2019/07/10 by Tian-Hao Li, Tianhao Li, Muhammad R. A. Khandaker +10
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Millimeter-Wave Propagation and Modeling #Signal Processing (eess.SP) #Wireless Signal Modulation Classification #cs.IT #cs.LG #eess.SP #electronic engineering #information engineering #math.IT #stat.ML
paper · pdf · doi:10.48550/arxiv.1907.04831
arxiv created 2019/07/10 · openalex publication_date 2019/07/10 · arxiv updated 2019/07/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.