Using Mobility for Agility: Enhancing Wireless Networks with Aerial Access Nodes and User Involvement
Bibliographic record
Abstract
Considering numerous futuristic applications that will be enabled by wireless networks, one may wonder the essence of the next evolution in wireless networks.Recently, concepts and technologies such as polar codes, wireless network virtualization, millimeter wave communications, and non-orthogonal multiple access have emerged as new enablers.They provide promising solutions for significant problems by enhancing capacity, flexibility, and spectrum usage.However, they cannot address precisely the most needed new capability to handle diverse applications without relying on to gross over-engineering: Agility.This study proposes a new solution to dramatically improve agility without leaning on overengineering: Using mobility for agility, where the inherent support of wireless networks for user terminal mobility is used to support the mobility of access points.In addition, we propose to influence the user demand in space and time to flexibly shape the network from both sink and source perspectives.We propose the spatial network configuration (SNC) scheme, which utilizes dronebase-stations (drone-BSs) to re-configure topology of access points, and user-in-the-loop (UIL) to influence demand of users.Drone-BSs are shifting paradigms of heterogeneous wireless networks by providing radically flexible deployment opportunities.On the other hand, their limited endurance and potential high cost increase the importance of utilizing drone-BSs efficiently.Therefore, we thoroughly investigate efficient utilization of drone-BSs from placement methods to refinements on air-to-ground channel modeling.To further exploit drone-BSs, we influence locations of users via the UIL method, which aims at influencing mobility of the users by offering incentives.Finally, we investigate the SNC scheme with a holistic approach and propose the drone management framework.We show that the integration methods severely affect the network performance when wireless access virtualization is applied.Furthermore, we survey the latest developments in 3GPP 5G Release-16 standardization to discuss the capabilities and shortcomings of current and I am deeply grateful for my PhD studies, which enhanced my life both professionally and personally, with the help of many amazing people, among which certainly my supervisor Prof. Halim Yanikomeroglu comes first.His generous support, strong professionalism, exemplary attitudes, profound knowledge, and patience have guided this thesis for the more visionary and myself for the better.Huawei Technologies Canada research and development team has provided fundamental support, the most valuable discussions, and nonesuch guidance for the completion of this thesis, specially Dr.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".