wireless-networks

This paper proposes FedLBW, a federated learning aggregation strategy that weights client updates by inverse validation loss instead of dataset size, improving accuracy and robustness to non-IID data and client dropouts in wireless networks.

This paper introduces OCO-PAoI-Hard, an online convex optimization framework for multi-sensor IoT scheduling that enforces hard per-slot peak Age-of-Information deadlines under adversarial channels, achieving zero modeled-state deadline violations and O(√T) regret.

This paper presents a tutorial on using Joint-Embedding Predictive Architecture (JEPA) for self-supervised learning in 6G networks, along with a beam management case study and open challenges.

This paper introduces PROBE, a multi-stage pipeline for diagnosing 802.11 packet captures that combines deterministic normalization, multi-run ensemble, and a verdict-aware evidence framework to produce reliable and calibrated diagnoses, outperforming single-pass LLM analysis and naive ensemble voting.