An Experimental Study on Multipath TCP Congestion Control With Heterogeneous Radio Access Technologies
Bibliographic record
Abstract
In the near future, a large volume of the data traversing wireless networks will not only be requested and/or reported by humans but also by machines (e.g., the Internet-of-things and machine-to-machine applications). This mandates the availability of enormous radio spectrum resources and an end-to-end reliable information transfer. Currently, many wireless devices are equipped with two wireless interfaces with heterogeneous radio access technologies. Thus, the usage of a transport layer designed for multi-homed devices such as multipath transmission control protocol (MPTCP) is inevitable. This paper presents an experimental performance study of three congestion control algorithms, which can be used by MPTCP, namely, Cubic, linked-increases algorithm (LIA), and opportunistic LIA (OLIA). The testbed comprises real (not simulated) LTE and WiFi networks that are used to connect dual-homed wireless nodes to one another. We comparatively study the throughput performance of the three algorithms under varying factors, including the receiver buffer size, number of parallel connections, data volume, and flow lifetime. Our key findings reveal that, although Cubic is not designed with multipath in mind, it outperforms the multipath-based LIA and OLIA, whenever the LTE per-node capacity is higher than its WiFi counterpart. Also, in a reversed situation (WiFi per-node capacity is higher) Cubic outperforms OLIA and LIA for short-lived flows.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".