Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding

• URL: http://arxiv.org/abs/2211.04339v3
• Date: Wed, 24 May 2023 15:13:21 GMT
• Title: Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding
• Authors: Jincheng Dai, Sixian Wang, Ke Yang, Kailin Tan, Xiaoqi Qin, Zhongwei Si, Kai Niu, Ping Zhang
• Abstract summary: We propose an online learned joint source and channel coding approach that leverages the deep learning model's overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the model or representations to an individual data or channel state instance.
• Score: 11.101344530143303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than the established source and channel coding methods. While, so far, research efforts mainly concentrated on architecture and model improvements toward a static target domain. Despite their successes, such learned models are still suboptimal due to the limitations in model capacity and imperfect optimization and generalization, particularly when the testing data distribution or channel response is different from that adopted for model training, as is likely to be the case in real-world. To tackle this, we propose a novel online learned joint source and channel coding approach that leverages the deep learning model's overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the codec model or representations to an individual data or channel state instance, which can further lead to substantial gains in terms of the bandwidth ratio-distortion performance. The proposed methods enable the communication-efficient adaptation for all parameters in the network without sacrificing decoding speed. Our experiments, including user study, on continually changing target source data and wireless channel environments, demonstrate the effectiveness and efficiency of our approach, on which we outperform existing state-of-the-art engineered transmission scheme (VVC combined with 5G LDPC coded transmission).

Related papers

• Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling [9.20186865054847]
  Anomaly detection (AD) is increasingly recognized as a key component for ensuring the resilience of future communication systems. This work considers AD in network flows using incomplete measurements. We propose a novel block-successive convex approximation algorithm based on a regularized model-fitting objective. Inspired by Bayesian approaches, we extend the model architecture to perform online adaptation to per-flow and per-time-step statistics.
  arXiv  Detail & Related papers  (2024-09-17T19:59:57Z)
• Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence [60.37934652213881]
  Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Active Domain Adaptation (SFADA), where source data becomes inaccessible during adaptation. We present learn from the learnt (LFTL), a novel paradigm for SFADA to leverage the learnt knowledge from the source pretrained model and actively iterated models without extra overhead.
  arXiv  Detail & Related papers  (2024-07-26T17:51:58Z)
• Modeling of Time-varying Wireless Communication Channel with Fading and Shadowing [0.0]
  We propose a new approach that combines a deep learning neural network with a mixture density network model to derive the conditional probability density function of receiving power. Experiments on Nakagami fading channel model and Log-normal shadowing channel model with path loss and noise show that the new approach is more statistically accurate, faster, and more robust than the previous deep learning-based channel models.
  arXiv  Detail & Related papers  (2024-05-13T21:30:50Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Towards a Better Theoretical Understanding of Independent Subnetwork Training [56.24689348875711]
  We take a closer theoretical look at Independent Subnetwork Training (IST) IST is a recently proposed and highly effective technique for solving the aforementioned problems. We identify fundamental differences between IST and alternative approaches, such as distributed methods with compressed communication.
  arXiv  Detail & Related papers  (2023-06-28T18:14:22Z)
• Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme [17.4244108919728]
  To serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding framework for the joint task and data oriented semantic communications. An iterative training algorithm is proposed to tackle the overfitting issue of deep learning models.
  arXiv  Detail & Related papers  (2023-02-27T08:34:37Z)
• Rethinking the Role of Pre-Trained Networks in Source-Free Domain Adaptation [26.481422574715126]
  Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-labeled source domain to an unlabeled target domain. Large-data pre-trained networks are used to initialize source models during source training, and subsequently discarded. We propose to integrate the pre-trained network into the target adaptation process as it has diversified features important for generalization.
  arXiv  Detail & Related papers  (2022-12-15T02:25:22Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• 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)
• Modality Compensation Network: Cross-Modal Adaptation for Action Recognition [77.24983234113957]
  We propose a Modality Compensation Network (MCN) to explore the relationships of different modalities. Our model bridges data from source and auxiliary modalities by a modality adaptation block to achieve adaptive representation learning. Experimental results reveal that MCN outperforms state-of-the-art approaches on four widely-used action recognition benchmarks.
  arXiv  Detail & Related papers  (2020-01-31T04:51:55Z)

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.