A Learned-PPR Decoding Scheme for Partial Packet Recovery in Network Coding

doi:10.1109/JIOT.2026.3667346

A Learned-PPR Decoding Scheme for Partial Packet Recovery in Network Coding

2026-02-23

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DOI：10.1109/JIOT.2026.3667346

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Network coding (NC) has proven to offer significant benefits in long-distance and broadcast transmissions, enhancing both throughput and energy efficiency. Recent studies have incorporated partial packet recovery (PPR) into packet-level NC, using syndromes from coded packets to correct bit errors and thereby reduce completion delay. Motivated by recent breakthroughs in deep learning, this article introduces a novel neural network-based decoding framework for packet-level NC, referred to as Learned-PPR. The proposed framework incorporates a bilateral efficient self-attention network (Bi-ESANet) architecture, which leverages a bilateral network structure to effectively capture both inter and intrapacket information. Furthermore, we introduce an ESA module to mitigate the GPU memory overhead compared with traditional Transformer attention modules. To handle rateless NC, we propose a “rateless masking” training strategy that enables efficient decoding of rateless codes within the Bi-ESANet framework. Simulation results across various transmission scenarios demonstrate that the proposed approach significantly outperforms existing PPR schemes, achieving lower completion delay. Specifically, compared to existing methods, the proposed approach reduces completion delay by more than 25%. However, the introduced framework incurs higher computational complexity due to the integration of the Bi-ESANet architecture.

Keywords:

Completion delay

efficient transmission

network coding (NC)

neural network

partial packet recovery (PPR)

syndrome decoding

A Learned-PPR Decoding Scheme for Partial Packet Recovery in Network Coding

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