11,965 research outputs found
Deep Joint Source-Channel Coding for Adaptive Image Transmission over MIMO Channels
This paper introduces a vision transformer (ViT)-based deep joint source and
channel coding (DeepJSCC) scheme for wireless image transmission over
multiple-input multiple-output (MIMO) channels, denoted as DeepJSCC-MIMO. We
consider DeepJSCC-MIMO for adaptive image transmission in both open-loop and
closed-loop MIMO systems. The novel DeepJSCC-MIMO architecture surpasses the
classical separation-based benchmarks with robustness to channel estimation
errors and showcases remarkable flexibility in adapting to diverse channel
conditions and antenna numbers without requiring retraining. Specifically, by
harnessing the self-attention mechanism of ViT, DeepJSCC-MIMO intelligently
learns feature mapping and power allocation strategies tailored to the unique
characteristics of the source image and prevailing channel conditions.
Extensive numerical experiments validate the significant improvements in
transmission quality achieved by DeepJSCC-MIMO for both open-loop and
closed-loop MIMO systems across a wide range of scenarios. Moreover,
DeepJSCC-MIMO exhibits robustness to varying channel conditions, channel
estimation errors, and different antenna numbers, making it an appealing
solution for emerging semantic communication systems.Comment: arXiv admin note: text overlap with arXiv:2210.1534
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
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