Beamforming and combining based on estimated channels in cooperative relay networks
Bibliographic record
Abstract
In this paper, we study an amplify-and-forward (AF) based multiple-input multiple-output (MIMO) cooperative relay network in which beamforming is done by using estimated channels in a Rayleigh fading environment. A protocol for training of the source, relay and destination is described and methods of channel estimation are proposed. Simulation results show that the trained MIMO relay system based on beamforming outperforms the trained MIMO relay system without beamforming. It is also shown that the performance of the beamforming based MIMO relay system improves with increase in the number of receive antennas at the destination. We analyze the symbol error rate (SER) versus signal-to-noise ratio (SNR) performance of beamforming based AF relaying with perfect channel knowledge at the source, relay, and destination. An expression of the moment generating function (m.g.f.) of the received instantenous SNR at the destination is derived. By using this m.g.f., an exact expression for the SER of M-ary phase-shift keying is obtained.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".