Enhancing TCP performance in wide-area cellular wireless networks: transport level approaches
Bibliographic record
Abstract
Internet technology-based architectures and protocols for supporting multimedia traffic over wireless networks are evolving. Since TCP (Transmission Control Protocol)/IP (Internet Protocol) is the standard network protocol stack on the Internet, its use over the next-generation wireless mobile networks is a certainty. Performance of TCP would be one of the most critical issues in IP-based data networking over wireless links. This article presents a comprehensive study on the performances of the basic TCP variants (e.g., TCP Tahoe, TCP Reno, TCP New-Reno, SACK TCP, FACK TCP) in wide-area cellular wireless networks. For the basic TCP variants, an in-depth analysis of the transport-level system dynamics is presented based on computer simulations using ns-2. Impacts of variations in wireless channel error characteristics, number of concurrent TCP flows and wireless link bandwidth on the average TCP throughput and fairness performances are investigated. The maximum achievable throughput under window-based end-to-end transmission control is also evaluated and the throughput performances of TCP New-Reno, SACK TCP and FACK TCP are compared against this ideal TCP throughput performance. To this end, an overview of the major modifications to the basic TCP variants based on transport-level approaches to enhance TCP performance in wired-cum-wireless networks is presented.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".