Bibliographic record
Abstract
Summary form only given. The tutorial will cover robust system design in a broad range of communication applications. In particular, both broadcast channels and multiple access channels will be considered, and design approaches for both linear and non-linear transceivers will be developed. The tutorial will consider different models, e.g., statistical and bounded, for the uncertainties in the CSI in order to match the model to the method of acquiring the CSI. For these models of channel uncertainty, a variety of transceivers design formulations will be considered, including those based on optimizing performance under transmitter power constraints, and those based on minimizing the transmitted power required to satisfy the QoS constraints specified by the users. The tutorial will also present multiuser systems designs with outage based constraints for both linear and non-linear transceivers, and the design of multi-user transceivers with fairness constraints. The unification of these designs will rely on the theories of convex and robust optimization, hence an introduction to these theories is useful, and will be presented during the tutorial. This introduction will enable an unfamiliar attendee to quickly become acquainted with the core mathematical tools employed in the main part of the tutorial.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.624 | 0.555 |
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".