Cross‐layer resource allocation for critical MTC coexistent with human‐type communications in LTE: a two‐sided matching approach
Bibliographic record
Abstract
Cellular systems present one of the most suitable wireless technologies to efficiently serve critical machine‐type communications (MTC) that require strict quality‐of‐service (QoS) guarantees. Therefore, ultra‐reliable and low‐latency communications is a target use case in the design of the upcoming generations of cellular networks. From the radio resource management perspective, guaranteeing such stringent QoS requirements in long term evolution (LTE) networks is a challenging task, especially in the case of the coexistence of MTC with the human‐type communications (HTC). In this study, the authors address the resource allocation and scheduling problem of critical MTC that coexist with HTC in LTE. The optimisation problem is formulated such that the overall system utility is maximised while fulfilling the different QoS demands of the two sets of users. Utilising the effective bandwidth and effective capacity theories, a cross‐layer design is developed to guarantee the QoS requirements of the critical MTC. For a computationally‐efficient solution of the problem, they formulate it as a two‐sided matching process that can be used as a practical scheduling scheme. To this end, they analyse the convergence, stability, and computational complexity of the proposed methods. Results reveal the close‐to‐optimal performance of the matching‐based scheduling scheme and its superiority to other existing techniques.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".