AI and Machine Learning Approaches to Adaptive Signal Processing in Future Wireless Networks
Bibliographic record
Abstract
The rapid expansion of wireless communication networks, driven by the increasing demand for high-speed connectivity and the exponential growth of IoT devices, presents significant challenges to traditional signal processing methods. As Beyond 5G (B5G) and 6G technologies continue to evolve, wireless networks must address issues related to spectrum congestion, dynamic channel conditions, and interference management while maintaining low latency and high energy efficiency. Traditional signal processing approaches struggle to adapt to these dynamic environments, necessitating AI-driven adaptive signal processing frameworks. This study investigates the integration of artificial intelligence (AI) and machine learning (ML) in adaptive signal processing, focusing on Blind Spot Awareness Sensing (BSS), Edge Learning (EL), and Radio Frequency (RF) signal reflection. By using unsupervised learning for blind spectrum sensing, federated learning for distributed optimization, and AI-driven RF reflection techniques for wireless sensing, it is demonstrated that AI models enhance detection precision, optimize spectrum utilization, and improve anti-interference performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".