Hybridized MA-DRL for Serving xURLLC With Cognizable RIS and UAV Integration
Bibliographic record
Abstract
This work proposes a new model of reconfigurable intelligent surface (RIS) called cognizable RIS (CRIS) that is specifically designed to meet the unique demands of users who require extreme-ultra-reliable and low-latency Communication (xURLLC) in the sixth generation (6G) wireless networks. The programmable elements in the proposed CRIS unit can adapt to different modes of operation to provide significant performance gain. To improve reliability at the receiver, we integrate unmanned aerial vehicles with the CRIS module, which enhances network performance through beamforming and mobility. Our study focuses on maximizing the sum throughput in a multiple-input multiple-output scenario using the rate-splitting multiple access communication system. To achieve this, we introduce a novel hybridized multi-agent-based deep reinforcement learning (DRL) algorithm for optimal resource allocation that maximizes the sum throughput. We incorporate long-short-term memory (LSTM) networks into our proposed DRL to address the temporal dependencies due to stochastic channel conditions. By utilizing the proposed LSTM-based multi-agent DRL (MA-DRL) algorithm, we achieve notable gains of 11.7% and 26.9% in sum throughput over widely recognized DRL benchmark algorithms, all while adhering to xURLLC’s stringent maximum packet error probability constraint of 10−9.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".