Exploring the Performance of Fluid Antenna System (FAS)-Aided B5G mmWave Networks
Bibliographic record
Abstract
Reconfigurability and innovative design approaches to radio frequency components and network infrastructure are critical for the development of future communication networks, particularly beyond 5G (B5G), which aim to support the proliferation of Internet of Things (IoT) devices. Leveraging its favorable performance characteristics and potentially low cost, the fluid antenna system (FAS) has emerged as a compelling solution, garnering significant interest due to its reconfigurability, small form factor, flexibility, and transparency. This paper presents a comprehensive analysis of FAS in the context of B5G networks, with a focus on its theoretical performance and practical implementations. By deriving formulas for the semi-infinite outage probability and ergodic capacity of FAS receivers in equally correlated Nakagami-m channels, we showcase the remarkable diversity performance exhibited by FAS receivers, even with a half-wavelength antenna size. Monte Carlo simulations are employed to validate our theoretical findings in terms of the number of antenna ports and transmission power.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".