Cloud Based mmWave WLANs: Architectural Paradigms, Proposals and Perspectives
Bibliographic record
Abstract
With the explosive growth of data traffic over wireless access networks, the use of the millimeter- wave band becomes inevitable due to the spectrum scarcity in lower frequencies and the availability of large chunks of underutilized spectrum in this band. The rapid evolution of wireless technologies and co-existence of legacy and emerging wireless infrastructures are leading to complex network operation scenarios. For instance, to achieve the orchestration of co-existed networks and a large number of devices, dynamic operations of conventional distributed WLANs will become extremely challenging. Various technologies such as dense network deployment, dual-band cooperation, C-RAN, and AI, which have been developed for cellular networks, could be adopted to achieve intelligent and efficient WLAN operations. To meet the future traffic requirements of evolving WLAN, this article first outlines the architecture of future WLAN and the corresponding challenges. A centralized control architecture, named WLAN C-RAN, is proposed to achieve orchestrated coordination and improved network throughput. Critical technologies of WLAN C-RAN, for example, dual-band protocol stack and resource management schemes are also developed, and the simulation results demonstrate the improvements on system capacity. Finally, we present some potential challenges and feasible solutions for the future WLAN.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".