Channel-aware hierarchical graph network for optimizing sliding window RLNC in challenging wireless environments.
Saved in:
| Title: | Channel-aware hierarchical graph network for optimizing sliding window RLNC in challenging wireless environments. |
|---|---|
| Authors: | Akilandeswary, G.1 (AUTHOR) akilandeswaryg@stjosephstechnology.ac.in, Manickam, J. Martin Leo2 (AUTHOR) josephmartin74@gmail.com |
| Source: | EURASIP Journal on Wireless Communications & Networking. 1/20/2026, Vol. 2026 Issue 1, p1-21. 21p. |
| Subjects: | Linear network coding, Graph neural networks, Wireless communications, Network performance, Channel capacity (Telecommunications) |
| Abstract: | Sliding window random linear network coding has emerged as a powerful technique for enhancing the performance of communication networks, particularly in multicast and wireless networks. However, its performance is significantly affected by varying channel conditions such as multipath fading, interference, and noise. To overcome this challenge, novel adaptive channel coefficient engineering is required to dynamically optimize RLNC functionality across varying channel conditions. This paper presents a novel channel-aware hierarchical graph network (CAHGN) that captures the complex interdependencies between various channel features (such as signal-to-noise ratio, path loss, fading factor, interference level, channel magnitude, and phase) and generates an optimized channel coefficient metric to enhance coding performance. The effectiveness of the CAHGN model depends on two key elements: stochastic feature aggregation and dynamic redundancy factor. The stochastic feature aggregation process provides adaptive and weighted channel features by selecting the most relevant features, which are incorporated using the posterior distribution of feature weights. The dynamic redundancy factor controls redundancy levels according to changing feature weights. Simulation results demonstrate that the generated channel coefficient metric improves throughput (by 47.4%), reliability (by 18.2%), and spectral efficiency (by 50%) compared to the traditional RLNC technique under varying wireless channel conditions. Thus, the CAHGN model provides consistent near-optimal performance across different channel environments, reducing recovery time by approximately 60.9%. [ABSTRACT FROM AUTHOR] |
| Copyright of EURASIP Journal on Wireless Communications & Networking is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
