Energy-Efficient Adaptive Rate Control for Streaming Media Transmission Over Cognitive Radio
Bibliographic record
Abstract
In future mobile computing systems, cognitive radio (CR) emerges as a promising solution for alleviating spectrum shortage and satisfying the high bandwidth demand of multimedia streaming, while it presents tough challenges in provisioning user experience-perceived quality of service (QoS) and conserving transmission energy. In this paper, an adaptive rate control (ARC) scheme with the aid of the receive buffer is presented for energy-efficient transmission of streaming media over CR with QoS guarantee. The QoS metric, either display smoothness or transmission delay, is quantified by the state of the receive buffer. Cross-layer information is utilized to form a closed-loop feedback optimal control. A novel analytical model called event-driven discrete-time Markov control process is introduced to formulate the QoS-guaranteed energy-efficient ARC problem. Based on potential theory, a policy iteration algorithm that combines potentials estimation and stochastic approximation is proposed for finding the optimal policy online. By exploiting the system dynamics, this algorithm does not depend on any prior knowledge of channel availability or fading statistics, and it can converge to the global optimum with a low computational cost and reasonable speed. Simulation results demonstrate the effectiveness of the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 | 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".