System Level Analysis of Computational Channel Characterization using Compressive Surfaces

Direction of arrival (DoA) estimation plays a crucial role in channel characterization and is a critical step to execute the necessary beam-shaping operations needed from antennas present within a wireless environment. Conventional DoA estimation frameworks rely on array-based topologies, making use of the phase difference information between the individual channels to retrieve the DoA data. Recently, the idea of computational imaging has been shown to offer a promising solution to conventional raster-scan-based techniques. This is mainly due to the physical layer compression facilitated by special types of compressive apertures (or surfaces) that leverage the idea of synthesizing quasi-orthogonal radiation patterns (modes) to probe and encode the scene information in an indirect manner and compress it into a single channel (or a reduced number of channels). The application of computational imaging to the channel characterization problem is intriguing. Yet, a system level of knowledge of the design parameters, which are key to understanding the development of compressive surfaces for computational DoA estimation, is far from comprehensive. In this paper, we demonstrate different techniques to synthesize spatio-temporally incoherent field patterns, a key requirement for computational DoA estimation, and provide a study of the different system level parameters needed to design such antennas. We show that by increasing the orthogonality of the radiated modes (and thus reducing the information redundancy), a single-channel compressive antenna can retrieve the DoA pattern of multiple far-field sources even under a signal-to-noise ratio (SNR) level of as low as 0 dB, without the necessity to use a multi-channel physical architecture.