) where C is a parameter of the interference level
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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".