7 research outputs found
CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation
Deep learning has been extensively used in wireless communication problems,
including channel estimation. Although several data-driven approaches exist, a
fair and realistic comparison between them is difficult due to inconsistencies
in the experimental conditions and the lack of a standardized experimental
design. In addition, the performance of data-driven approaches is often
compared based on empirical analysis. The lack of reproducibility and
availability of standardized evaluation tools (e.g., datasets, codebases)
hinder the development and progress of data-driven methods for channel
estimation and wireless communication in general. In this work, we introduce an
initiative to build benchmarks that unify several data-driven OFDM channel
estimation approaches. Specifically, we present CeBed (a testbed for channel
estimation) including different datasets covering various systems models and
propagation conditions along with the implementation of ten deep and
traditional baselines. This benchmark considers different practical aspects
such as the robustness of the data-driven models, the number and the
arrangement of pilots, and the number of receive antennas. This work offers a
comprehensive and unified framework to help researchers evaluate and design
data-driven channel estimation algorithms
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
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
Mixed-Variable PSO with Fairness on Multi-Objective Field Data Replication in Wireless Networks
Digital twins have shown a great potential in supporting the development of
wireless networks. They are virtual representations of 5G/6G systems enabling
the design of machine learning and optimization-based techniques. Field data
replication is one of the critical aspects of building a simulation-based twin,
where the objective is to calibrate the simulation to match field performance
measurements. Since wireless networks involve a variety of key performance
indicators (KPIs), the replication process becomes a multi-objective
optimization problem in which the purpose is to minimize the error between the
simulated and field data KPIs. Unlike previous works, we focus on designing a
data-driven search method to calibrate the simulator and achieve accurate and
reliable reproduction of field performance. This work proposes a search-based
algorithm based on mixedvariable particle swarm optimization (PSO) to find the
optimal simulation parameters. Furthermore, we extend this solution to account
for potential conflicts between the KPIs using {\alpha}-fairness concept to
adjust the importance attributed to each KPI during the search. Experiments on
field data showcase the effectiveness of our approach to (i) improve the
accuracy of the replication, (ii) enhance the fairness between the different
KPIs, and (iii) guarantee faster convergence compared to other methods.Comment: Accepted in International Conference on Communications (ICC) 202