Скачать или смотреть Reasoning-Enhanced Neural Networks in Wireless Signal Processing

As wireless environments grow increasingly dynamic and complex, traditional signal processing methods are reaching their limits. In this session, experts explore the urgent need for smarter wireless signal processing, emphasizing the limitations of conventional neural networks, which—while effective at detection and estimation—struggle with generalization, interpretability, and adaptability. Reasoning-Enhanced Neural Networks, a new paradigm that fuses deep learning with reasoning capabilities, is explored as well as hybrid models that can capture causal relationships, enforce logical constraints, and adapt to dynamic environments, enabling a shift from reactive to proactive signal processing.

Featured speakers discuss cutting-edge reasoning techniques—such as graph neural networks (GNNs) for relational reasoning, reinforcement learning (RL) for temporal reasoning, neural-symbolic systems for logical reasoning, and Bayesian neural networks (BNNs) for probabilistic reasoning. Despite their promise, these approaches face significant challenges, including the lack of large-scale reasoning-labeled datasets, high computational costs, and integration hurdles with real-time systems.

Ultimately, the session makes a compelling case for evolving from mere learning to true understanding in wireless signal processing—paving the way for more robust, interpretable, and intelligent communication systems.

Featured Speakers: John Cioffi, Reinaldo Valenzuela, Gerhard Fettweis, Rahim Tafazolli, Khaled Letaief, Chengshan Xiao, Alberto Leon-Garcia, Sherman Shen, Wen Tong, Peiying Zhu, Wei Zhang, Kostas Plataniotis