Cost-Efficient VBI-Based Multiuser Detection for Uplink Grant-Free MIMO-NOMA
Bibliographic record
Abstract
Grant-free non-orthogonal multiple access (GF-NOMA) based on multiple-input multiple-output (MIMO) has attracted much attention as a promising technique to support massive connectivity and bursty data transmission in massive machine-type communication. In this paper, we propose two compressed sensing based multiuser detection (MUD) algorithms for the MIMO-enabled GF-NOMA system. First, the spatially enhanced variational Bayesian inference (SE-VBI) algorithm is developed for MUD by exploiting the Gaussian mixture prior and diversity combining technique. Then, by applying the covariance-free (CoFe) strategy to the SE-VBI framework to estimate the diagonal elements of the posterior covariance, we propose a low-complexity MUD method named SE-CoFe-VBI. In particular, the proposed algorithms integrate the multivariate nature of the transmitted signal, i.e., discreteness, sparsity, and spatial correlation. Simulation results show that the proposed algorithms offer improved detection performance over the state-of-the-art spatially enhanced sparse Bayesian learning method.
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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 | 0.006 |
| 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 | 0.002 |
| 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".