Throughput Maximization for User Cooperative Wireless Systems with Adaptive Modulation
Bibliographic record
Abstract
Adaptive modulation has been widely adopted in broadband wireless communication systems to improve spectrum efficiency. On the other hand, user cooperative diversity has been investigated to improve system coverage and efficiency. How to take the advantage of adaptive modulation for user cooperative transmissions to maximize network throughput under the constraint of the bit error rate (BER) requirement is an open issue. In this paper, a simple user-cooperation strategy with adaptive M-ary Quadrature Amplitude Modulation (M-QAM) is proposed to fill the gap. We use an approximate BER expression of M-QAM modulation to formulate an easy-to-solve optimization problem, so the modulation types for the source node and the relay node can be optimized in real time to maximize the throughput under the BER constraint. To maximize the throughput for the whole network, we further use a worst-link-first (WLF) matching algorithm for selecting appropriate cooperators. Numerical results show that the proposed adaptive cooperative protocol can effectively improve system spectral efficiency.
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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 | 0.002 | 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 | 0.001 | 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".