Blind Estimation of Sparse Multi-User Massive MIMO Channels

TL;DR

This paper introduces a maximum likelihood method for blind estimation of sparse multi-user massive MIMO channels, reducing pilot overhead and improving robustness in dynamic scenarios by exploiting angular domain sparsity.

Contribution

It presents a novel blind estimation approach leveraging channel sparsity, enabling interference separation and robustness without relying on detailed statistical channel models.

Findings

Achieves superior estimation accuracy compared to existing methods.

Reduces pilot overhead significantly at low SNR.

Robust in rapidly changing channel conditions.

Abstract

We provide a maximum likelihood formulation for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure. The main advantage of this approach is the fact that the overhead due to pilot sequences can be reduced dramatically especially when operating at low SNR per antenna. Thereby, the sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user's channels. On the other hand, as only the sparsity is assumed, the proposed method is robust with respect to the statistical properties of the channel and data and allows the estimation in rapidly time-varying scenarios and eventually the separation of interfering users from adjacent base stations. Additionally, a performance limit is derived based on the clairvoyant Cram\'er Rao lower bound. Simulation results demonstrate that this maximum likelihood formulation yields superior estimation accuracy with reasonable computational complexity and limited model assumptions.