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In this paper, we propose new Discrete Fourier Transform (DFT)-based beamspace selection algorithms for Massive Multiple Input, Multiple Output (MIMO) receiver operating in realistic multi-user (MU) scenarios. In practical uplink scenarios, there is a power ratio between signals received from different users that complicates performance analysis in MU cases. Our algorithms are inspired by a proportional fair approach to allocating spatial resources for target users. Thus, we analyze performance in terms of coverage (for low-power users) and capacity (for high-power users) and consider the implementation complexity to highlight feasible algorithms. Simulations with realistic non-line-of-sight scenarios generated by the QuaDRiGa 2.0 demonstrate our methods outperform other DFT-based alternatives, such as the beamspace selection based on the projection power maximization.INDEX TERMS 5G, multi-user massive MIMO, beamspace selection, angular domain processing.

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An indoor localization approach uses Wi-Fi Access Points (APs) to estimate the Direction of Arrival (DoA) of the Wi-Fi signals. This paper demonstrates FIND, a tool for Fine INDoor localization based on a software-defined radio, which receives Wi-Fi frames in the 80 MHz band with four antennas. To the best of our knowledge, it is the first-ever prototype that extracts from such frames data in both frequency and time domains to calculate the DoA of Wi-Fi signals in real-time. Apart from other prototypes, we retrieve from frames comprehensive information that could be used to DoA estimation: all preamble fields in the time domain, Channels State Information, and signal-tonoise ratio. Using our device, we collect a dataset for comparing different algorithms estimating the angle of arrival in the same scenario. Furthermore, we propose a novel calibration method, eliminating the constant phase shift between receiving paths caused by hardware imperfections. All calibration data, as well as a gathered dataset with various DoA in an anechoic chamber and in a classroom, are provided to facilitate further research in the area of indoor localization, intelligence surfaces, and multi-user transmissions in dense deployments 1 .

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