XR Streaming Performance With Wi-Fi 7 Multi-Link Operation
Bibliographic record
Abstract
Extended Reality (XR) has stringent throughput and delay requirements that are hard to meet with current wireless technologies. Missing these requirements can lead to worsened picture quality, perceived lag between user input and corresponding output, and even dizziness for the end user. In this paper, we study the capability of Wi-Fi 7, and its novel support for Multi-Link Operation (MLO), to cope with these tight requirements. Our study is based on simulation results extracted from an MLO-compliant simulator that realistically reproduces VR traffic. Results show that MLO can sustain Virtual Reality (VR) applications. By jointly using multiple links with independent channel access procedures, MLO can reduce the overall delay, which is especially useful in the uplink, as it has more stringent requirements than the downlink, and is instrumental in delivering the expected performance. We show that using MLO can support more users per Access Point (AP) than an equivalent number of links using Single Link Operation (SLO). We also show that, while maintaining the same overall bandwidth, a higher number of MLO links with narrower channels leads to lower delays than a lower number of links with wider channels. We also study the impact of Overlapping Basic Service Sets (OBSS) on performance of XR applications, showing how SLO struggles to maintain a low delay when OBSS activity increases, and how MLO can cope with this interference by using MLO-aware channel assignment strategies. Finally, we consider random positioning for the users, showcasing that MLO can support 5× more users than SLO.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".