On bandwidth-efficient multiuser-space-time signal design and detection
Bibliographic record
Abstract
Signals designed for transmission over multiple transmit antennas are capable for achieving significant capacity gain. Traditional approaches aim at improving the single-user link with a centralized control over the set of transmit antennas. In this paper, by considering a set of independent and synchronized users communicating with the base station on the up-link, the joint signal can be viewed as space-time coded signal without a centralized control. Co-channel/inter-antenna interference presents a major impairment that limits the capacity. We propose a novel multiuser signal structure called interference-resistant modulation (IRM) to improve performance without coding nor bandwidth expansion. IRM can also be combined with fading-resistant modulation or space-time coding to yield additional gain when each user employs multiple transmit antennas. We prove that, both analytically and by simulations, the IRM with maximum-likelihood (ML) detection achieves the single-user performance asymptotically. Furthermore, to reduce the prohibitive complexity posed by ML detection, we propose a simple minimum-mean-square-error based precombining group detector and an interference cancellation scheme. It is shown that the proposed detector combined with IRM provides significant improvement over previous approaches.
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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 | 0.000 | 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) | 0.001 | 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".