SPAT: A Semantic Port-Aware Adaptive-Rate Transmission Protocol for Semantic Communication
- URL: http://arxiv.org/abs/2605.00897v1
- Date: Tue, 28 Apr 2026 03:32:50 GMT
- Title: SPAT: A Semantic Port-Aware Adaptive-Rate Transmission Protocol for Semantic Communication
- Abstract summary: 6G semantic communication has emerged as a promising paradigm by prioritizing the delivery of task-relevant meaning over strict bit-level correctness.<n>Existing transport mechanisms still rely on explicit port headers and bit-level validation, making them vulnerable to header corruption and the resulting packet loss.<n>This paper proposes a Semantic Port-Aware Adaptive-Rate Transmission Protocol (SPAT) for semantic communication.
- Score: 56.844498058243744
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the evolution of 6G, semantic communication has emerged as a promising paradigm by prioritizing the delivery of task-relevant meaning over strict bit-level correctness. However, existing transport mechanisms still rely on explicit port headers and bit-level validation, making them vulnerable to header corruption and the resulting packet loss. To address this issue, this paper proposes a Semantic Port-Aware Adaptive-Rate Transmission Protocol (SPAT) for semantic communication. The proposed framework jointly embeds source and destination port information into semantic representations, thereby reducing dependence on explicit port headers while enabling robust port-aware transmission. Furthermore, a differentiated semantic processing mechanism is developed for uplink and downlink scenarios, where port identification is introduced for uplink service recognition and destination-aware conditional gating is designed for downlink selective decoding. In addition, an adaptive-rate controller is incorporated to dynamically adjust the number of transmitted semantic channels according to channel conditions and feature importance, thereby improving both robustness and transmission efficiency. Experimental results on the AFHQ and ImageNet-10 datasets, together with real-world experimental measurements, demonstrate that SPAT consistently outperforms TCP, UDP, and SITP in reconstruction quality across different SNRs while maintaining low-latency transmission.
Related papers
- A VAE-Driven Multi-Task Satellite-Aided Semantic Communication Framework for 6G-Enabled Connected Autonomous Vehicles [51.449502491030756]
We present a multi-task semantic communication framework for satellite-assisted autonomous driving.<n>The framework is trained end-to-end to jointly optimize both tasks.<n>Results show that the proposed approach achieves significant bandwidth reduction of up to 87.23% to 98.17%.
arXiv Detail & Related papers (2026-07-15T06:49:41Z) - Adaptive Cooperative Transmission Design for Ultra-Reliable Low-Latency Communications via Deep Reinforcement Learning [2.665411186169543]
Next-generation wireless communication systems must support ultra-reliable low-latency communication (URLLC) service for mission-critical applications.<n>Meeting stringent URLLC requirements is challenging, especially for two-hop cooperative communication.<n>We develop an adaptive transmission design for a two-hop relaying communication system.
arXiv Detail & Related papers (2025-11-04T03:08:59Z) - Distributionally Robust Wireless Semantic Communication with Large AI Models [111.47794569742206]
Current SemCom systems fail to generalize across diverse noise conditions, adversarial attacks, and out-of-distribution data.<n>Wasserstein distributionally robust optimization is employed to provide resilience against semantic misinterpretation and channel perturbations.<n> Experimental results on image and text transmission demonstrate that WaSeCom achieves improved robustness under noise and adversarial perturbations.
arXiv Detail & Related papers (2025-05-28T04:03:57Z) - Adaptive Semantic Token Communication for Transformer-based Edge Inference [15.405730528104113]
This paper presents an adaptive framework for edge inference based on a dynamically transformer-powered deep joint source channel coding architecture.<n>We employ a semantic token selection mechanism that adaptively compresses informative features into a user specified number of tokens per sample.<n>We incorporate a resource allocation algorithm based on Lyapunov optimization to enhance robustness under dynamic network conditions.
arXiv Detail & Related papers (2025-05-23T08:15:05Z) - Task-Adaptive Semantic Communications with Controllable Diffusion-based Data Regeneration [45.55410059471241]
Next-generation networking shifts bit-wise data delivery to conveying semantic meanings for bandwidth efficiency.<n>This work presents a novel task-adaptive semantic communication framework based on diffusion models.<n>Test results demonstrate the efficacy of the proposed method in adaptively preserving task-relevant information for semantic communications.
arXiv Detail & Related papers (2025-05-12T18:23:53Z) - Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks.<n>Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance.<n>We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
arXiv Detail & Related papers (2025-01-20T04:26:21Z) - Reasoning with the Theory of Mind for Pragmatic Semantic Communication [62.87895431431273]
A pragmatic semantic communication framework is proposed in this paper.
It enables effective goal-oriented information sharing between two-intelligent agents.
Numerical evaluations demonstrate the framework's ability to achieve efficient communication with a reduced amount of bits.
arXiv Detail & Related papers (2023-11-30T03:36:19Z) - Communication-Efficient Framework for Distributed Image Semantic
Wireless Transmission [68.69108124451263]
Federated learning-based semantic communication (FLSC) framework for multi-task distributed image transmission with IoT devices.
Each link is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator.
Channel state information-based multiple-input multiple-output transmission module designed to combat channel fading and noise.
arXiv Detail & Related papers (2023-08-07T16:32:14Z) - Robust Information Bottleneck for Task-Oriented Communication with
Digital Modulation [31.39386509261528]
Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system.
We develop a robust encoding framework, named robust information bottleneck (RIB), to improve the communication robustness to the channel variations.
The proposed DT-JSCC achieves better inference performance than the baseline methods with low communication latency.
arXiv Detail & Related papers (2022-09-21T14:21:14Z) - 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)
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.