Cross-Layer Design for TCP Throughput Optimization in Cooperative Relaying Networks
Bibliographic record
Abstract
In this paper, we investigate the transmission control protocol (TCP) throughput in cooperative relaying networks and take an cross-layer design approach when selecting a relay to optimize the TCP throughput. A first-order finite-sate Markov channel (FSMC) is used to model the wireless time varying channels, and the TCP throughput is estimated as a function of physical layer signal-to-noise ratio (SNR) and link-layer frame size and retransmission times. Since relay selection is crucial in improving the TCP performance, we proposed a stochastic decision making approach to select the optimal relay for every TCP packet according to the states of each relay. We formulated the cross-layer TCP throughput optimization problem as a restless bandit system and obtained the statistically optimal relay selection policy, which has an indexability property and can be easily implemented in real system. We compare the proposed scheme through simulations under different parameters of physical layer and link-layer, simulation results show that the TCP throughput can be improved significantly by the optimal relay selection scheme.
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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 | 0.005 | 0.007 |
| 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 | 0.001 |
| 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".