Joint AP Scheduling and Precoding in RIS-Aided Distributed MIMO Networks: A Hierarchical DRL Framework
Bibliographic record
Abstract
With the increasing demands for spectral and energy efficiency, distributed multiple-input multiple-output (MIMO) networks assisted with reconfigurable intelligent surfaces (RISs) have attracted considerable attention. In this paper, we investigate the joint optimization of access point (AP) scheduling and precoding for RIS-aided distributed MIMO networks. To make this challenging problem more tractable, we decouple the joint optimization into two hierarchical subproblems, which eventually formulate a two-timescale scheme. To further reduce the computational complexity of joint precoding, we propose a hierarchical deep reinforcement learning (HDRL) framework to maximize the system sum spectral efficiency (SE), which leads to a distributed deployment with centralized training. Simulation results show that the proposed framework yields significant reduction in the computational complexity with slight performance loss, and strong generalizability against varying system parameters.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".