Leveraging Waveform Structure to Develop a Power Scalable AoA Estimation

In a mmWave mobile device, power consumption resulting from the high sampling rate is a primary concern for angle of arrival (AoA) estimation solutions. In this paper, we provide a power scalable solution for AoA estimation with structured waveforms in a narrowband channel. We design a set of pilot sequences that maintain orthogonality in sub-Nyquist sampling domains. We leverage the sequences’ structure to develop a variable rate decoupling algorithm to separate multiple sources at the receiver using partial knowledge about the pilots. The decoupling enables a feasible, low-complexity AoA estimation for digital architectures with flexible antenna array design. In this paper, we provide one such AoA estimation solution named ADELA for a linear antenna array design. Simulation results show that AoA estimation performance reaches the Cramer-Rao-Bounds (CRBs) for a range of SNRs. The proposed estimator with subsampling factors of 8 or less outperforms two examples of full rate virtual array AoA estimators for unknown waveforms: 2-level nested array and coprime filter bank estimators. Compared to these two examples of virtual array, our method offers an 8 times lower ADC power consumption, and a significantly lower computational complexity.