Matching-Based Resource Allocation for Critical MTC in Massive MIMO LTE Networks
Bibliographic record
Abstract
Supporting critical Machine-Type Communications (MTC) in addition to Human-Type Communications (HTC) is a major target for LTE networks to fulfill the 5G requirements. However, guaranteeing a stringent Quality-of-Service (QoS) for MTC, in terms of latency and reliability, while not sacrificing that of HTC is a challenging task from the radio resource management perspective. In this paper, we optimize the resource allocation process through exploiting the additional degrees of freedom introduced by massive Multiple-Input Multiple-Output (MIMO) techniques. We utilize the effective bandwidth and effective capacity concepts to provide statistical guarantees for the QoS, in terms of probability of delay-bound violation, of critical MTC in a cross-layer design manner. In addition, we employ the matching theory to solve the formulated combinatorial problem with much lower computational complexity compared to that of the global optimal solution so that the proposed scheme can be used in practice. In this regard, we analyze the computational complexity of the proposed algorithms and prove their convergence, stability and optimality. The results of extensive simulations that we performed show the ability of the proposed matching-based scheme to satisfy the strict QoS requirements of critical MTC with no impact on those of HTC. In addition, the results show a close-to-global optimal performance while outperforming other algorithms that belong to different scheduling strategies in terms of the adopted performance indicators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".