TCP DCERL+: Improving congestion control in mobile ad hoc networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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