Diverse Traffic Demands Oriented Multi-User Detection for Grant-Free Massive MTC Networks
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Bibliographic record
Abstract
The diverse time-varying transmission demands cause significant challenges in the grant-free based multi-user detection (MUD) scheme design for massive machine-type communications (mMTC) networks. In this paper, we develop a multistate Markov model to characterize the diverse time-varying traffic demands, where the temporal correlation of the user activity and the data length diversity are considered simultaneously. Based on the developed Markov model, a diverse traffic demands oriented MUD scheme is proposed to realize the efficient joint user activity and data detection. Specifically, we first construct the block sparse structure for the transmitted signal to fully exploit the structured sparsity of the data matrix. Then, we convert the MUD into a maximum a posteriori probability (MAP) problem such that the block sparsity of the transmitted signal and the temporal correlation and data length diversity provided by the established Markov model can be efficiently exploited. Moreover, we further develop an intra-block pruning aided Bayesian block orthogonal matching pursuit (IBPA-BBOMP) algorithm such that the formulated MAP problem is efficiently solved. Simulation results show that the proposed scheme can achieve a substantial performance gain over existing methods.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it