Tensor Decomposition-Based Channel Estimation and Sensing for Millimeter Wave MIMO-OFDM V2I Systems

Abstract: An integrated sensing and communication (ISAC) scheme for a millimeter wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) Vehicle-to-Infrastructure (V2I) system is presented, in which both the access point (AP) and the vehicle equip large antenna arrays and employ hybrid analog and digital beamforming structures to compensate the path loss meanwhile compromise between hardware complexity and system performance. Based on the sparse scattering nature of the mmWave channel, the received signal at the AP is organized to a four-order tensor by the introduced novel frame structure. A CANDECOMP/PARAFAC (CP) decomposition-based method is proposed for time-varying channel parameter extraction, including angles of departure/arrival (AODs/AOAs), Doppler shift, time delay and path gain. Then leveraging the estimates of channel parameters, a nonlinear weighted least-square problem is proposed to accurately recover the location, heading and velocity of vehicles. Simulation results show that the proposed methods are effective and efficient in time-varying channel estimation as well as vehicle sensing in mmWave MIMO-OFDM V2I systems.

Keywords: MIMO-OFDM Vehicle-to-Infrastructure (V2I) systems; ISAC; time-varying channel estimation; CANDECOMP/PARAFAC (CP) decomposition