Advancing Rate Optimization in Full-Duplex MIMO Systems: Addressing Ricean Fading and Hardware Imperfections
Bibliographic record
Abstract
Full-duplex (FD) communication is becoming a key technology for future wireless systems, offering the potential to significantly improve spectral efficiency (SE) and overall system performance. This study explores how hardware impairments (HWIs) impact the performance of FD multiple-input multiple-output (MIMO) systems. We develop models to evaluate the achievable data rates for both uplink (UL) and downlink (DL) communications in the presence of hardware imperfections. Additionally, we address the challenge of optimizing power allocation to maximize FD SE using the Karush-Kuhn-Tucker (KKT) conditions. Our approach not only boosts system performance but also compensates for HWIs, even outperforming ideal systems operating at maximum power. Finally, extensive simulations validate our theoretical findings.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".