A Deep Dive into Congestion Control and Buffer Management for Fluctuation-Prone 5G-A/6G Links
Bibliographic record
Abstract
The introduction of the next generations’ mobile communications, 5 G -Advance and $\mathbf{6 G}$ (5G-A/6G), promises boosting data throughput to new dimensions, achieving submillisecond latency, and providing wider coverage. Based on this promise, a great number of previously infeasible high-throughput, real-time, and IoT-based applications, such as high-resolution face recognition and extended reality, are being developed for deployment over 5G-A/6G networks. Such demanding applications assume that ample bandwidth will be available through the utilization of a high-frequency spectrum. However, at high frequencies, radio channels are susceptible to sudden changes in the surrounding conditions, generating highly fluctuating scenarios that directly impact the performance of upperlayer protocols and services. For applications that operate under end-to-end congestion control algorithm (CCA) (e.g., TCP- and QUIC-based applications), extreme fluctuations may generate unwanted behaviors that hurt the throughput and possibly favor non-CCA traffic with unfair results in bandwidth distribution. This paper thoroughly investigates the impact of fluctuating radio access channels on $5 \mathrm{G}-\mathrm{A} / \mathbf{6 G}$ networks. We analyze the performance of various congestion control algorithms, including CUBIC, High-Speed, and BBR, as well as non-CCA traffic, under such conditions. Our evaluation, conducted through realistic simulations, examines the network’s ability to maintain desired service levels amidst fluctuations. Furthermore, we explore the potential of state-of-the-art active queue management and buffer management policies at the gNB to mitigate the negative effects of these fluctuations and enhance overall network performance.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".