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
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a leading science technology engineering and top consultancy in the field of nth
Generation Fixed or Mobile Wireless Systems and Networks.
TACS scientists engineers and consultants are the first inventors of fixed or mobile
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TACS
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