Optimized MIMO transmission and compression for interference mitigation with cooperative relay
Bibliographic record
Abstract
This paper considers a novel use of device-to-device link for cooperative communication wherein a nearby user terminal acts as a relay in enabling both signal enhancement and common interference rejection at the intended destination. Assuming Gaussian transmission and Gaussian compress-and-forward relaying strategy for the multiple-input multiple-output (MIMO) relay channel with a finite-capacity out-of-band relay-destination link and with arbitrarily correlated noises, this paper proposes a coordinate ascent approach for iteratively optimizing the transmit covariance matrix at the source and the quantization noise covariance matrix at the relay. We show that the optimization of quantization noise covariance matrix under fixed input can be solved in closed form using a simultaneous diagonalization approach, while the optimization of transmit covariance matrix under fixed quantization can be cast as a convex optimization problem. This paper further introduces the concept of antenna pooling and illustrates the importance of accounting for the noise correlation across the user terminals due to common interference. We show that the optimized transmission and device-to-device relaying strategies that take advantage of the noise correlation can significantly improve the user throughput in a cellular environment by enabling interference rejection across the user terminals.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".