Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence

• URL: http://arxiv.org/abs/2502.15472v1
• Date: Fri, 21 Feb 2025 13:55:41 GMT
• Title: Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence
• Authors: Yufeng Diao, Yichi Zhang, Changyang She, Philip Guodong Zhao, Emma Liying Li,
• Abstract summary: This paper proposes a novel communication framework that aligns reconstruction-oriented and task-oriented communications for edge intelligence.<n>The proposed framework is particularly effective in edge-based autonomous driving scenarios.
• Score: 13.863202685618058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing communication systems aim to reconstruct the information at the receiver side, and are known as reconstruction-oriented communications. This approach often falls short in meeting the real-time, task-specific demands of modern AI-driven applications such as autonomous driving and semantic segmentation. As a new design principle, task-oriented communications have been developed. However, it typically requires joint optimization of encoder, decoder, and modified inference neural networks, resulting in extensive cross-system redesigns and compatibility issues. This paper proposes a novel communication framework that aligns reconstruction-oriented and task-oriented communications for edge intelligence. The idea is to extend the Information Bottleneck (IB) theory to optimize data transmission by minimizing task-relevant loss function, while maintaining the structure of the original data by an information reshaper. Such an approach integrates task-oriented communications with reconstruction-oriented communications, where a variational approach is designed to handle the intractability of mutual information in high-dimensional neural network features. We also introduce a joint source-channel coding (JSCC) modulation scheme compatible with classical modulation techniques, enabling the deployment of AI technologies within existing digital infrastructures. The proposed framework is particularly effective in edge-based autonomous driving scenarios. Our evaluation in the Car Learning to Act (CARLA) simulator demonstrates that the proposed framework significantly reduces bits per service by 99.19% compared to existing methods, such as JPEG, JPEG2000, and BPG, without compromising the effectiveness of task execution.

Related papers

• Semantic Communication based on Generative AI: A New Approach to Image Compression and Edge Optimization [1.450405446885067]
  This thesis integrates semantic communication and generative models for optimized image compression and edge network resource allocation.<n>The communication infrastructure can benefit to significant improvements in bandwidth efficiency and latency reduction.<n>Results demonstrate the potential of combining generative AI and semantic communication to create more efficient semantic-goal-oriented communication networks.
  arXiv  Detail & Related papers  (2025-02-01T21:48:31Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• Integrating Pre-Trained Language Model with Physical Layer Communications [19.20941153929975]
  We introduce a practical ondevice AI communication framework, integrated with physical layer (PHY) communication functions. Our framework incorporates end-to-end training with channel noise to enhance resilience, incorporates vector quantized variational autoencoders (VQ-VAE) for efficient and robust communication, and utilizes pre-trained encoder-decoder transformers for improved generalization capabilities.
  arXiv  Detail & Related papers  (2024-02-18T17:27:51Z)
• RIS-Based On-the-Air Semantic Communications -- a Diffractional Deep Neural Network Approach [10.626169088908867]
  Current AI-based semantic communication methods require digital hardware for implementation. RIS-based semantic communications offer appealing features, such as light-speed computation, low computational power requirements, and the ability to handle multiple tasks simultaneously.
  arXiv  Detail & Related papers  (2023-12-01T12:15:49Z)
• Rate-Adaptive Coding Mechanism for Semantic Communications With Multi-Modal Data [23.597759255020296]
  We propose a distributed multi-modal semantic communication framework incorporating the conventional channel encoder/decoder. We establish a general rate-adaptive coding mechanism for various types of multi-modal semantic tasks. Numerical results show that the proposed mechanism fares better than both conventional communication and existing semantic communication systems.
  arXiv  Detail & Related papers  (2023-05-18T07:31:37Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications [87.05763097471487]
  This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading.
  arXiv  Detail & Related papers  (2022-11-24T20:13:17Z)
• Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach [3.983055670167878]
  A low-end edge device transmits the extracted feature vector of a local data sample to a powerful edge server for processing. It is critical to encode the data into an informative and compact representation for low-latency inference given the limited bandwidth. We propose a learning-based communication scheme that jointly optimize feature extraction, source coding, and channel coding.
  arXiv  Detail & Related papers  (2021-02-08T12:53:32Z)
• Model-Based Machine Learning for Communications [110.47840878388453]
  We review existing strategies for combining model-based algorithms and machine learning from a high level perspective. We focus on symbol detection, which is one of the fundamental tasks of communication receivers.
  arXiv  Detail & Related papers  (2021-01-12T19:55:34Z)
• Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots [58.980293789967575]
  A communication enabled indoor intelligent robots (IRs) service framework is proposed. Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent.
  arXiv  Detail & Related papers  (2020-11-23T21:45:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.