  End-to-end wireless communication is new concept expected to be widely used
in the physical layer of future wireless communication systems (6G). It
involves the substitution of transmitter and receiver block components with a
deep neural network (DNN), aiming to enhance the efficiency of data
transmission. This will ensure the transition of autonomous vehicles (AVs) from
self-autonomy to full collaborative autonomy, that requires vehicular
connectivity with high data throughput and minimal latency. In this article, we
propose a novel neural network receiver based on transformer architecture,
named TransRx, designed for vehicle-to-network (V2N) communications. The
TransRx system replaces conventional receiver block components in traditional
communication setups. We evaluated our proposed system across various scenarios
using different parameter sets and velocities ranging from 0 to 120 km/h over
Urban Macro-cell (UMa) channels as defined by 3GPP. The results demonstrate
that TransRx outperforms the state-of-the-art systems, achieving a 3.5dB
improvement in convergence to low Bit Error Rate (BER) compared to
convolutional neural network (CNN)-based neural receivers, and an 8dB
improvement compared to traditional baseline receiver configurations.
Furthermore, our proposed system exhibits robust generalization capabilities,
making it suitable for deployment in large-scale environments.
