Sum Power Minimization for TDD-Enabled Full-Duplex Bi-Directional MIMO Systems Under Channel Uncertainty
Bibliographic record
Abstract
In this paper we address a sum power minimization problem for a bi-directional and full-duplex (FD) communication system, where the required rate constraints are imposed on the guaranteed communication rates in each direction. In this regard, the impact of channel-state information (CSI) error, as well as the signal distortion due to hardware impairments are jointly taken into account. In order to ensure backwards compatibility to an equivalent half-duplex setup, we assume a time-division-duplex capable system where the FD communication process takes place in multiple independent time segments. Due to the intractable structure of the resulting optimization problem, a weighted minimum mean squared-error based method is applied to cast the power minimization problem into a separately convex structure, which can be iteratively solved with a guaranteed convergence. The resulting computational complexity of the algorithm is then discussed analytically. Finally, the performance of the proposed algorithm is numerically evaluated over different levels of rate demand, CSI error and transmitter/receiver dynamic range.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".