Joint Position and Beamforming Optimization for Full-Duplex MIMO Systems with Position-Reconfigurable Antennas
Bibliographic record
Abstract
This paper investigates performance improvement of full-duplex (FD) multiple-input multiple-output (MIMO) communication systems through the integration of position reconfigurable antennas (PRAs). Using weighted sum-rate as the evaluation metric, we analyze a system where a base station simultaneously serves both downlink and uplink users, with both transmitters and receivers equipped with PRAs. To maximize the weighted sum-rate, we formulate a highly non-convex optimization problem subject to constraints on the reconfigurable region size, minimum inter-antenna distance, and transmit power. To tackle this challenge, we propose an alternating optimization framework that decomposes the original problem into sub-problems and solves them iteratively. Within this framework, fractional programming techniques are employed to decouple optimization variables from logarithmic and ratio terms, while a block successive upper-bound minimization approach addresses the non-convexity of PRA positioning. Simulation results confirm the performance gain achieved by incorporating PRAs into FD-MIMO systems and demonstrate the advantages of the proposed optimization algorithm.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".