11 research outputs found
Robust Semantic Communications with Masked VQ-VAE Enabled Codebook
Although semantic communications have exhibited satisfactory performance for
a large number of tasks, the impact of semantic noise and the robustness of the
systems have not been well investigated. Semantic noise refers to the
misleading between the intended semantic symbols and received ones, thus cause
the failure of tasks. In this paper, we first propose a framework for the
robust end-to-end semantic communication systems to combat the semantic noise.
In particular, we analyze sample-dependent and sample-independent semantic
noise. To combat the semantic noise, the adversarial training with weight
perturbation is developed to incorporate the samples with semantic noise in the
training dataset. Then, we propose to mask a portion of the input, where the
semantic noise appears frequently, and design the masked vector
quantized-variational autoencoder (VQ-VAE) with the noise-related masking
strategy. We use a discrete codebook shared by the transmitter and the receiver
for encoded feature representation. To further improve the system robustness,
we develop a feature importance module (FIM) to suppress the noise-related and
task-unrelated features. Thus, the transmitter simply needs to transmit the
indices of these important task-related features in the codebook. Simulation
results show that the proposed method can be applied in many downstream tasks
and significantly improve the robustness against semantic noise with remarkable
reduction on the transmission overhead.Comment: 16 pages, 11 figures. arXiv admin note: text overlap with
arXiv:2202.0333
A Unified Multi-Task Semantic Communication System for Multimodal Data
Task-oriented semantic communication has achieved significant performance
gains. However, the model has to be updated once the task is changed or
multiple models need to be stored for serving different tasks. To address this
issue, we develop a unified deep learning enabled semantic communication system
(U-DeepSC), where a unified end-to-end framework can serve many different tasks
with multiple modalities. As the difficulty varies from different tasks,
different numbers of neural network layers are required for various tasks. We
develop a multi-exit architecture in U-DeepSC to provide early-exit results for
relatively simple tasks. To reduce the transmission overhead, we design a
unified codebook for feature representation for serving multiple tasks, in
which only the indices of these task-specific features in the codebook are
transmitted. Moreover, we propose a dimension-wise dynamic scheme that can
adjust the number of transmitted indices for different tasks as the number of
required features varies from task to task. Furthermore, our dynamic scheme can
adaptively adjust the numbers of transmitted features under different channel
conditions to optimize the transmission efficiency. According to simulation
results, the proposed U-DeepSC achieves comparable performance to the
task-oriented semantic communication system designed for a specific task but
with significant reduction in both transmission overhead and model size
Research Roadmap of Service Ecosystems: A Crowd Intelligence Perspective
With the mutual interaction and dependence of several intelligent services, a crowd intelligence service network has been formed, and a service ecosystem has gradually emerged. Such a development produces an ever-increasing effect on our lives and the functioning of the whole society. These facts call for research on these phenomena with a new theory or perspective, including what a smart society looks like, how it functions and evolves, and where its boundaries and challenges are. However, the research on service ecosystems is distributed in many disciplines and fields, including computer science, artificial intelligence, complex theory, social network, biological ecosystem, and network economics, and there is still no unified research framework. The researchers always have a restricted view of the research process. Under this context, this paper summarizes the research status and future developments of service ecosystems, including their conceptual origin, evolutionary logic, research topic and scale, challenges, and opportunities. We hope to provide a roadmap for the research in this field and promote sound development
Deep-Unfolding for Next-Generation Transceivers
The stringent performance requirements of future wireless networks, such as
ultra-high data rates, extremely high reliability and low latency, are spurring
worldwide studies on defining the next-generation multiple-input
multiple-output (MIMO) transceivers. For the design of advanced transceivers in
wireless communications, optimization approaches often leading to iterative
algorithms have achieved great success for MIMO transceivers. However, these
algorithms generally require a large number of iterations to converge, which
entails considerable computational complexity and often requires fine-tuning of
various parameters. With the development of deep learning, approximating the
iterative algorithms with deep neural networks (DNNs) can significantly reduce
the computational time. However, DNNs typically lead to black-box solvers,
which requires amounts of data and extensive training time. To further overcome
these challenges, deep-unfolding has emerged which incorporates the benefits of
both deep learning and iterative algorithms, by unfolding the iterative
algorithm into a layer-wise structure analogous to DNNs. In this article, we
first go through the framework of deep-unfolding for transceiver design with
matrix parameters and its recent advancements. Then, some endeavors in applying
deep-unfolding approaches in next-generation advanced transceiver design are
presented. Moreover, some open issues for future research are highlighted.Comment: 16 pages, 6 figure
Robust Semantic Communications Against Semantic Noise
Although the semantic communications have exhibited satisfactory performance
in a large number of tasks, the impact of semantic noise and the robustness of
the systems have not been well investigated. Semantic noise is a particular
kind of noise in semantic communication systems, which refers to the misleading
between the intended semantic symbols and received ones. In this paper, we
first propose a framework for the robust end-to-end semantic communication
systems to combat the semantic noise. Particularly, we analyze the causes of
semantic noise and propose a practical method to generate it. To remove the
effect of semantic noise, adversarial training is proposed to incorporate the
samples with semantic noise in the training dataset. Then, the masked
autoencoder (MAE) is designed as the architecture of a robust semantic
communication system, where a portion of the input is masked. To further
improve the robustness of semantic communication systems, we firstly employ the
vector quantization-variational autoencoder (VQ-VAE) to design a discrete
codebook shared by the transmitter and the receiver for encoded feature
representation. Thus, the transmitter simply needs to transmit the indices of
these features in the codebook. Simulation results show that our proposed
method significantly improves the robustness of semantic communication systems
against semantic noise with significant reduction on the transmission overhead.Comment: 7 pages, 6 figure