Neural Network Aided M-PSK Detection in 802.11P V2V OFDM Systems Under ICI Conditions

This paper investigates the application of neural networks for Quadrature Phase Shift Keying (QPSK) signal detection in Orthogonal Frequency Division Multiplexing (OFDM) systems designed for wideband vehicle-to-vehicle (V2V) communication environments. In such scenarios, inter-carrier interference (ICI) induced by doubly dispersive channels leads to a loss of subcarrier orthogonality, significantly impairing detection performance. Traditional linear and nonlinear ICI mitigation techniques exhibit fundamental trade-offs between bit error rate (BER) performance and computational complexity, limiting their effectiveness in highly dynamic V2V conditions. A low-complexity neural network is proposed for M-PSK detection under ICI. The approach includes a preprocessing step that converts interference mitigation into a classification task. Results show that this approach performs better than conventional non-linear algorithms while keeping complexity near to linear least squares detection.