Bibliographic record
Abstract
In the past few years, machine learning (ML) techniques have been introduced for the physical layer applications in wireless communications. In contrast to employing centralized learning (CL) techniques, federated learning (FL) presents lower communication overhead as it does not involve dataset transmission between the edge users and the server. As a result, FL is particularly useful for applications, wherein the dataset is huge. Such examples include physical layer design applications, which may require huge datasets to represent the environment accurately. This chapter is concerned with FL-based physical layer applications, e.g., channel estimation and hybrid beamforming. The channel estimation problem is investigated for both conventional and reconfigurable intelligent surface-aided millimeter wave (mmWave) and terahertz (THz) scenarios. We begin by introducing the channel models for both mmWave and THz. Then, we discuss the implementation of FL for various channel estimation problems. We also discuss near-field channel estimation, which may occur in the THz scenario, for which the operating wavelength is very small. Then, we present FL-based hybrid beamforming in mmWave wireless communications. The performance evaluation of FL is provided via several numerical simulation results to show its effectiveness.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".