107 research outputs found
Deep Joint Source-Channel Coding for Wireless Image Transmission with Entropy-Aware Adaptive Rate Control
Adaptive rate control for deep joint source and channel coding (JSCC) is
considered as an effective approach to transmit sufficient information in
scenarios with limited communication resources. We propose a deep JSCC scheme
for wireless image transmission with entropy-aware adaptive rate control, using
a single deep neural network to support multiple rates and automatically adjust
the rate based on the feature maps of the input image and their entropy, as
well as the channel conditions. In particular, we maximize the entropy of the
feature maps to increase the average information carried by each transmitted
symbol during the training. We further decide which feature maps should be
activated based on their entropy, which improves the efficiency of the
transmitted symbols. We also propose a pruning module to remove less important
pixels in the activated feature maps in order to further improve transmission
efficiency. The experimental results demonstrate that our proposed scheme
learns an effective rate control strategy that reduces the required channel
bandwidth while preserving the quality of the reconstructed images
Mean Field Game-based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems
Data collection and processing timely is crucial for mobile crowd integrated
sensing, communication, and computation~(ISCC) systems with various
applications such as smart home and connected cars, which requires numerous
integrated sensing and communication~(ISAC) devices to sense the targets and
offload the data to the base station~(BS) for further processing. However, as
the number of ISAC devices growing, there exists intensive interactions among
ISAC devices in the processes of data collection and processing since they
share the common network resources. In this paper, we consider the environment
sensing problem in the large-scale mobile crowd ISCC systems and propose an
efficient waveform precoding design algorithm based on the mean field
game~(MFG). Specifically, to handle the complex interactions among large-scale
ISAC devices, we first utilize the MFG method to transform the influence from
other ISAC devices into the mean field term and derive the
Fokker-Planck-Kolmogorov equation, which model the evolution of the system
state. Then, we derive the cost function based on the mean field term and
reformulate the waveform precoding design problem. Next, we utilize the G-prox
primal-dual hybrid gradient algorithm to solve the reformulated problem and
analyze the computational complexity of the proposed algorithm. Finally,
simulation results demonstrate that the proposed algorithm can solve the
interactions among large-scale ISAC devices effectively in the ISCC process. In
addition, compared with other baselines, the proposed waveform precoding design
algorithm has advantages in improving communication performance and reducing
cost function.Comment: 13 pages,9 figure
Online Resource Allocation for Semantic-Aware Edge Computing Systems
In this paper, we propose a semantic-aware joint communication and
computation resource allocation framework for MEC systems. In the considered
system, random tasks arrive at each terminal device (TD), which needs to be
computed locally or offloaded to the MEC server. To further release the
transmission burden, each TD sends the small-size extracted semantic
information of tasks to the server instead of the original large-size raw data.
An optimization problem of joint semanticaware division factor, communication
and computation resource management is formulated. The problem aims to minimize
the energy consumption of the whole system, while satisfying longterm delay and
processing rate constraints. To solve this problem, an online low-complexity
algorithm is proposed. In particular, Lyapunov optimization is utilized to
decompose the original coupled long-term problem into a series of decoupled
deterministic problems without requiring the realizations of future task
arrivals and channel gains. Then, the block coordinate descent method and
successive convex approximation algorithm are adopted to solve the current time
slot deterministic problem by observing the current system states. Moreover,
the closed-form optimal solution of each optimization variable is provided.
Simulation results show that the proposed algorithm yields up to 41.8% energy
reduction compared to its counterpart without semantic-aware allocation
Robust mmWave Beamforming by Self-Supervised Hybrid Deep Learning
Beamforming with large-scale antenna arrays has been widely used in recent
years, which is acknowledged as an important part in 5G and incoming 6G. Thus,
various techniques are leveraged to improve its performance, e.g., deep
learning, advanced optimization algorithms, etc. Although its performance in
many previous research scenarios with deep learning is quite attractive,
usually it drops rapidly when the environment or dataset is changed. Therefore,
designing effective beamforming network with strong robustness is an open issue
for the intelligent wireless communications. In this paper, we propose a robust
beamforming self-supervised network, and verify it in two kinds of different
datasets with various scenarios. Simulation results show that the proposed
self-supervised network with hybrid learning performs well in both classic
DeepMIMO and new WAIR-D dataset with the strong robustness under the various
environments. Also, we present the principle to explain the rationality of this
kind of hybrid learning, which is instructive to apply with more kinds of
datasets
Seismic Vulnerability Evaluation of a Three-Span Continuous Beam Railway Bridge
In order to evaluate the seismic vulnerability of a railway bridge, a nonlinear finite element model of typical three-span continuous beam bridge on the Sichuan-Tibet railway in China was built. It further aimed at performing a probabilistic seismic demand analysis based on the seismic performance of the above-mentioned bridge. Firstly, the uncertainties of bridge parameters were analyzed while a set of finite element model samples were formulated with Latin hypercube sampling method. Secondly, under Wenchuan earthquake ground motions, an incremental dynamic method (IDA) analysis was performed, and the seismic peak responses of bridge components were recorded. Thirdly, the probabilistic seismic demand model for the bridge principal components under the prerequisite of two different kinds of bearing, with and without seismic isolation, was generated. Finally, comparison was drawn to further ascertain the effect of two different kinds of bearings on the fragility components. Based on the reliability theory, results were presented concerning the seismic fragility curves
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