Dynamic Power Allocation over Block-Fading Channels with Delay Constraint
Bibliographic record
Abstract
The problem of allocating power over a non-ergodic Gaussian block fading channel is addressed for delay constrained applications, where transmission takes place over a limited number of time slots. We propose an algorithm in which the transmission power is determined at each time slot based on the channel condition at the current and future time slots, where a Markov model is used to capture the correlation between channel coefficients in different time slots. The problem is formulated in the framework of finite-horizon dynamic programming, where the optimal transmission strategy is assigned based on the relative importance of power and the quality of service (QoS). Depending on the importance of meeting the QoS constraint compared to the cost of power, the best power level is dynamically assigned by the algorithm, taking into account the channel state and the chance of meeting the QoS constraint. The performance of the proposed dynamic power allocation algorithm is evaluated for different channel states and QoS constraints. We compare the performance of the algorithm with schemes having strict constraints on power. Simulation results show that due to the flexibility given to the algorithm by removing the strict power constraint, the dynamic power allocation algorithm outperforms the optimal power constrained algorithm. Also, the results indicate that increasing the cost of power at the transmitter changes the system dynamics in a way that keeps the balance between QoS and power consumption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".