Joint User Scheduling, Phase Shift Control, and Beamforming Optimization in Intelligent Reflecting Surface-Aided Systems
Bibliographic record
Abstract
In this paper, we formulate a joint uplink scheduling, phase shift control, and beamforming optimization problem in intelligent reflecting surface (IRS)-aided systems. We consider maximizing the aggregate throughput and achieving the proportional fairness as objectives. We propose a deep reinforcement learning-based user scheduling, phase shift control, beamforming optimization (DUPB) algorithm to solve the joint problem. The proposed DUPB algorithm applies the neural combinatorial optimization (NCO) technique to solve the user scheduling subproblem, in which a stochastic user scheduling policy is learned by deep neural networks with attention mechanism. Curriculum learning with deep deterministic policy gradient (CL-DDPG) is used in the proposed DUPB algorithm to jointly optimize the phase shift control and beamforming vectors. The knowledge on the hidden convexity of the joint problem is exploited to facilitate the policy learning in CL-DDPG. Simulation results show that, with the maximum aggregate throughput as the objective, the proposed DUPB algorithm achieves an aggregate throughput that is higher than the alternating optimization (AO)-based algorithms. Moreover, the throughput fairness among the users is improved when proportional fairness is used as the objective. The proposed DUPB algorithm outperforms the AO-based algorithms in terms of runtime when the number of reflecting elements is large.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".