Performance Analysis of Incremental Relaying Cooperative Diversity Networks over Rayleigh Fading Channels
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Bibliographic record
Abstract
Cooperative diversity networks have recently been proposed as a way to form virtual antenna arrays without using collocated multiple antennas. Cooperative diversity networks use the neighbor nodes to assist the source by sending the source information to the destination for achieving spatial diversity. Regular cooperative diversity networks make an inefficient use of the channel resources because relays forward the source signal to the destination every time regardless of the channel conditions. Incremental relaying cooperative diversity has been proposed to save the channel resources by restricting the relaying process to the bad channel conditions only [1]. Incremental relaying cooperative relaying networks exploit limited feedback from the destination terminal, e.g., a single bit indicating the success or failure of the direct transmission. If the destination provides a negative acknowledgment via feedback; in this case only, the relay retransmits in an attempt to exploit spatial diversity by combining the signals that the destination receives from the source and the relay. In this paper, we study the end-to-end performance of incremental relaying cooperative diversity networks using amplify-and-forward relays over independent non-identical Rayleigh fading channels. Closed-form expressions for the bit error rate and the signal-to-noise ratio (SNR) outage probability are determined. Results show that the incremental relaying cooperative diversity can achieve the maximum possible diversity, compared with the regular cooperative diversity networks, with higher channel utilization.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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