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.
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:2602. 22277v2 Announce Type: replace Abstract: AI-native architectures are vital for 6G wireless communications.
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:2608. 14820v1 Announce Type: cross Abstract: Handover (HO) management in vehicular networks requires fast and reliable decision-making under highly dynamic conditions.
The paper introduces DRIFT, a lightweight framework for joint channel estimation and prediction in low Earth orbit non-terrestrial networks, aiming to reduce pilot overhead by using data-driven processing after the initial slot. DRIFT refines data-aided channel estimates and forecasts future channel responses with low computational cost, offering two variants based on convolutional and LSTM layers. Simulations show up to 12% spectral efficiency gain over conventional pilot-based systems, with under 200k multiply-accumulate operations suitable for on-board satellite implementation.
arXiv:2608. 14709v1 Announce Type: cross Abstract: This demo presents real-time AI-based uplink channel-estimation inference using data collected from a hardware-in-the-loop 5G platform.
arXiv:2608. 14676v1 Announce Type: cross Abstract: In 5G/6G wireless systems, accurate and timely channel estimation is critical to ensure reliable communication under complex, fast-changing radio conditions.
arXiv:2609.22139v1 Announce Type: cross Abstract: Automatic modulation classification (AMC) of received radio signals is prudent for further signal processing tasks such as communication monitoring,...
The paper introduces Encoder‑Sharing Hierarchical Multi‑Task Federated Learning (EN‑HMTFL) for vehicular ad hoc networks, combining cluster‑based hierarchical federated learning with a globally shared encoder and vehicle‑local decoders. This design allows vehicles performing different perception tasks to collaboratively learn a transferable feature representation while keeping raw data and task‑specific decoders local. Experiments on MNIST and GTSRB datasets show that EN‑HMTFL can improve accuracy by up to 24.0% and reduce communication rounds by up to 69 (28.8%) compared to a representation‑sharing benchmark.
arXiv:2608. 02172v1 Announce Type: cross Abstract: Neural receivers have been recognized as a promising paradigm for the next-generation (NextG) communications.
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.