AI 中文总结
APC-RLNC通过EWMA可靠性指标动态聚类对等节点，结合分层RLNC，在多类去中心化无线网络场景下提升PDR、降低延迟并增强鲁棒性，为6G无线系统提供基础原语。
AI 中文摘要
包括车载集群、物联网簇和边缘AI网络在内的去中心化无线集体，需要通信协议在动态拓扑和异构链路质量下保持鲁棒性。随机线性网络编码（RLNC）虽能提供抗分组删除的代数鲁棒性，但当对等节点表现出不同信道条件时，其性能会显著下降。本文提出自适应对等聚类与分层RLNC（APC-RLNC）系统，该系统通过指数加权移动平均（EWMA）可靠性指标动态分组对等节点，并在簇内及簇间应用多层网络编码。我们将聚类优化问题形式化，推导马尔可夫删除信道的闭式解码概率界，并在正则化领导者跟随（FTRL）框架下证明在线重构的O(√T)遗憾值。我们的实现既包括高保真网络模拟器，也包括在Jetson Nano边缘设备上的概念验证测试床部署。在高移动性车载网络、突发错误信道和对抗性干扰等不同场景下的评估显示，与最先进基线相比，分组交付率（PDR）提升5.2-9.8个百分点，延迟降低10-23%，节点保留率最高提升30%。该系统对500+节点表现出线性可扩展性，且实时重构开销低于3%。APC-RLNC将自适应聚类确立为原生AI 6G无线系统的基础原语。
英文摘要
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.
Comments8 pages, 4 figures. Under review at IEEE Transactions on Wireless Communications. Code: https://github.com/ka-cyber/apc-rlnc
