Optimizing Pilots and Analog Processing for Channel Estimation in Cell-Free Massive MIMO With One-Bit ADCs

TL;DR

This paper proposes a joint optimization method for pilot sequences and analog combiners in cell-free massive MIMO systems with one-bit ADCs, improving channel estimation accuracy in resource-constrained scenarios.

Contribution

It introduces an iterative algorithm that jointly optimizes pilots and analog combiners considering one-bit ADC effects, a novel approach for such systems.

Findings

Joint optimization reduces channel estimation error.

Proposed method outperforms baseline schemes.

Efficient iterative algorithm demonstrated through simulations.

Abstract

In a cell-free cloud radio access network (C-RAN) architecture, uplink channel estimation is carried out by a centralized baseband processing unit (BBU) connected to distributed remote radio heads (RRHs). When the RRHs have multiple antennas and limited radio front-end resources, the design of uplink channel estimation is faced with the challenges posed by reduced radio frequency (RF) chains and one-bit analog-to-digital converters (ADCs) at the RRHs. This work tackles the problem of jointly optimizing the pilot sequences and the pre-RF chains analog combiners with the goal of minimizing the sum of mean squared errors (MSEs) of the estimated channel vectors at the BBU. The problem formulation models the impact of the ADC operation by leveraging Bussgang's theorem. An efficient solution is developed by means of an iterative alternating optimization algorithm. Numerical results validate the advantages of the proposed joint design compared to baseline schemes that randomly choose either pilots or analog combiners.