Beamforming
This MATLAB script demonstrates an approach to beamforming and interference suppression in scenarios with multiple users and multiple interferers. It constructs an N-element linear array, computes beamformer weights through a generalized eigen-decomposition of summed desired and interference correlation matrices, and then runs a Monte Carlo simulation to estimate the Signal-to-Interference-plus-Noise Ratio (SINR) for one of the users under random channel conditions.
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This dataset has been collected from both User Equipment (UE) and Network sides. UE side metrics consist of radio metrics that have been merged with localization information from the modem. Network side metrics consist of network Key Performance Indicators (KPI).
The dataset contains both stationary and movement samples for different approaches. Beamforming information is available from the serving and up to 3 neighbouring beams.
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This dataset contains results of the 60 GHz indoor sensing measurement campaign using a bistatic OFDM radar based on 5G-specified positioning reference signals (PRSs). The data can be used for testing end-to-end indoor millimeter-wave radio positioning as well as simultaneous localization and mapping (SLAM) algorithms, including channel parameter estimation. Beamformed PRS with dense angular sampling in transmission and reception allows efficient capture of line-of-sight (LoS) as well as multipath components.
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This data provides realized gain values for a handset operating at 28 GHz, with 3 4x1 linear antenna arrays placed around the handset along the right edge, bottom edge and back face of the handset. Beam steering was carried out at each of these antenna arrays and results for the handset with and without the hand phantom are included to show the effect that the introduction of the hand phantom has on the realized gain of the handset.
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This is a file containing the codes for IEEE paper
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