Performance and Optimization of Amplify-and-Forward Cooperative Diversity Systems in Generic Noise and Interference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cooperative diversity systems have received significant attention recently as a distributed means of exploiting the inherent spatial diversity of wireless networks. In this paper, we consider a cooperative diversity system consisting of a source, a destination, and multiple single-hop amplify-and-forward relays, and provide a mathematical framework for the asymptotic analysis of this system in generic noise and interference for high signal-to-noise ratios. Assuming independent Rayleigh fading for all links in the network and orthogonal relay-destination channels, we obtain simple and elegant closed-form expressions for the asymptotic symbol and bit error rates valid for arbitrary linear modulation formats, arbitrary numbers of relays, and arbitrary types of noise and interference with finite moments including co-channel interference, ultra-wideband interference, impulsive ε-mixture noise, generalized Gaussian noise, and Gaussian noise. Furthermore, we exploit the derived analytical error rate expressions to develop power allocation, relay selection, and relay placement schemes that are asymptotically optimal in environments with generic noise and interference. In general, the power allocation problem results in a geometric program which can be solved efficiently numerically. For the special case of only one relay, we provide a closed-form result for the optimal power allocation. Simulation results confirm our analysis and illustrate that, in non-Gaussian noise, the proposed power allocation, relay selection, and relay placement schemes lead to large performance gains compared to their conventional counterparts optimized for Gaussian noise.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it