Channel Estimation for Mixed-Analog to Digital Converters Architecture in Massive MIMO Architecture Using Approximate Conjugate Gradient Pursuit Algorithm

Millimeter Wave (mmWave) Massive Multiple Input Multiple Out (MIMO) system is a key technology for future wireless transmission. The system's architecture can differ based on the type of Analog-to-Digital Converters (ADCs) used at the receiver, whether they are all low-resolution or a mix of different resolutions (Mixed-ADCs). Mixed-ADCs is a promising solution to achieve better performance than low-resolution ADC-only architectures by leveraging high-resolution ADCs to capture critical signal components while maintaining energy efficiency through low-resolution ADCs. In this paper, the problem of channel estimation for this system architecture is taken into consideration. A novel compressive-sensing based algorithm, that is called Approximate Conjugate Gradient Pursuit (ACGP), is proposed to estimate the channel coefficients. The performance of the proposed algorithm is investigated under varying system parameters, including different Signal-to-Noise Ratios (SNR), Radio Frequency (RF) chains, ADC resolutions, and numbers of observation frames. Matlab software was used to perform numerical simulations. The results demonstrated that mixed-ADCs architecture outperforms low resolutions only in performance. It was found that ACGP achieves lower Minimum Mean Squared Error (MMSE) compared to Orthogonal Matching Pursuit (OMP) and Least Square (LS), particularly in low SNR conditions showcasing its robustness and efficiency in signal reconstruction, achieving an average enhancement of 30% to 50% at moderate SNR levels. While OMP exhibited faster computation times under various number of observation frames, ACGP maintained stable computational performance, with a slight increase in computation time. For applications where accurate channel estimation is required under noisy environment, the proposed algorithm is an effective choice to meet such requirements.