Efficient Near-field Localization for Hybrid Analog and Digital UM-MIMO Systems

Hybrid analog and digital ultra-massive multiple-input multiple-output (UM-MIMO) has become one of the key enabling technologies for the upcoming 6G. As the number of antennas of UM-MIMO systems increases and the array size grows, the near-field assumption should be considered instead of the far-field assumption. Therefore, more complex algorithms are required to estimate the DoA and distance that describe the characteristics of the source. In this paper, we propose an efficient near-field localization algorithm for hybrid analog and digital UM-MIMO systems, which reduces the high computational complexity of existing algorithms in the near field by decoupling directions of arrival (DoA) and distance estimation. Firstly, by designing the digital combiner, we estimate the DoA using the central subarray received signal. Next, we design a set of analog combiners that depend solely on the distance, and apply signal-to-noise ratio (SNR) determination to narrow down the range for distance estimation. Finally, digital combiners are applied to perform an exhaustive search within the narrowed distance range, enabling efficient localization. Simulation results show that the localization performance of our proposed algorithm is superior to the existing algorithms, and its computational complexity is significantly reduced.

Efficient Near-field Localization for Hybrid Analog and Digital UM-MIMO Systems

Yanran Sun, Chuang Yang, Beijing University of Posts and Telecommunications; Mugen Peng, Beijing University of Posts & Telecommunications