Learning-Based Resource Allocation for Data-Intensive and Immersive Tactile Applications
Bibliographic record
Abstract
The immersive tactile applications that are emerging in the entertainment, education and health industries are anticipated to be available for mobile users in the close future. These applications are data-intensive and delay-sensitive due to the nature of information that is being exchanged. With today's mobile networks, the throughput and latency challenges are the major roadblocks for mobile users. In this paper, we propose a resource allocation technique with the aim of increasing throughput and reducing latency of Data Intensive Devices (DIDs). We consider the coexistence of DIDs with traditional User Equipments (UEs) on a two-tier, densely deployed network of Small cell Base Stations (SBSs) and eNBs. We propose a Q-learning-based resource allocation scheme, namely, Throughput Maximizing Q-Learning (TMQ) that learns the efficient resource allocation of both SBSs and eNB. The proposed technique is compared with well-known Proportional Fairness (PF) algorithm in terms of average throughput, delay, and fairness. Simulation results show significant improvement in throughput, 80% reduction in delay, and 6% increase in fairness.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".