Antenna/Relay Selection for Coded Wireless Cooperative Networks
Bibliographic record
Abstract
In this paper, we consider distributed coding for wireless cooperative networks with antenna/relay selection. The aim of this of work is to find ways to improve the reliability of the source-relay link in an effort to maintain the diversity order available in the system. To this end, we propose to use antenna selection at the relay node whereby the antenna with the best instantaneous received signal to noise ratio is selected. This assumes that the relay node is equipped with multiple antennas, but only one radio frequency (RF) chain is employed. The concept of antenna selection can be extended to relay selection. That is, among the available relay nodes, the one with the best source-relay link reliability is selected. Assuming decode-and-forward (DF) relaying, we analyze the antenna/relay selection in conjunction with a previously proposed distributed coded cooperation scheme based on convolutional codes. Specifically, we derive an upper bounded expression for the symbol error rate assuming M-ary phase shift keying (M-PSK) transmission. Our analytical results show that the maximum diversity order of the system is maintained for the entire range of bit error rate of interest, unlike the case without antenna selection. Several numerical and simulation results are presented to demonstrate the efficiency of the proposed scheme.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".