Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems

Millimeter wave vehicular channels exhibit structure that can be exploited for beam alignment with fewer channel measurements compared to exhaustive beam search. With fixed layouts of roadside buildings and regular vehicular moving trajectory, the dominant path directions of channels will likely be among a subset of beam directions instead of distributing randomly over the whole beamspace. In this paper, we propose a deep learning-based technique to design a structured compressed sensing (CS) matrix that is well suited to the underlying channel distribution for mmWave vehicular beam alignment. The proposed approach leverages both sparsity and the particular spatial structure that appears in vehicular channels. We model the compressive channel acquisition by a two-dimensional (2D) convolutional layer followed by dropout. We incorporate the low-resolution phase shifter constraint during neural network training by using projected gradient descent for weight updates. Furthermore, we exploit channel spectral structure to optimize the power allocated for different subcarriers. Simulations indicate that our deep learningbased approach achieves better beam alignment than standard CS techniques which use random phase shift-based design. Numerical experiments also show that one single subcarrier is sufficient to provide necessary information for beam alignment. Millimeter-wave (mmWave) vehicular communication enables massive sensor data sharing and various emerging applications related to safety, traffic efficiency and infotainment [2]-[4]. Yuyang Wang is with Apple Inc., One Apple park way, Cupertino, CA, 95014, USA, email: yuywang@utexas.edu. Nitin Jonathan Myers is with Samsung Semiconductor Inc., 5465 Morehouse Dr, San Diego, CA 92121 USA, email: nitinjmyers@utexas.edu. Nuria González-Prelcic, and Robert W. Heath Jr. are with the Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC 27606 USA, email: {ngprelcic, rwheathjr}@ncsu.edu. Part of this work has been presented at IEEE ICASSP 2020 [1]. This material is based upon work supported in part by the National Science Foundation under Grant No. ECCS-1711702, and by a Qualcomm Faculty Award.