Channel Estimation Using Hybrid Attention-Based Neural Network for V2X Communication

In the dynamic realm of the internet of vehicles, ensuring robust Vehicle-to-Everything (V2X) communication is essential for advancing intelligent transportation systems. The IEEE 802.11p standard, while foundational, faces challenges in channel estimation due to its limited pilot structure, especially under high-mobility scenarios. Traditional pilot-aided estimation techniques often grapple with error propagation and diminished accuracy in such environments. Addressing these challenges, this paper introduces ‘HAN’, an innovative hybrid attention-based deep learning model that synergizes time-domain and frequency-domain attention mechanisms to enhance channel estimation. Empirical evaluations reveal that HAN achieves up to a 4 dB performance improvement in high signal-to-noise ratio conditions. Comprehensive testing across various channel conditions, modulation schemes, and vehicular speeds within the vehicle-to-vehicle expressway (VTV-EX) channel underscores its resilience and efficacy. Furthermore, implementation on the Xilinx ZCU102 FPGA platform demonstrates the model’s practicality for real-time applications in resource-constrained environments.