TCP DCERL+: Improving congestion control in mobile ad hoc networks
Bibliographic record
Abstract
With the improvement of wireless access networks, applications have been developed requiring a very high data rate. Random loss due to mobility and channel fluctuations leads to the devolution of the performance of such high data rate networks. Many variants of TCP congestion control algorithms have been proposed to achieve high performance but all fall below the desired throughput. In this paper, we propose a Dynamic TCP Congestion Control Enhancement for Random Loss Plus (DCERL+). This algorithm is a modification of the TCP Reno protocol at the sender end, differentiating between random loss and congestion loss. DCERL+ achieves very high throughput by employing an estimated bottleneck queue length algorithm to control the congestion window adaptively. We evaluate the performance of DCERL+ in terms of throughput, energy, end-to-end delay, and node mobility speed, and compare it with legacy and recent algorithms. We have performed the simulations of DCERL+ in NS3. The simulation results show that the performance of DCERl+ outperforms recent algorithms such as DA-BBR and D-TCP.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".