Joint robust beamforming design for WPT-assisted D2D communications in MISO-NOMA: Fractional programming and deep reinforcement learning
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Bibliographic record
Abstract
This paper introduces a framework aimed at aiding the development of sixth-generation (6G) ultra-massive machine type communications (um-MTC). Precisely, the deployment of wireless power transfer (WPT) supported device-to-device (D2D) communication occurs within multiple-input single-output non-orthogonal multiple access (MISONOMA) downlink networks to facilitate spectrum and energy collaboration. A pure fractional programming (PFP) algorithm is proposed to maximize the WPT-assisted device's energy efficiency. An optimal closed-form solution for determining the time-switching coefficient of the WPT device is provided. For the robust beamforming design, the complex multi-dimension quadratic transform is applied. Moreover, the paper applies the deep deterministic policy gradient (DDPG)-based approach to directly address the problem and compares it with the proposed algorithm. Simulation outcomes highlight two key insights: 1) The PFP algorithm surpasses the performance of the DRL-based algorithm when the acquired channel state information (CSI) is accurate or contains negligible errors, while the opposite is true for imperfect CSI 2) The higher energy efficiency gains can be achieved in NOMA scheme than that in Orthogonal Multiple Access (OMA) scheme.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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 it