Link adaptation and multi-objective resource optimization in intelligent wireless networks using power-domain non-orthogonal multiple access
Bibliographic record
Abstract
In intelligent wireless networks, achieving reliable communication between vehicles and infrastructure is critical for enhancing user experiences and addressing the demands of next-generation networks. However, maintaining robust connectivity is challenging due to urban environments and network variability in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication systems. This paper proposes a novel framework for link adaptation and multi-objective resource optimization, leveraging power-domain non-orthogonal multiple access (NOMA) and blind reconfigurable intelligent surfaces (IRS). The proposed method incorporates a multi-agent Deep Reinforcement Learning (DRL) model, where each agent dynamically allocates resources by optimizing power control and scheduling based on real-time network data and traffic patterns. Our approach uses IRS to enhance signal quality and extend coverage even in complex and highly dynamic environments, while the multi-agent DRL framework with graph attention mechanisms enables decentralized and scalable resource management. The agents learn from the environment, adjusting resource allocation across multiple objectives, such as maximizing throughput, improving energy efficiency, and ensuring reliable connectivity. By optimizing power allocation and link adaptation, the framework addresses the challenges of channel variability and improves network performance without requiring precise channel state information (CSI). Simulation results show that the proposed approach achieved significant improvements in both energy efficiency and throughput compared to conventional methods such as NFVMCH and HetVNet. Additionally, the throughput of TRONICS scales effectively, reaching nearly 55 Mbps/Hz with 60 users per cluster, while competing methods only manage up to 26 Mbps/Hz, underscoring its advanced resource optimization capabilities.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".