81 research outputs found
NeurJSCC Enabled Semantic Communications: Paradigms, Applications, and Potentials
Recent advances in deep learning have led to increased interest in solving
high-efficiency end-to-end transmission problems using methods that employ the
nonlinear property of neural networks. These techniques, we call neural joint
source-channel coding (NeurJSCC), extract latent semantic features of the
source signal across space and time, and design corresponding variable-length
NeurJSCC approaches to transmit latent features over wireless communication
channels. Rapid progress has led to numerous research papers, but a
consolidation of the discovered knowledge has not yet emerged. In this article,
we gather diverse ideas to categorize the expansive aspects on NeurJSCC as two
paradigms, i.e., explicit and implicit NeurJSCC. We first focus on those two
paradigms of NeurJSCC by identifying their common and different components in
building end-to-end communication systems. We then focus on typical
applications of NeurJSCC to various communication tasks. Our article highlights
the improved quality, flexibility, and capability brought by NeurJSCC, and we
also point out future directions
WITT: A Wireless Image Transmission Transformer for Semantic Communications
In this paper, we aim to redesign the vision Transformer (ViT) as a new
backbone to realize semantic image transmission, termed wireless image
transmission transformer (WITT). Previous works build upon convolutional neural
networks (CNNs), which are inefficient in capturing global dependencies,
resulting in degraded end-to-end transmission performance especially for
high-resolution images. To tackle this, the proposed WITT employs Swin
Transformers as a more capable backbone to extract long-range information.
Different from ViTs in image classification tasks, WITT is highly optimized for
image transmission while considering the effect of the wireless channel.
Specifically, we propose a spatial modulation module to scale the latent
representations according to channel state information, which enhances the
ability of a single model to deal with various channel conditions. As a result,
extensive experiments verify that our WITT attains better performance for
different image resolutions, distortion metrics, and channel conditions. The
code is available at https://github.com/KeYang8/WITT
Pre-configured Error Pattern Ordered Statistics Decoding for CRC-Polar Codes
In this paper, we propose a pre-configured error pattern ordered statistics
decoding (PEPOSD) algorithm and discuss its application to short cyclic
redundancy check (CRC)-polar codes. Unlike the traditional OSD that changes the
most reliable independent symbols, we regard the decoding process as testing
the error patterns, like guessing random additive noise decoding (GRAND). Also,
the pre-configurator referred from ordered reliability bits (ORB) GRAND can
better control the range and testing order of EPs. Offline-online structure can
accelerate the decoding process. Additionally, we also introduce two orders to
optimize the search order for testing EPs. Compared with CRC-aided OSD and list
decoding, PEPOSD can achieve a better trade-off between accuracy and
complexity
Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding
Classical communication paradigms focus on accurately transmitting bits over
a noisy channel, and Shannon theory provides a fundamental theoretical limit on
the rate of reliable communications. In this approach, bits are treated
equally, and the communication system is oblivious to what meaning these bits
convey or how they would be used. Future communications towards intelligence
and conciseness will predictably play a dominant role, and the proliferation of
connected intelligent agents requires a radical rethinking of coded
transmission paradigm to support the new communication morphology on the
horizon. The recent concept of "semantic communications" offers a promising
research direction. Injecting semantic guidance into the coded transmission
design to achieve semantics-aware communications shows great potential for
further breakthrough in effectiveness and reliability. This article sheds light
on semantics-guided source and channel coding as a transmission paradigm of
semantic communications, which exploits both data semantics diversity and
wireless channel diversity together to boost the whole system performance. We
present the general system architecture and key techniques, and indicate some
open issues on this topic.Comment: IEEE Wireless Communications, text overlap with arXiv:2112.0309
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