Deep Recurrent Reinforcement Learning for Partially Observable User Association in a Vertical Heterogenous Network
Bibliographic record
Abstract
To ensure ubiquitous connectivity and meet increasing users’ demands in next-generation wireless networks, we investigate user association in a three-layer network consisting of a terrestrial base station (TBS), a high-altitude platform station (HAPS), and a satellite. To maintain scalability and reduce frequent channel state information (CSI) exchanges across layers, we assume only the CSI of previously associated links is available. To address our partially observable user association problem, we propose the action-specific deep recurrent Q-network (ADRQN) method. This involves incorporating a long short-term memory (LSTM) layer alongside fully connected layers, utilizing both observation and action vectors in the policy network. We compare our proposed ADRQN method to the exhaustive search, deep Q-network, deep recurrent Q-network, and convex optimization methods and demonstrate the necessity of using the action vector. Finally, we consider the absence of perfect CSI and show that our ADRQN agent outperforms the convex optimization method in this scenario.
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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 | 0.000 | 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".