arXiv:2609.09708v2 Announce Type: replace-cross Abstract: 6G wireless technology is poised to adopt higher and wider frequency bands, leveraging highly directional beamforming. However, the vast band...
arXiv:2607. 00860v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging.
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:2511. 06663v2 Announce Type: replace-cross Abstract: Accurate Channel State Information (CSI) is critical for Hybrid Beamforming (HBF) tasks.
arXiv:2607. 08454v1 Announce Type: cross Abstract: Coordinated beamforming in distributed 5G networks relies on the timely exchange of inter-cell scheduling information, but backhaul latency makes this information stale.
arXiv:2608. 07444v1 Announce Type: cross Abstract: Accurate user equipment (UE) localization is critical for beam management in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) based sixth-generation (6G) networks, especially if the direct base-station-UE links are unavailable.
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:2511. 20305v2 Announce Type: replace-cross Abstract: This paper investigates a reconfigurable intelligent surface (RIS)-assisted multi-waveguide pinching-antenna (PA) system (PASS) for multi-user downlink information transmission, motivated by the unknown impact of the integration of emerging PASS and RIS on wireless communications.
The paper tackles the challenge of converting uplink to downlink channel covariance matrices in FDD massive MIMO systems, where learning‑based methods lose accuracy as the antenna count grows. It identifies that the mean angle of arrival creates a phase ramp whose oscillation rate increases with array size, making fixed‑size datasets sparse. The authors propose a deramping technique that estimates and removes this ramp before learning, reducing estimation error across three different learners and maintaining superiority over model‑based benchmarks at large array sizes.
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:2606. 06373v1 Announce Type: cross Abstract: Wireless foundation models have emerged as a promising alternative to building separate models for each wireless task.
The paper proposes a CSI‑free hierarchical multi‑agent reinforcement learning framework for controlling reconfigurable reflective surfaces in millimeter‑wave networks. By replacing per‑element channel estimation with user localization data, the system uses a two‑tier neural architecture: a high‑level controller for discrete user‑to‑reflector assignments and low‑level controllers that optimize continuous focal points via MAPPO under a CTDE scheme. Deterministic ray‑tracing tests show RSSI gains of up to 7.79 dB over centralized PPO baselines and robust performance with sub‑meter localization errors for multiple users and reflector arrays.