Strongest Angle-of-Arrival Estimation for Hybrid Millimeter Wave Architecture with 1-Bit A/D Equipped at Transceivers

Area of Science:

• Wireless Communication
• Signal Processing

Background:

• Millimeter wave (mmWave) systems offer high bandwidth but face challenges with hardware complexity and power consumption.
• Hybrid architectures with 1-bit analog-to-digital/digital-to-analog converters (A/Ds) are explored to reduce cost and power.
• Accurate estimation of angles of arrival (AoAs) is crucial for beamforming and system performance.

Purpose of the Study:

• To propose an effective algorithm for estimating the strongest angles of arrival (AoAs) in hybrid mmWave systems with 1-bit A/Ds.
• To minimize estimation overheads while preserving the accuracy of AoA estimates.
• To evaluate the algorithm's performance against conventional methods and theoretical bounds.

Main Methods:

• Numerical simulations were conducted to determine optimal quantization thresholds for A/Ds across various signal-to-noise ratios (SNRs) and antenna configurations.
• The proposed algorithm leverages these thresholds to estimate strongest AoAs with reduced computational and signaling overhead.
• Performance was benchmarked against 1-bit Fast Fourier Transform (FFT), 1-bit exhaustive search, and the 1-bit Cramér-Rao lower bound (CRLB).

Main Results:

• The proposed algorithm effectively estimates strongest AoAs using significantly reduced overheads compared to existing methods.
• Simulation results demonstrate that the algorithm maintains reasonable estimation performance, particularly in low SNR conditions.
• The root mean square error (RMSE) of AoA estimates is kept competitive, validating the algorithm's practical applicability.

Conclusions:

• The developed algorithm offers an efficient solution for AoA estimation in 1-bit hybrid mmWave systems.
• It presents a favorable trade-off between estimation overhead reduction and performance accuracy.
• The findings support the viability of employing 1-bit A/Ds in future mmWave communication systems with optimized estimation strategies.