3 citations · 6 across the 7 of their papers we have counts for
8 papers
FM-Receiver: A Foundation Model Enabled Unified Inner and Outer Neural Receiver Towards AI-Native Wireless Communications
Tianyue Zheng, Chao Jiang, Linglong Dai
With the development of artificial intelligence (AI) techniques, neural receivers, which apply AI to improve wireless receivers have been developed. However, most existing neural r…
MUSE-FM: Multi-task Environment-aware Foundation Model for Wireless Communications
Tianyue Zheng, Jiajia Guo, Linglong Dai +2
Recent advancements in foundation models (FMs) have attracted increasing attention in the wireless communication domain. Leveraging the powerful multi-task learning capability, FMs…
Empowering Near-Field Communications in Low-Altitude Economy with LLM: Fundamentals, Potentials, Solutions, and Future Directions
Zhuo Xu, Tianyue Zheng, Linglong Dai
The low-altitude economy (LAE) is gaining significant attention from academia and industry. Fortunately, LAE naturally aligns with near-field communications in extremely large-scal…
Large Language Model Enabled Multi-Task Physical Layer Network
Tianyue Zheng, Linglong Dai
The advance of Artificial Intelligence (AI) is continuously reshaping the future 6G wireless communications. Particularly, the development of Large Language Models (LLMs) offers a…
Unified Error Correction Code Transformer with Low Complexity
Yongli Yan, Jieao Zhu, Tianyue Zheng +3
Channel coding is vital for reliable sixth-generation (6G) data transmission, employing diverse error correction codes for various application scenarios. Traditional decoders requi…
Near-Field Wideband Beam Training Based on Distance-Dependent Beam Split
Tianyue Zheng, Mingyao Cui, Zidong Wu +1
Near-field beam training is essential for acquiring channel state information in 6G extremely large-scale multiple input multiple output (XL-MIMO) systems. To achieve low-overhead…