arXiv:2602. 22277v2 Announce Type: replace Abstract: AI-native architectures are vital for 6G wireless communications.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The paper introduces X-RACE, a framework that combines explainable AI with recurrent neural networks to improve channel estimation in high‑mobility vehicular environments. X-RACE employs a low‑complexity, one‑shot dual‑optimization strategy to prune unnecessary input subcarriers and hidden units, while also defining new temporal XAI metrics—Saturation Time, Importance Drift, and Relevance Contrast—to analyze LSTM learning dynamics. Simulation results show that X-RACE cuts inference complexity by at least 44.1% and maintains or improves Bit Error Rate performance compared to traditional XAI methods.
arXiv:2606. 11857v1 Announce Type: cross Abstract: Multi-channel mixed-SNR training improves out-of-distribution (OOD) generalisation of deep learning channel estimators for IEEE 802.
Multi-channel mixed-SNR training improves out-of-distribution (OOD) generalisation of deep learning channel estimators for IEEE 802. 11p vehicular communications, yet the internal mechanism responsible for this remains unexplained.
arXiv:2507. 09627v3 Announce Type: replace-cross Abstract: Next-generation wireless technologies such as 6G aim to meet demanding requirements such as ultra-high data rates, low latency, and enhanced connectivity.
arXiv:2602. 11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization.
arXiv:2106. 06998v5 Announce Type: replace Abstract: Training convolutional neural networks at scale demands substantial memory, largely because intermediate activations must be stored for backpropagation.