Deep Learning Assisted Channel Estimation for Cell-Free Distributed MIMO Networks

Pilot contamination poses a critical challenge for channel estimation in dense cell-free (CF) distributed multiple-input multiple-output (CF-DMIMO) wireless networks. State-of-the-art channel estimation schemes require inversion of a high-dimensional channel covariance matrix, which is practically infeasible for dense CF-DMIMO networks owing to the requirement of large storage and high dimensional computational complexity. In this work, we investigate channel estimation problem for a CF-DMIMO network, where both terrestrial and aerial users are jointly supported by distributed access points. We formulate the problem of estimating channel coefficients from the received in-phase/quadrature (I/Q) samples as a non-linear regression problem and propose two deep-learning aided channel estimation schemes for the considered network, namely, deep model-agnostic neural network (DMANN) and deep successive contamination cancellation (DSCC) schemes. Compared to the state-of-the-art channel estimation schemes for CF-DMIMO networks, the proposed schemes (i) tackle the unavoidable pilot contamination issue in dense CF-DMIMO networks while estimating the channel gains for both terrestrial and aerial users; (2) does not require prior knowledge of signal-to-noise ratios; and (3) works well in the presence of non-Gaussian correlated noise. Simulation results demonstrate the effectiveness of the proposed schemes over state-of-the-art channel estimation schemes in various use cases of the CF-DMIMO networks.

I. Introduction

The cell-centric paradigm of wireless networks, which is limited by inter-cell interference, is evolving toward a ubiquitous cell-free (CF) architecture that is more user-centric and robust to interference while providing users with macro-diversity [1]. In the CF distributed multiple input multiple output (CF-DMIMO) systems, many geographically distributed access points (APs) employing single or multiple antennas simultaneously serve a limited number of user equipment (UE) in a time-division duplex (TDD) scheme with the help of a fronthaul network and a central processing unit (CPU) operating in the same time-frequency resource. The CPU transmits data and resource control information to the APs through the downlink, while the APs transmit data received from the UEs using the uplink to the CPU via the fronthaul connection. CF-DMIMO essentially integrates the best attributes of ultra-dense networks, coordinated multi-point transmission, and cellular MIMO, and achieves improved spectral efficiency and transmission reliability by leveraging robust channel estimation and favorable propagation characteristics arising from the exploitation of a large number of propagation paths [2]. CF-DMIMO is therefore a key enabler of the emerging high-throughput, ultra-reliable, and low-latency applications of sixth generation (6G) networks [3].