22 research outputs found
Vector Approximate Message Passing With Arbitrary I.I.D. Noise Priors
Approximate message passing (AMP) algorithms are devised under the
Gaussianity assumption of the measurement noise vector. In this work, we relax
this assumption within the vector AMP (VAMP) framework to arbitrary independent
and identically distributed (i.i.d.) noise priors. We do so by rederiving the
linear minimum mean square error (LMMSE) to accommodate both the noise and
signal estimations within the message passing steps of VAMP. Numerical results
demonstrate how our proposed algorithm handles non-Gaussian noise models as
compared to VAMP. This extension to general noise priors enables the use of AMP
algorithms in a wider range of engineering applications where non-Gaussian
noise models are more appropriate.Comment: Accepted to the IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP
Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues
Data-driven machine learning (ML) is promoted as one potential technology to
be used in next-generations wireless systems. This led to a large body of
research work that applies ML techniques to solve problems in different layers
of the wireless transmission link. However, most of these applications rely on
supervised learning which assumes that the source (training) and target (test)
data are independent and identically distributed (i.i.d). This assumption is
often violated in the real world due to domain or distribution shifts between
the source and the target data. Thus, it is important to ensure that these
algorithms generalize to out-of-distribution (OOD) data. In this context,
domain generalization (DG) tackles the OOD-related issues by learning models on
different and distinct source domains/datasets with generalization capabilities
to unseen new domains without additional finetuning. Motivated by the
importance of DG requirements for wireless applications, we present a
comprehensive overview of the recent developments in DG and the different
sources of domain shift. We also summarize the existing DG methods and review
their applications in selected wireless communication problems, and conclude
with insights and open questions
From Multilayer Perceptron to GPT: A Reflection on Deep Learning Research for Wireless Physical Layer
Most research studies on deep learning (DL) applied to the physical layer of
wireless communication do not put forward the critical role of the
accuracy-generalization trade-off in developing and evaluating practical
algorithms. To highlight the disadvantage of this common practice, we revisit a
data decoding example from one of the first papers introducing DL-based
end-to-end wireless communication systems to the research community and
promoting the use of artificial intelligence (AI)/DL for the wireless physical
layer. We then put forward two key trade-offs in designing DL models for
communication, namely, accuracy versus generalization and compression versus
latency. We discuss their relevance in the context of wireless communications
use cases using emerging DL models including large language models (LLMs).
Finally, we summarize our proposed evaluation guidelines to enhance the
research impact of DL on wireless communications. These guidelines are an
attempt to reconcile the empirical nature of DL research with the rigorous
requirement metrics of wireless communications systems