A Reconfigurable Access Scheme for Massive-MIMO MTC Networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an efficient reconfigurable access scheme for massive machine-type communication networks, where to provide massive connectivity, the base station is equipped with a large-scale antenna array. In particular, to maximize the expected throughput of the network, the scheme uses a frame divided into two segments: grant-based and grant-free that are more efficient for devices with high and low expected throughput, respectively. At the beginning of each frame, the base station decides how to partition the two segments, the resource allocation in the grant-based segment and the access probabilities in the grant-free segment to maximize the expected throughput based on the device traffic profiles. The corresponding optimization problem is formulated, and a sub-optimal solution algorithm with low computational complexity is proposed. The performance of the proposed access scheme is evaluated under different conditions to demonstrate its advantages in terms of achieved throughput and average packet delay.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it