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

Alleviating Distortion Accumulation in Multi-Hop Semantic Communication

Recently, semantic communication has been investigated to boost the performance of end-to-end image transmission systems. However, existing semantic approaches are generally based on deep learning and belong to lossy transmission. Consequently, as the receiver continues to transmit received images to another device, the distortion of images accumulates with each transmission. Unfortunately, most recent advances overlook this issue and only consider single-hop scenarios, where images are transmitted only once from a transmitter to a receiver. In this letter, we propose a novel framework of a multi-hop semantic communication system. To address the problem of distortion accumulation, we introduce a novel recursive training method for the encoder and decoder of semantic communication systems. Specifically, the received images are recursively input into the encoder and decoder to retrain the semantic communication system. This empowers the system to handle distorted received images and achieve higher performance. Our extensive simulation results demonstrate that the proposed methods significantly alleviate distortion accumulation in multi-hop semantic communication

Thermal conductivity of MgO in giant planetary interior conditions predicted by deep potential

Thermal conductivity $\kappa$ of MgO plays a fundamental role in understanding the thermal evolution and mantle convection in the interior of terrestrial planets. However, previous theoretical calculations deviate from each other and the $\kappa$ of high-pressure B2 phase remains undetermined. Here, by combining molecular dynamics and deep potential trained with first-principles data, we systematically investigate the $\kappa$ of MgO from ambient state to the core-mantle boundary (CMB) of super-Earth with $5M_{\oplus}$. We point out the significance of 4-phonon scatterings and modify the conventional thermal conductivity model of MgO by considering the density-dependent proportion of 3-phonon and 4-phonon scatterings. The $\kappa$ profiles of MgO in Earth and super-Earth are further estimated. For super-Earth, we predict a significant reduction of $\kappa$ at the B1-B2 phase transition area near the CMB. This work provides new insights into thermal transport under extreme conditions and an improved thermal model for terrestrial planets.Comment: 4 figure

Anomalous thermal transport across the superionic transition in ice

Superionic ices with highly mobile protons within the stable oxygen sub-lattice occupy an important proportion of the phase diagram of ice and widely exist in the interior of icy giants and throughout the universe. Understanding the thermal transport in superionic ice is vital for the thermal evolution of icy planets. However, it is highly challenging due to the extreme thermodynamic conditions and dynamical nature of protons, beyond the capability of the traditional lattice dynamics and empirical potential molecular dynamics approaches. In this work, by utilizing the deep potential molecular dynamics approach, we investigate the thermal conductivity of ice-VII and superionic ice-VII" along the isobar of $p = 30\ \rm{GPa}$. A non-monotonic trend of thermal conductivity with elevated temperature is observed. Through heat flux decomposition and trajectory-based spectra analysis, we show that the thermally-activated proton diffusion in ice-VII and superionic ice-VII" contribute significantly to heat convection, while the broadening in vibrational energy peaks and significant softening of transverse acoustic branches lead to a reduction in heat conduction. The competition between proton diffusion and phonon scattering results in anomalous thermal transport across the superionic transition in ice. This work unravels the important role of proton diffusion in the thermal transport of high-pressure ice. Our approach provides new insights into modeling the thermal transport and atomistic dynamics in superionic materials.Comment: 5 figure

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

Three-step Formation of Diamonds in Shock-compressed Hydrocarbons: Decomposition, Species Separation, and Nucleation

The accumulation and circulation of carbon-hydrogen dictate the chemical evolution of ice giant planets. Species separation and diamond precipitation have been reported in carbon-hydrogen systems, verified by static and shock-compression experiments. Nevertheless, the dynamic formation processes for the above-mentioned phenomena are still insufficiently understood. Here, combing deep learning model, we demonstrate that diamonds form through a three-step process involving decomposition, species separation and nucleation procedures. Under shock condition of 125 GPa and 4590 K, hydrocarbons are decomposed to give hydrogen and low-molecular-weight alkanes (CH4 and C2H6), which escape from the carbon chains resulting in C/H species separation. The remaining carbon atoms without C-H bonds accumulate and nucleate to form diamond crystals. The process of diamond growth is found to associated with a critical nucleus size where dynamic energy barrier plays a key role. These dynamic processes for diamonds formation are insightful in establishing the model for ice giant planet evolution.Comment: 5 figure

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