On achievable rate and ergodic capacity of non-symmetric half-duplex NAF relay channels
Bibliographic record
Abstract
In this paper, we investigate the achievable rate and ergodic capacity of a general non-symmetric half-duplex non-orthogonal amplify-and-forward (NAF) single-relay channel in Rayleigh fading environments, assuming that the channel state information is only available at the destination. The considered channel, which captures pathloss and shadowing effects over the transmission links, includes the symmetric one as a special case. At first, for a given power allocation scheme, simple and closed-form expressions of the upper and lower bounds on the achievable rate are derived. As shown by various numerical examples, the gap between the upper and lower bounds is small in the entire range of SNRs, which makes them useful in finding the optimal power allocation solution to achieve the capacity. Focusing on the two extreme cases of low and high SNRs, we then provide relatively tight approximations of the achievable rate. Using these approximations, it is then revealed that at both high and low SNR regimes, the ergodic capacity is achieved when the relay is inactive. Equivalently, NAF relaying does not yield any advantage over direct transmission at low and high SNRs. The results and observations in this paper therefore provide some further important insights on NAF relaying.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".