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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".