Deep Reinforcement Learning for Joint User Association and Resource Allocation in Factory Automation
Bibliographic record
Abstract
We consider the problem of joint user association, channel assignment, and power allocation for mobile robot application in factory automation system that require ultrareliable and low latency communications (URLLC). The aim is to deliver control commands from the controller to mobile robots with stringent requirements of latency and reliability. To achieve URLLC, we develop a two-phase communication scheme. The robots work close to each other in a factory environment and can form clusters for reliable device-to-device (D2D) communications. Within the latency requirements, the combined payload of a cluster is transmitted to the leader in Phase I. In Phase II, the leader broadcasts the payload to its members. Under this strategy, we use multi-agent reinforcement learning (MARL) for resource allocation. The cluster leaders in Phase I act as the agents and interact with the environment to optimally select the Access Point (AP) for connection along with the sub-band and power level. The objective is to maximize the successful payload delivery probability to all the robots. Illustrative simulation results indicate that the proposed scheme can offer average successful payload delivery probability close to that of centralized exhaustive search algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".