SPCp1-08: Adaptive Learning of Transmission Control Policies for MIMO Fading Channels under Delay Constraint
Bibliographic record
Abstract
This paper addresses learning based adaptive resource allocation for wireless MIMO channels with Markovian fading. The problem is posed as constrained Markov decision process with the goal of minimizing the average transmission cost (such as the transmission power) with the constraint on the average holding cost (such as the transmitter delay). Standard Q-learning algorithm is employed to adaptively find the optimal policy for unknown channel/traffic statistics, its convergence properties discussed and shown that it can relatively quickly compute the optimal policy even for rather large state spaces. In order to further improve the convergence rate of the standard Q- learning, we establish several structural results on the optimal policies. We show that the optimal transmission policy is monotonic in the buffer occupancy. This permits us to utilize the supermodularity of the Q-factors and form a structured Q-learning algorithm that increases the convergence rate with respect to the standard Q-learning 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".