Secure and Trustful Cross-domain Communication with Decentralized Identifiers in 5G and Beyond
- URL: http://arxiv.org/abs/2509.05797v1
- Date: Sat, 06 Sep 2025 18:20:01 GMT
- Title: Secure and Trustful Cross-domain Communication with Decentralized Identifiers in 5G and Beyond
- Abstract summary: This paper proposes the application of W3C-endorsed Decentralized Identifiers (DIDs) to establish secure and trustful communication channels.<n>A new communication agent is introduced that integrates seamlessly with 5G-standardized network functions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the evolving landscape of future mobile networks, there is a critical need for secure and trustful communication modalities to support dynamic interactions among core network components of different network domains. This paper proposes the application of W3C-endorsed Decentralized Identifiers (DIDs) to establish secure and trustful communication channels among network functions in 5G and subsequent generations. A new communication agent is introduced that integrates seamlessly with 5G-standardized network functions and utilizes a DID-based application layer transport protocol to ensure confidentiality, integrity, and authenticity for cross-domain interactions. A comparative analysis of the two different versions of the DID-based communication protocol for inter network function communication reveals compatibility advantages of the latest protocol iteration. Furthermore, a comprehensive evaluation of the communication overhead caused by both protocol iterations compared to traditional TCP/TLS shows the benefits of using DIDs to improve communication security, albeit with performance loses compared to TCP/TLS. These results uncover the potential of DID-based communication for future mobile networks but also point out areas for optimization.
Related papers
- SPAT: A Semantic Port-Aware Adaptive-Rate Transmission Protocol for Semantic Communication [56.844498058243744]
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.
arXiv Detail & Related papers (2026-04-28T03:32:50Z) - Semantic Communication-Enhanced Split Federated Learning for Vehicular Networks: Architecture, Challenges, and Case Study [50.345531105285524]
Vehicular edge intelligence (VEI) is vital for future intelligent transportation systems.<n>Traditional centralized learning in dynamic vehicular networks faces significant communication overhead and privacy risks.<n>This paper presents a semantic communication-enhanced split federated learning (SC-USFL) framework.
arXiv Detail & Related papers (2026-03-05T08:36:49Z) - Token Communications: A Large Model-Driven Framework for Cross-modal Context-aware Semantic Communications [78.80966346820553]
We introduce token communications (TokCom), a large model-driven framework to leverage cross-modal context information in generative semantic communications (GenSC)<n>In this paper, we introduce the potential opportunities and challenges of leveraging context in GenSC, explore how to integrate GFM/MLLMs-based token processing into semantic communication systems, present the key principles for efficient TokCom at various layers in future wireless networks.
arXiv Detail & Related papers (2025-02-17T18:14:18Z) - Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study [62.5909195375364]
Cross-layer covert communication mechanism emerges as an effective strategy to mitigate regulatory challenges.<n>We propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI)<n>Case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication.
arXiv Detail & Related papers (2025-01-19T15:05:03Z) - Multi-level Reliability Interface for Semantic Communications over Wireless Networks [5.9056146376982]
Joint source-channel coding (JSCC) maps source messages directly into channel input symbols.
We propose designing the source and channel mappings separately and sequentially via a novel multi-level reliability interface.
This work represents an important step towards realizing semantic communications in wireless networks.
arXiv Detail & Related papers (2024-07-07T20:15:10Z) - Will 6G be Semantic Communications? Opportunities and Challenges from
Task Oriented and Secure Communications to Integrated Sensing [49.83882366499547]
This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) networks through the integration of multi-task learning.
We employ deep neural networks representing a dedicated encoder at the transmitter and multiple task-specific decoders at the receiver.
We scrutinize potential vulnerabilities stemming from adversarial attacks during both training and testing phases.
arXiv Detail & Related papers (2024-01-03T04:01:20Z) - Communication-Efficient Federated Optimization over Semi-Decentralized Networks [35.6514238987893]
Communication efficiency is one of the most challenging bottlenecks in large-scale networks.<n>We study the communication efficiency under semi-decentralized communication protocol, in which agents can perform both agent-to-agent and agent-to-server communication.
arXiv Detail & Related papers (2023-11-30T18:37:15Z) - Multi-Agent Reinforcement Learning Based on Representational
Communication for Large-Scale Traffic Signal Control [13.844458247041711]
Traffic signal control (TSC) is a challenging problem within intelligent transportation systems.
We propose a communication-based MARL framework for large-scale TSC.
Our framework allows each agent to learn a communication policy that dictates "which" part of the message is sent "to whom"
arXiv Detail & Related papers (2023-10-03T21:06:51Z) - Learning Practical Communication Strategies in Cooperative Multi-Agent
Reinforcement Learning [5.539117319607963]
Communication in realistic wireless networks can be highly unreliable due to network conditions varying with agents' mobility.
We propose a framework to learn practical communication strategies by addressing three fundamental questions.
We show significant improvements in game performance, convergence speed and communication efficiency compared with state-of-the-art.
arXiv Detail & Related papers (2022-09-02T22:18:43Z) - A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G:
Integrating Domain Knowledge into Deep Learning [115.75967665222635]
Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.
Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks.
This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
arXiv Detail & Related papers (2020-09-13T14:53:01Z) - Communication-Efficient and Distributed Learning Over Wireless Networks:
Principles and Applications [55.65768284748698]
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond.
This article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases.
arXiv Detail & Related papers (2020-08-06T12:37:14Z)
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.