DRL-Powered Sum-Rate Maximization Design for IRS-Assisted Full Duplex Cluster NOMA WPCNs
Bibliographic record
Abstract
This paper tackles the complex problem of maximizing the ergodic sum-rate in intelligent reflecting surface (IRS)-assisted full-duplex (FD) cluster non-orthogonal multiple access wireless-powered communication networks. The primary challenge lies in the joint optimization of hybrid access point (HAP) beamforming vectors, IRS phase shifts, and uplink power allocation for sensors, while considering practical constraints— including channel uncertainties, a nonlinear energy harvesting model, self-interference at both the HAP and sensors due to FD transmission, the rank-one constraint of the IRS, and the limited buffer capacity of the sensors. These factors result in an NP-hard optimization problem that conventional approaches cannot solve efficiently. To address this, we propose utilizing a deep reinforcement learning framework based on the twin delayed deep deterministic policy gradient (TD3) algorithm. Our results show that TD3 improves the system’s average sum-rate by up to 12% and 48% over the deep deterministic policy gradient (DDPG) and random allocation benchmarks, respectively. The results also show that both TD3 and DDPG have similar computational complexity. Thus, TD3 achieves a favorable balance between performance and computational complexity, making it a scalable and practical solution for next-generation networks where spectrum efficiency and throughput maximization are critical in hyper-dense environments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".