A Joint Symbol-Detection, Channel-Estimation and Decoding Scheme under Few-Bit ADCs in mmWave Communications

Area of Science:

• Electrical Engineering
• Signal Processing
• Wireless Communications

Background:

• Few-bit analog-to-digital converters (ADCs) are crucial for reducing power consumption in Internet of Things (IoT) devices, especially in millimeter-wave (mmWave) communication systems.
• Existing methods face challenges in joint symbol detection, channel estimation, and decoding with limited bit quantization.

Purpose of the Study:

• To propose a new joint scheme for symbol detection, channel estimation, and decoding tailored for few-bit ADC systems in mmWave communications.
• To enhance the performance and robustness of IoT devices by addressing power consumption and nonlinear distortions.

Main Methods:

• Leveraging the parametric bilinear generalized approximate message passing (PBiGAMP) framework.
• Introducing a doping factor for flexible control over information components.
• Implementing the joint scheme using fast Fourier transformation (FFT) for logarithmic complexity.

Main Results:

• The proposed scheme demonstrates significant performance gains compared to benchmark algorithms.
• Effectively mitigates nonlinear distortions introduced by few-bit ADCs.
• Achieves logarithmic complexity growth through FFT implementation.

Conclusions:

• The developed joint scheme offers a powerful and efficient solution for few-bit ADC-based mmWave communication systems.
• Provides a flexible approach to handle diverse priors and quantized observations, crucial for advanced IoT applications.