Iterative Semiblind Multiuser Receivers for a Space–Time Block-Coded MC-CDMA Uplink System
Bibliographic record
Abstract
Iterative multiuser detection and space-time coding are two promising techniques to improve the capacity and performance of coded multiuser systems in wireless channels. In this paper, we present iterative multiuser detection schemes for a space-time block-coded multicarrier code-division multiple-access system with multiple transmit and receive antennas. We consider a more general case of an uplink system in the presence of both intra- and intercell interferences. We propose two types of iterative semiblind space-time receivers for such an uplink environment. The first is based on the minimum mean-square error criterion and the second is a hybrid scheme based on a combination of parallel interference cancellation and linear multiuser detection. These iterative receivers are derived, using a subspace approach, which utilizes known users' information for the computation of log-likelihood ratios (LLRs) while blindly suppressing the unknown interference. The LLRs are refined successively during the iterative process by using the extrinsic information available through decoding of all known users. A turbo code is used for channel coding. Simulation results in a frequency-selective Rayleigh-fading environment are presented to verify the performance of the proposed schemes.
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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 | 0.004 |
| 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 | 0.001 |
| 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".