Cross-Layer Design for TCP Performance Improvement in Cognitive Radio Networks
Why this work is in the frame
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Bibliographic record
Abstract
In cognitive radio (CR) networks, the end-to-end transmission-control protocol (TCP) performance experienced by secondary users is a very important factor that evaluates the secondary user perceived quality of service (QoS). Most previous works in CR networks ignore the TCP performance. In this paper, we take a cross-layer design approach to jointly consider the spectrum sensing, access decision, physical-layer modulation and coding scheme, and data-link layer frame size in CR networks to maximize the TCP throughput in CR networks. The wireless channel and the primary network usage are modeled as a finite-state Markov process. Due to the miss detection and the estimation error experienced by secondary users, the system state cannot be directly observed. Consequently, we formulate the cross-layer TCP throughput optimization problem as a partially observable Markov decision process (POMDP). Simulation results show that the design parameters in CR networks have a significant impact on the TCP throughput, and the TCP throughput can be substantially improved if the low-layer parameters in CR networks are optimized jointly.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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