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

Abstract

Few-bit analog-to-digital converter (ADC) is regarded as a promising technique to greatly reduce power consumption of Internet of Things (IoT) devices in millimeter-wave (mmWave) communications. In this work, based on the recently proposed parametric bilinear generalized approximate message passing (PBiGAMP), we propose a new scheme to perform joint symbol detection, channel estimation and decoding. The proposed scheme is flexible to deal with discrete prior on symbols, Gaussian mixture prior on channels and quantized likelihood on observations. Furthermore, we introduce doping factor to control the portion of “extrinsic” and “posterior” information with negligible complexity increase. Since this joint scheme can be implemented via fast Fourier transformation (FFT), the complexity grows only logarithmically. Compared to the benchmark algorithms, numerical results show that the proposed joint scheme can achieve significant performance gain, and demonstrate the effectiveness in dealing with the nonlinear distortion from few-bit ADC.

Keywords

• IoT
• mmWave communications
• few-bit ADC
• parametric bilinear generalized approximate message passing
• doping factor