Leveraging Deep Unsupervised Learning for Joint Passive Beamforming and Power Control of RIS-Aided D2D Networks with Energy Constrained Nodes
Bibliographic record
Abstract
In this work, we investigate sum-rate for the reconfigurable intelligent surface (RIS)-aided device-to-device (D2D) networks with energy constrained wireless nodes. Specifically, we aim to maximize the sum-rate for the RIS-aided D2D networks while meeting minimum energy requirements of the energy constrained wireless nodes. To this end, we formulate a problem that involves joint optimization of phase-shifts of reflecting elements of the RIS (passive beamforming) and transmit power control of each transmitter. Nonetheless, this optimization problem is a high-dimensional non-convex, thus finding its optimal solution is significantly challenging. In order to solve this optimization problem efficiently, we propose a novel data-driven scheme that is based on the deep unsupervised learning (DUL) mechanism. It is shown through simulations that our proposed data-driven scheme achieves a sum-rate comparable to that of the existing benchmark scheme, yet our scheme notably excels in efficiency, i.e., demonstrating a time-complexity which is at-least ten times lower compared to the existing benchmark.
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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 | 0.000 |
| 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".