Stochastic geometry analysis of multiple access, mobility, and learning in cellular networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Use cases of future wireless networks (e.g. fifth-generation [5G] networks and beyond [B5G]) will have service-quality requirements including higher data rates than today's networks for enhanced mobile broadband (eMBB), minimal latency and high network availability for ultra-reliability low-latency connection (URLLC), and massive access support for machine-type communications (mMTC). Also, 5G and B5G are expected to support communications for highly mobile scenarios with applications in new vertical sectors such as unmanned aerial vehicle (UAV) and autonomous car. Therefore, 5G and B5G cellular systems require a set of new technology enablers and solutions. In this thesis, we address some of the challenges of future wireless networks. In particular, we develop novel analytical models as well as methods, which will enable us to obtain insights into the performance of large-scale cellular networks and optimize network parameters. Non-orthogonal multiple access (NOMA) is a promising multiple access technique that enables massive connectivity and reduces the delay. We develop an analytical framework to derive the distribution of transmission success probabilities, meta distribution, for uplink and downlink NOMA. We also investigate the accuracy of distance-based ranking, instead of instantaneous signal power-based ranking, in the successive interference cancellation (SIC) at the NOMA receiver. Sojourn time, the time duration that a mobile user stays within a cell, is a mobility-aware parameter that can significantly impact the performance of mobile users and it can also be exploited to improve resource allocation and mobility management methods in the network. We derive the distribution and mean of the sojourn time in multi-tier cellular networks. Future wireless networks will exploit data-driven machine learning techniques for improving network management as well as service provisioning. Due to privacy and communication issues, learning at a centralized location (for example, at a base station) by collecting data from the mobile devices may not be always feasible. Federated learning is a machine learning setting where the centralized location trains a learning model using remote devices. Federated learning algorithms cannot be employed in real-world scenarios unless they consider unreliable and resource-constrained nature of the wireless medium. We propose a federated learning algorithm that is suitable for wireless networks.
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.
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 | 0.001 |
| 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 it