Performance of Optimum Switching Adaptive $M$-QAM for Amplify-and-Forward Relays
Bibliographic record
Abstract
Optimization of the switching thresholds for constant-power adaptive five-modeM-ary quadrature amplitude modulation (M-QAM) transmission with an amplify-and-forward (AF) relay network is developed. The optimization criterion is the maximization of spectral efficiency subject to an average bit-error-rate (BER) constraint. This approach results in a constant-BER variable-rateM-QAM AF relay system, which requires feedback of log2(N) bits forNmodes. The performance analysis is based on an upper bound on the total effective SNR. Expressions are derived for the outage probability, the achievable spectral efficiency, and the error-rate performance for the AF cooperative system over both independent identically distributed (i.i.d.) and non-i.i.d. Rayleigh fading environments. The tightness of the upper bound is validated by Monte Carlo simulation. Adaptive five-modeM-QAM with optimum switching levels is shown to offer performance gains of 2-2.5 dB compared with fixed switching in terms of the transmit SNR to achieve specific spectral efficiency. Furthermore, the spectral efficiency of adaptive five-modeM-QAM with optimized switching comes within ~6 dB of the theoretical Shannon channel capacity. However, this performance gain, which is obtained by employing adaptiveM-QAM under cooperative diversity, comes at the cost of increased system complexity that is incurred due to the additional complexity of transmitter and receiver design.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".