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This paper introduces Adaptive Peer Clustering with Hierarchical Random Linear Network Coding (APC-RLNC), which enhances communication resilience in decentralized wireless networks by dynamically clustering peers based on their reliability metrics. By employing multi-tier network coding and formalizing the clustering optimization problem, the authors achieve significant improvements in packet delivery ratio, latency, and node retention across various challenging scenarios. The results demonstrate that APC-RLNC not only scales effectively to over 500 nodes but also maintains low reconfiguration overhead, making it a promising solution for future AI-native 6G systems.
APC-RLNC achieves up to 9.8 percentage-point improvements in packet delivery while reducing latency by up to 23% in dynamic wireless environments.
Decentralized wireless collectives including vehicular swarms, IoT clusters, and edge AI networks require communication protocols that maintain robustness under dynamic topologies and heterogeneous link quality. While Random Linear Network Coding (RLNC) provides algebraic resilience against packet erasures, its performance degrades significantly when peers exhibit diverse channel conditions. This paper presents Adaptive Peer Clustering with Hierarchical RLNC (APC-RLNC), a system that dynamically groups peers by exponentially weighted moving average (EWMA) reliability metrics and applies multi-tier network coding within and across clusters. We formalize the clustering optimization problem, derive closed-form decoding probability bounds for Markov erasure channels, and prove O(sqrt(T)) regret for online reconfiguration under the Follow-the-Regularized-Leader (FTRL) framework. Our implementation includes both a high-fidelity network simulator and a proof-of-concept testbed deployment on Jetson Nano edge devices. Evaluation across diverse scenarios including high-mobility vehicular networks, burst-error channels, and adversarial interference demonstrates 5.2-9.8 percentage-point packet delivery ratio (PDR) improvements, 10-23% latency reductions, and up to 30% higher node retention compared to state-of-the-art baselines. The system exhibits linear scalability to 500+ nodes and maintains real-time reconfiguration overhead below 3%. APC-RLNC establishes adaptive clustering as a foundational primitive for AI-native 6G wireless systems.
