249 research outputs found
SCAN: Semantic Communication with Adaptive Channel Feedback
In existing semantic communication systems for image transmission, some
images are generally reconstructed with considerably low quality. As a result,
the reliable transmission of each image cannot be guaranteed, bringing
significant uncertainty to semantic communication systems. To address this
issue, we propose a novel performance metric to characterize the reliability of
semantic communication systems termed semantic distortion outage probability
(SDOP), which is defined as the probability of the instantaneous distortion
larger than a given target threshold. Then, since the images with lower
reconstruction quality are generally less robust and need to be allocated with
more communication resources, we propose a novel framework of Semantic
Communication with Adaptive chaNnel feedback (SCAN). It can reduce SDOP by
adaptively adjusting the overhead of channel feedback for images with different
reconstruction qualities, thereby enhancing transmission reliability. To
realize SCAN, we first develop a deep learning-enabled semantic communication
system for multiple-input multiple-output (MIMO) channels (DeepSC-MIMO) by
leveraging the channel state information (CSI) and noise variance in the model
design. We then develop a performance evaluator to predict the reconstruction
quality of each image at the transmitter by distilling knowledge from
DeepSC-MIMO. In this way, images with lower predicted reconstruction quality
will be allocated with a longer CSI codeword to guarantee the reconstruction
quality. We perform extensive experiments to demonstrate that the proposed
scheme can significantly improve the reliability of image transmission while
greatly reducing the feedback overhead
Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions
The new demands for high-reliability and ultra-high capacity wireless
communication have led to extensive research into 5G communications. However,
the current communication systems, which were designed on the basis of
conventional communication theories, signficantly restrict further performance
improvements and lead to severe limitations. Recently, the emerging deep
learning techniques have been recognized as a promising tool for handling the
complicated communication systems, and their potential for optimizing wireless
communications has been demonstrated. In this article, we first review the
development of deep learning solutions for 5G communication, and then propose
efficient schemes for deep learning-based 5G scenarios. Specifically, the key
ideas for several important deep learningbased communication methods are
presented along with the research opportunities and challenges. In particular,
novel communication frameworks of non-orthogonal multiple access (NOMA),
massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are
investigated, and their superior performances are demonstrated. We vision that
the appealing deep learning-based wireless physical layer frameworks will bring
a new direction in communication theories and that this work will move us
forward along this road.Comment: Submitted a possible publication to IEEE Wireless Communications
Magazin
Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective
As generative artificial intelligence (GAI) models continue to evolve, their
generative capabilities are increasingly enhanced and being used extensively in
content generation. Beyond this, GAI also excels in data modeling and analysis,
benefitting wireless communication systems. In this article, we investigate
applications of GAI in the physical layer and analyze its support for
integrated sensing and communications (ISAC) systems. Specifically, we first
provide an overview of GAI and ISAC, touching on GAI's potential support across
multiple layers of ISAC. We then concentrate on the physical layer,
investigating GAI's applications from various perspectives thoroughly, such as
channel estimation, and demonstrate the value of these GAI-enhanced physical
layer technologies for ISAC systems. In the case study, the proposed diffusion
model-based method effectively estimates the signal direction of arrival under
the near-field condition based on the uniform linear array, when antenna
spacing surpassing half the wavelength. With a mean square error of 1.03
degrees, it confirms GAI's support for the physical layer in near-field sensing
and communications
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