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Beam Tracking Particle Filter for Hybrid Beamforming and Precoding Systems

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Abstract

Because millimeter wave (mmWave) systems can span notably wide spectral bands, mmWave systems are expected to dominate fifth-generation (5G) communication systems. Due to the short wave-length of mmWave radiation, multiple-input multiple-output (MIMO) systems can use massive antennas and precoding technology to overcome signal attenuation in mmWave channels. However, the cost and power consumption of radio frequency (RF) chains would increase substantially with the number of antennas. Hence, hybrid beamforming was proposed to reduce the number of RF chains in massive MIMO systems. Hybrid beamforming involves RF beamforming matrix construction and baseband precoding matrix derivation. This study focused on the design and implementation of an algorithm for the RF beamforming matrix construction for mobile environments. Accordingly, this study presents a mixture particle filter that exploits the temporal continuity of beam clusters in a mobile mmWave channel to reduce the computational complexity of RF beamforming matrix construction. Moreover, this beam-tracking particle filter is based on parallel processing architecture to support the tracking of multiple beam clusters in the mmWave channel. Finally, the beam-tracking particle filter was implemented on a field-programmable gate array platform and was verified in a hybrid beamforming system for mmWave MIMO systems. The particle filter processor achieved a maximal throughput of 9.198k matrices/s with a clock rate of 192 MHz, which could support a speed of up to 88.5 km/h for mobile users.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Tsing Hua University, Hsinchu, R.O.C., 30013, Taiwan

    Huan-Zhang Huang, Yan-Cheng Wang & Yuan-Hao Huang

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  1. Huan-Zhang Huang
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  2. Yan-Cheng Wang
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  3. Yuan-Hao Huang
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Correspondence to Yuan-Hao Huang.

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Huang, HZ., Wang, YC. & Huang, YH. Beam Tracking Particle Filter for Hybrid Beamforming and Precoding Systems. J Sign Process Syst 92, 499–515 (2020). https://doi.org/10.1007/s11265-019-01513-1

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  • DOI: https://doi.org/10.1007/s11265-019-01513-1

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