Ergodic sum rate analysis and efficient power allocation for a massive MIMO two‐way relay network
Why this work is in the frame
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Bibliographic record
Abstract
The authors study the transmit power allocation (PA) problem for a network of two multi‐antenna terminals (one of which is a massive multiple‐input and multiple‐output (MIMO) terminal) and a two‐way, amplify‐and‐forward relay. The relay is limited to a single antenna. Using perfect channel state information, the terminals employ beamforming with maximum‐ratio‐transmission and maximum‐ratio‐combining for transmission and reception, respectively. The authors investigate two practical problems, namely; (i) maximising the sum rate subject to a total power constraint (ii) maximising the sum rate when one of the terminals must exceed a target signal‐to‐noise ratio (SNR). For the first case, the authors derive the closed‐form optimal PA and for the second, the authors derive a sub‐optimal PA. In both cases, the resulting sum rates are a function of instantaneous channel gains. Thus by averaging over the Nakagami‐ m distribution and exploiting the weak law of large numbers, the authors derive the closed‐form ergodic sum rates. Finally, the simulation results validate the theoretical analysis and show the sum‐rate improvements over uniform PA. For example, to achieve 4 bit/s/Hz, a uniform allocation needs 1 dB more than the authors’ optimal allocation. When one of the SNRs must exceed a target value, the gap between the authors’ sub‐optimal PA and random PA increases to 2 dB.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it