21 research outputs found
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
Continual Learning-Based MIMO Channel Estimation: A Benchmarking Study
With the proliferation of deep learning techniques for wireless
communication, several works have adopted learning-based approaches to solve
the channel estimation problem. While these methods are usually promoted for
their computational efficiency at inference time, their use is restricted to
specific stationary training settings in terms of communication system
parameters, e.g., signal-to-noise ratio (SNR) and coherence time. Therefore,
the performance of these learning-based solutions will degrade when the models
are tested on different settings than the ones used for training. This
motivates our work in which we investigate continual supervised learning (CL)
to mitigate the shortcomings of the current approaches. In particular, we
design a set of channel estimation tasks wherein we vary different parameters
of the channel model. We focus on Gauss-Markov Rayleigh fading channel
estimation to assess the impact of non-stationarity on performance in terms of
the mean square error (MSE) criterion. We study a selection of state-of-the-art
CL methods and we showcase empirically the importance of catastrophic
forgetting in continuously evolving channel settings. Our results demonstrate
that the CL algorithms can improve the interference performance in two channel
estimation tasks governed by changes in the SNR level and coherence time