Towards Democratic AI Governance
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Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
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Despite its governance-focused title, this arxiv preprint appears to be a technical paper on signal estimation and wireless communication systems using machine learning, not directly addressing AI safety governance concerns.
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Abstract
This article investigates signal estimation in wireless transmission (i.e., receive combining) from the perspective of statistical machine learning, where the transmit signals may be from an integrated sensing and communication system; that is, 1) signals may be not only discrete constellation points but also arbitrary complex values; 2) signals may be spatially correlated. Particular attention is paid to handling various uncertainties such as the uncertainty of the transmit signal covariance, the uncertainty of the channel matrix, the uncertainty of the channel noise covariance, the existence of channel impulse noises, the non-ideality of the power amplifiers, and the limited sample size of pilots. To proceed, a distributionally robust receive combining framework that is insensitive to the above uncertainties is proposed, which reveals that channel estimation is not a necessary operation. For optimal linear estimation, the proposed framework includes several existing combiners as special cases such as diagonal loading and eigenvalue thresholding. For optimal nonlinear estimation, estimators are limited in reproducing kernel Hilbert spaces and neural network function spaces, and corresponding uncertainty-aware solutions (e.g., kernelized diagonal loading) are derived. In addition, we prove that the ridge and kernel ridge regression methods in machine learning are distributionally robust against diagonal perturbation in feature covariance.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Yoshua Bengio | Person | 39.0 |
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# Distributionally Robust Beamforming and Estimation of Wireless Signals
Shixiong Wang, Wei Dai,
and Geoffrey Ye Li,
S. Wang, W. Dai, and G. Li are with the Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, United Kingdom (E-mail: s.wang@u.nus.edu; wei.dai1@imperial.ac.uk; geoffrey.li@imperial.ac.uk).
This work is supported by the UK Department for Science, Innovation
and Technology under the Future Open Networks Research Challenge project
TUDOR (Towards Ubiquitous 3D Open Resilient Network).
###### Abstract
This paper investigates signal estimation in wireless transmission from the perspective of statistical machine learning, where the transmitted signals may be from an integrated sensing and communication system; that is, 1) signals may be not only discrete constellation points but also arbitrary complex values; 2) signals may be spatially correlated. Particular attention is paid to handling various uncertainties such as the uncertainty of the transmitting signal covariance, the uncertainty of the channel matrix, the uncertainty of the channel noise covariance, the existence of channel impulse noises (i.e., outliers), and the limited sample size of pilots. To proceed, a distributionally robust machine learning framework that is insensitive to the above uncertainties is proposed for beamforming (at the receiver) and estimation of wireless signals, which reveals that channel estimation is not a necessary operation. For optimal linear estimation, the proposed framework includes several existing beamformers as special cases such as diagonal loading and eigenvalue thresholding. For optimal nonlinear estimation, estimators are limited in reproducing kernel Hilbert spaces and neural network function spaces, and corresponding uncertainty-aware solutions (e.g., kernelized diagonal loading) are derived. In addition, we prove that the ridge and kernel ridge regression methods in machine learning are distributionally robust against diagonal perturbation in feature covariance.
###### Index Terms:
Wireless Transmission, Smart Antenna, Machine Learning, Robust Estimation, Robust Beamforming, Distributional Uncertainty, Channel Uncertainty, Limited Sample.
## I Introduction
In wireless transmission, detection and estimation of transmitted signals is of high importance, and beamforming at array receivers serves as a key signal-processing technique to suppress interference and environmental noises. The earliest beamforming solutions rely on the use of phase shifters (e.g., phased arrays) to steer and shape wave lobes, while advanced beamforming methods allow the employment of digital signal processing units, which introduce additional structural freedom (e.g., fully digital, hybrid, nonlinear, wideband) in beamformer design and significant performance improvement in signal recovery \[ [1](https://ar5iv.labs.arxiv.org/html/2401.12345#bib.bib1 ""), [2](https://ar5iv.labs.arxiv.org/html/2401.12345#bib.bib2 ""), [3
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