Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper introduces Adaptive Peer Clustering with Hierarchical Random Linear Network Coding (APC-RLNC), a novel communication protocol designed for decentralized wireless networks that optimizes peer grouping based on reliability metrics. By employing exponentially weighted moving averages for clustering and multi-tier network coding, the method significantly enhances packet delivery ratios and reduces latency in dynamic environments. Experimental results show that APC-RLNC achieves up to 9.8 percentage points improvement in packet delivery and maintains real-time performance even with over 500 nodes, establishing a new standard for resilient communication in AI-driven 6G systems.
APC-RLNC boosts packet delivery by nearly 10% and cuts latency by up to 23% in decentralized wireless networks, even under challenging conditions.
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.