114 research outputs found
Full-State Linearization and Stabilization of SISO Markovian Jump Nonlinear Systems
This paper investigates the linearization and stabilizing control design problems for a class of SISO Markovian jump nonlinear systems. According to the proposed relative degree set definition, the system can be transformed into the canonical form through the appropriate coordinate changes followed with the Markovian switchings; that is, the system can be full-state linearized in every jump mode with respect to the relative degree set n,…,n. Then, a stabilizing control is designed through applying the backstepping technique, which guarantees the asymptotic stability of Markovian jump nonlinear systems. A numerical example is presented to illustrate the effectiveness of our results
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
Improved Nonlinear Transform Source-Channel Coding to Catalyze Semantic Communications
Recent deep learning methods have led to increased interest in solving
high-efficiency end-to-end transmission problems. These methods, we call
nonlinear transform source-channel coding (NTSCC), extract the semantic latent
features of source signal, and learn entropy model to guide the joint
source-channel coding with variable rate to transmit latent features over
wireless channels. In this paper, we propose a comprehensive framework for
improving NTSCC, thereby higher system coding gain, better model versatility,
and more flexible adaptation strategy aligned with semantic guidance are all
achieved. This new sophisticated NTSCC model is now ready to support large-size
data interaction in emerging XR, which catalyzes the application of semantic
communications. Specifically, we propose three useful improvement approaches.
First, we introduce a contextual entropy model to better capture the spatial
correlations among the semantic latent features, thereby more accurate rate
allocation and contextual joint source-channel coding are developed accordingly
to enable higher coding gain. On that basis, we further propose response
network architectures to formulate versatile NTSCC, i.e., once-trained model
supports various rates and channel states that benefits the practical
deployment. Following this, we propose an online latent feature editing method
to enable more flexible coding rate control aligned with some specific semantic
guidance. By comprehensively applying the above three improvement methods for
NTSCC, a deployment-friendly semantic coded transmission system stands out
finally. Our improved NTSCC system has been experimentally verified to achieve
considerable bandwidth saving versus the state-of-the-art engineered VTM + 5G
LDPC coded transmission system with lower processing latency
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