Distortion-transmission trade-off in real-time transmission of Markov\n sources
Bibliographic record
Abstract
The problem of optimal real-time transmission of a Markov source under\nconstraints on the expected number of transmissions is considered, both for the\ndiscounted and long term average cases. This setup is motivated by applications\nwhere transmission is sporadic and the cost of switching on the radio and\ntransmitting is significantly more important than the size of the transmitted\ndata packet. For this model, we characterize the distortion-transmission\nfunction, i.e., the minimum expected distortion that can be achieved when the\nexpected number of transmissions is less than or equal to a particular value.\nIn particular, we show that the distortion-transmission function is a piecewise\nlinear, convex, and decreasing function. We also give an explicit\ncharacterization of each vertex of the piecewise linear function.\n To prove the results, the optimization problem is cast as a decentralized\nconstrained stochastic control problem. We first consider the Lagrange\nrelaxation of the constrained problem and identify the structure of optimal\ntransmission and estimation strategies. In particular, we show that the optimal\ntransmission is of a threshold type. Using these structural results, we obtain\ndynamic programs for the Lagrange relaxations. We identify the performance of\nan arbitrary threshold-type transmission strategy and use the idea of\ncalibration from multi-armed bandits to determine the optimal transmission\nstrategy for the Lagrange relaxation. Finally, we show that the optimal\nstrategy for the constrained setup is a randomized strategy that randomizes\nbetween two deterministic strategies that differ only at one state. By\nevaluating the performance of these strategies, we determine the shape of the\ndistortion-transmission function. These results are illustrated using an\nexample of transmitting a birth-death Markov source.\n
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".