On the Ergodic Capacity of Amplify-and-Forward Relay Channels with Interference in Nakagami-m Fading
Bibliographic record
Abstract
Integrating relaying techniques into cellular communications sheds new light on higher capacity and broader coverage. However, applying relaying techniques in practice has to take into account important issues such as co-channel interference (CCI). In this work, a generalized framework for the ergodic capacity analysis of dual-hop fixed-gain amplify and forward (AF) relaying systems in the presence of interference is presented. New expressions for the ergodic capacity are derived considering transmissions over independent but not necessarily identically distributed Nakagami-m fading channels in the presence of a finite number of co-channel interferers. Our results establish that the ergodic capacity is dominated by the source-relay interference power and that it improves slowly with the average signal-to-noise ratio (SNR) increasing. It slightly deteriorates, however, with a larger Nakagami-m fading parameter for interference channels. Furthermore, our results offer an analytical insight into the key impact of relay placement on performance. Our new ergodic capacity expressions could therefore provide a very practical/low-cost performance optimization tool for relayed-communication system designers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".