An Efficient Low-Complexity Detector for Spatially Multiplexed MC-CDM
Bibliographic record
Abstract
We propose a novel suboptimum detection method for spatially multiplexed multicarrier code division multiplexing (SM-MC-CDM) communications. Compared to the spatially multiplexed OFDM (SM-OFDM), the frequency domain spreading in SM-MC-CDM systems results in an additional diversity gain. To take advantage of diversity and multiplexing while mitigating interference, we design a low complexity efficient detector called unified successive interference cancellation (SIC) detector for SM-MC-CDM communications. Further performance improvement is achieved by adopting in conjunction with the unified SIC the iterative subcarrier reconstruction-detection algorithm originally proposed for single antenna systems. The results demonstrate significant performance improvement over other existing methods of comparable complexity. A close approximation for the probability density function (pdf) of the proposed detector's output signal-to-interference plus noise ratio (SINR) is found and used to obtain error bounds for the bit error rate (BER). Performance of the coded SM-MC-CDM transmission is also discussed in the paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".