Deep Q-Learning for Low-Latency Tactile Applications: Microgrid Communications
Bibliographic record
Abstract
Ultra low-latency is one of the key requirements of tactile internet applications such as microgrid communication, where low end-to-end delay of control messages is essential. In addition, small cell wireless networks emerge as the enabler of networked microgrids, given their coverage, capacity and flexibility. Such densification can help in addressing the stringent QoS requirements of microgrid communications. In dense networks, besides the traditional resource allocation problem, user association can help in reducing communication latency, where users can associate with the base stations that can serve best. In this paper, we jointly address resource allocation and user/device association when Critical User Devices (CUDs), i.e. microgrid controllers, and non-critical users, i.e. User Equipments (UEs), co-exist in a small cell network. We formulate our optimization problem as a resource allocation as well as user association problem. We propose a deep Q-Network based algorithm, namely Delay Minimizing Deep Q-Network (DM-DQN) to address the low-latency requirement. DM-DQN aims at reducing the delay of CUDs by balancing the trade-off between allocating more RBs and associating devices to base stations with high channel quality. We compare the performance of DM-DQN to a tabular Q-learning algorithm. Our results show that the proposed scheme achieves 41% delay reduction for CUDs and it converges faster than Q-learning based scheme algorithm.
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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 | 0.003 |
| 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".