A Hybrid Collaborative Learning for Age of Information Minimization in Massive Access
Bibliographic record
Abstract
Supporting massive access from Internet of Things (IoT) devices plays a pivotal role in the design of 6G networks. Nonetheless, concurrent massive access to the network deteriorates the quality of 6G communications. To overcome the challenge of massive access, our focus shifts to optimizing the age-critical frameless ALOHA (ACFA) random access protocol. The conventional ACFA suffers from high latency and unreliability when massive devices attempt to access the network. Consequently, we introduce an adaptive algorithm to address the transmission issues. This paper optimizes the random access channel (RACH) procedure by maximizing a long-term multi-objective function, which consists of the average age of information (AoI), normalized throughput, traffic load and the average number of successfully accessed machine-type communication devices. To achieve the optimal objective in ACFA, we apply deep reinforcement learning (DRL) algorithms. In our algorithms, agents take action in both distributed and centralized manners. In the distributed approach, each device learns the choice of the access probability and the slot, guided by feedback from the base station (BS). Simultaneously, in the centralized approach, the BS restricts a specific number of devices from accessing the network and dynamically adjusts the frame length based on the transmission results of devices. Our simulation results demonstrate that the proposed scheme surpasses benchmark schemes and exhibits significant potential to minimize AoI performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".