Dual‐hop amplify‐and‐forward cooperative relaying in the presence of Tx and Rx in‐phase and quadrature‐phase imbalance
Bibliographic record
Abstract
In this study, dual‐hop channel state information‐assisted amplify‐and‐forward (AF) cooperative systems in the presence of in‐phase and quadrature‐phase (I/Q) imbalance, which refers to the mismatch between components in the I and Q branches, are investigated. First, the authors analyse the performance of the considered AF cooperative protocol without compensation for the I/Q imbalance as the benchmark. Then, a compensation algorithm for the I/Q imbalance is proposed, which makes use of the received signals at the destination, from the source and the relay nodes, together with their conjugations to detect the transmitted signal. Moreover, the authors study the considered AF cooperative system implemented with the opportunistic relay selection and the proposed compensation mechanism for the I/Q imbalance. The performance of the AF cooperative system under study is evaluated in terms of average symbol error probability, which is derived by considering transmission in a Rayleigh fading environment. Numerical results are provided and show that the proposed compensation algorithm can efficiently mitigate the effect of the I/Q imbalance. On the other hand, it is observed that the AF cooperative system with opportunistic relay selection acquires a performance gain beyond that without relay selection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".