A Novel Non-Coherent SCMA With Massive MIMO
Bibliographic record
Abstract
The synergistic amalgamation of sparse code multiple access (SCMA) and multiple-input multiple-output (MIMO) technologies can be exploited for improving spectral efficiency and providing enhanced wireless services to massive users. In this case, however, channel estimation is a burning issue with the increasing number of users and/or antennas. To tackle this problem, we propose a novel non-coherent transmission scheme for SCMA, referred to as NC-SCMA. In the proposed NC-SCMA, each user first maps its binary data to sparse codewords, and then perform differential modulation on the non-zero dimensions. Upon receiving all users’ signals, we leverage the channel hardening effect to carry out differential demodulation and multi-user detection without any instantaneous channel state information. In addition, the design of the sparse codebooks in the NC-SCMA system is investigated with the aid of the pair-wise probability. Numerical results demonstrate the superiority of the proposed technique over the benchmark scheme in terms of bit error rate performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".