Joint optimization of antenna selection and beamforming in MIMO SWIPT systems with bidirectional communication
Bibliographic record
Abstract
Abstract This paper investigates joint optimization of antenna selection and beamforming in multiple‐input multiple‐output simultaneous wireless information and power transfer systems with bidirectional communication. The downlink–uplink rate region is used as the performance metric. The formulated optimization problem is non‐convex mixed integer programming, which is challenging to solve. The problem is first converted into a form of a quadratically constrained quadratic program. Given fixed receive and transmit antenna sets at the energy harvesting device, the authors apply the semidefinite relaxation to obtain a convex problem for optimal beamforming which can be solved efficiently. It is proved that the semidefinite relaxation is tight. The authors solve the relaxed problem for every possible receive and transmit antenna sets and find the optimal solution by search. Moreover, a low‐complexity method to alleviate the computational complexity of the optimal solution by deriving effective heuristic algorithms for the beamforming and antenna selection is proposed. Simulation results demonstrate that both the optimal and the low‐complexity algorithms outperform the benchmark power splitting, when the circuit power consumption is sufficiently high or the number of receive antennas at the energy harvesting device is sufficiently large. Also, a near‐optimal performance can be achieved by the proposed low‐complexity method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".