Moments Based Framework for Performance Analysis of One-Way/Two-Way CSI-Assisted AF Relaying
Bibliographic record
Abstract
When analyzing system performance of conventional one-way relaying or advanced two-way relaying, these two techniques are always dealt with separately and, thus, their performance cannot be compared efficiently. Moreover, for ease of mathematical tractability, channels considered in such studies are generally assumed to be subject to Rayleigh fading or to be Nakagami-m channels with integer fading parameters, which is impractical in typical urban environments. In this paper, we propose a unified moments-based framework for general performance analysis of channel-state-information (CSI) assisted amplify-and-forward (AF) relaying systems. The framework is applicable to both one-way and two-way relaying over arbitrary Nakagami-m fading channels, and it includes previously reported results as special cases. Specifically, the mathematical framework is firstly developed under the umbrella of the weighted harmonic mean of two Gamma-distributed variables in conjunction with the theory of Padé approximants. Then, general expressions for the received signal-to-noise ratios of the users in one-way/two-way relaying systems and the corresponding moments, moment generation function, and cumulative density function are established. Subsequently, the mathematical framework is applied to analyze, compare, and gain insights into system performance of one-way and two-way relaying techniques, in terms of outage probability, average symbol error probability, and achievable data rate. All analytical results are corroborated by simulation results as well as previously reported results whenever available, and they are shown to be efficient tools to evaluate and compare system performance of one-way and two-way relaying.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".