Real-Time Tracking in a Status Update System With an Imperfect Feedback Channel
Why this work is in the frame
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Bibliographic record
Abstract
We consider a status update system consisting of a finite-state Markov source, an energy-harvesting-enabled transmitter, and a sink. The forward and feedback channels are error-prone. We study the problem of minimizing the long-term time average of a (generic) distortion function subject to an energy limitation. Since the feedback channel is error-prone, the transmitter has only partial knowledge about the transmission results and, consequently, about the estimate of the source state at the sink. Therefore, we model the problem as a partially observable Markov decision process (POMDP), which is then cast as a belief-MDP problem. The infinite belief-state space makes solving the belief-MDP difficult. Thus, by exploiting a specific property of the belief evolution, we truncate the state space and formulate a finite-state MDP problem, which is then solved using the relative value iteration algorithm (RVIA). Furthermore, we propose an energy-agnostic low-complexity policy in which the belief-MDP problem is transformed into a sequence of per-slot optimization problems. Then, the energy-agnostic low-complexity policy is extended to an energy-aware low-complexity policy by adding a regularization term to the objective function of the per-slot problems. Simulation results show the structure and effectiveness of the proposed policies and their superiority compared to baseline policies. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Index Terms:</b> Real-time tracking, status updating, energy harvesting, partially observable Markov decision process.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it