An overview of beam-tracking techniques for mmWave wireless communications

Abstract

Millimeter-wave (mmWave) communication is the key to increasing the demand for high data rates and low latency resulting from the rapid evolution of wireless communications, especially in the fifth generation (5G) of wireless communication systems and beyond. The mmWave band suffers from high path loss and obstacle blockage, significantly reducing the transmission range. Note that high-directional beams are required to perform well in the mmWave band. Hence, beam alignment is crucial for high-data-rate transmission between the transmitter (Tx) and the receiver (Rx). One of the drawbacks is getting an accurate beam alignment when the transceiver (Tx, Rx, or both) is mobile. Beam tracking plays a considerable role in 5G communications, especially in vehicular communications, due to the repeated change of the transceiver (Tx, Rx, or both) position. This work presents an overview of the different beam-tracking methods used in mmWave communications, focusing on hybrid beamforming techniques. We also compare the various tracking techniques in a recommendation table. This overview suggests that some tracking methods used in the sub-6-GHz band, such as least mean squares (LMS), recursive least squares (RLS), and Kalman filter, are unsuitable for the mmWave band (due to higher frequency and shorter coherence time), and it recommends faster tracking strategies.

1. Introduction

Millimeter-wave (mmWave) communication technology is viable for fifth-generation (5G) wireless communication systems and beyond because of the growing demand for data speeds and the restricted spectrum available for the sub-6-GHz band (Samimi et al., 2016). These frequencies have an available spectrum up to 200 times larger than current cellular spectrums (Barati et al., 2015; Kokshoorn et al., 2016) and frequency bands ranging from 30 GHz to 300 GHz (Wang P et al., 2015; Wang CX et al., 2020). mmWave frequencies suffer from high path loss and keen shadowing compared to traditional sub-6-GHz frequencies (Haghighatshoar and Caire, 2016) because they have a much smaller wavelength of about 1–10 mm (Wu S et al., 2021). This makes them susceptible to precipitation and oxygen absorption (Hur et al., 2013). Therefore, the receiver (Rx) and the transmitter (Tx) use directional beamforming (BF) to get around the channel attenuation problem (Va, 2018) and reduce interference in mmWave networks (Hashemi et al., 2018; Liu Q et al., 2022). They use large antenna arrays to obtain high gain with narrow beams (Wu W et al., 2019).

mmWave communications use different BF techniques, such as analog BF (ABF), digital BF (DBF), and hybrid BF (HBF), to beat the rise in power consumption and the enormous hardware cost that results from using a large array of antennas (Han C et al., 2019). As seen in Fig. S1, ABF uses only one radio frequency (RF) chain with phase shifters to form the beams. To compensate for the large amount of channel attenuation, ABF uses directivity gains and spatial division to send the signal to different areas. DBF allows enormous flexibility in molding the transmitted beams. Because many antennas operate in a considerable bandwidth, DBF requires one RF chain for each antenna element, raising the cost and complexity. Using the channel state information (CSI) of an active channel with a tiny dimension, DBF lowers the intrasector interference and provides a gain (El Ayach et al., 2014). As seen in Fig. S2, the HBF technology combines ABF and DBF into a single structure. ABF is produced in HBF by connecting a series of phase shifters for every RF chain (Perfecto et al., 2017). HBF gives a reasonable solution for both hardware complexity and performance gain (Mavromatis et al., 2017). For the mmWave, the channels’ sparseness reduces the design’s HBF complexity. The capacity to create beam patterns from the sum of two or more RF chain BF vectors is the primary benefit of HBF structures (Tagliaferri et al., 2021). The BF and precoding steps improve the mmWave spectral efficiency (Ciaramitaro et al., 2023). This paper focuses on HBF due to its flexibility and low complexity. Subsequently, BF with precoding (multiple data streams) improves the mmWave spectral efficiency (Ali et al., 2020). Beam alignment is necessary for fast-moving states because the angular spread of each path is very small. There are other works of literature illustrating the beam alignment problem in different scenarios.