Unveiling the Power of Complex-Valued Transformers in Wireless Communications
Yang Leng, Qingfeng Lin, Long-Yin Yung, Jingreng Lei, Yang Li, and, Yik-Chung Wu

TL;DR
This paper introduces complex-valued transformers for wireless communications, providing theoretical insights and demonstrating their superior performance over traditional real-valued models in tasks like channel estimation, user detection, and precoding.
Contribution
It offers a theoretical foundation for CVNNs requiring fewer layers and proposes a novel complex-valued transformer architecture tailored for wireless applications.
Findings
Complex-valued transformers outperform real-valued methods in wireless tasks.
Theoretically, CVNNs need fewer layers for the same approximation accuracy.
Experimental results confirm superior performance across multiple applications.
Abstract
Utilizing complex-valued neural networks (CVNNs) in wireless communication tasks has received growing attention for their ability to provide natural and effective representation of complex-valued signals and data. However, existing studies typically employ complex-valued versions of simple neural network architectures. Not only they merely scratch the surface of the extensive range of modern deep learning techniques, theoretical understanding of the superior performance of CVNNs is missing. To this end, this paper aims to fill both the theoretical and practice gap of employing CVNNs in wireless communications. In particular, we provide a comprehensive description on the various operations in CVNNs and theoretically prove that the CVNN requires fewer layers than the real-valued counterpart to achieve a given approximation error of a continuous function. Furthermore, to advance CVNNs in…
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Taxonomy
TopicsAdvanced Research in Systems and Signal Processing · Energy Efficient Wireless Sensor Networks · Network Time Synchronization Technologies
MethodsSoftmax · Attention Is All You Need
