Performance Analysis for Multihop Relaying Channels with Nakagami-m Fading: Ergodic Capacity Upper-Bounds and Outage Probability
Bibliographic record
Abstract
This paper investigates the ergodic capacity and outage probability performance of multihop relaying networks subject to independent non-identically distributed Nakagami-m fading. Particularly, we exploit a typical amplify-and-forward relaying system with an arbitrary number of cooperative intermediate relays and no direct link between the source and destination nodes. In our analysis, channel state information is assumed to be known only at the receiving nodes and the cooperative links may have distinct fading parameters and distinct average signal-to-noise ratio (SNR) levels. In this context, a tight closed-form upper bound expression for the ergodic capacity is derived. For this, firstly the moment generating function (MGF) of the inverse of the end-to-end SNR is obtained in closed-form. Then, making use of this expression, an upper bound for the ergodic capacity is attained. Thereafter, we investigate the end-to-end outage probability performance of the multihop relaying channels in Nakagami-m fading by making use of the aforementioned MGF expression. Finally, Monte-Carlo simulation results are provided and show the tightness of the proposed bounds.
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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 | 0.002 | 0.015 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".