MIMO Transmission Control in Fading Channels—A Constrained Markov Decision Process Formulation With Monotone Randomized Policies
Bibliographic record
Abstract
This paper addresses the optimal power and rate allocation control in multiple-input multiple-output (MIMO) wireless systems over Markovian fading channels. The problem is posed as an infinite horizon average-cost constrained Markov decision process (CMDP) with the goal of minimizing the average transmission power subject to delay constraints. By using a Lagrangian formulation of the CMDP, we use the concepts of stochastic dominance, submodularity, and multimodularity to prove that the optimal randomized policies are monotone. Three important structural results on the nature of the optimal randomized policies are derived. First, we show that the action space can be exponentially reduced by decomposing the rate allocation problem into bit-loading problem across individual antennas and the total rate allocation based on the current buffer occupancy and channel state. Second, we show that the optimal rate allocation policy is a randomized mixture of two pure policies that are monotonically increasing in the buffer occupancy. Finally, we show that the optimal power allocation is piecewise linear in the delay constraint. These three structural results can be exploited to devise efficient online reinforcement learning algorithms for optimal rate allocation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".