# Joint Channel Estimation and User Grouping for Massive MIMO Systems

arXiv: https://arxiv.org/abs/1804.09295 · HTML: https://mlsanity.com/paper/1804.09295/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults with Application to Multistation Assembly Systems](https://mlsanity.com/paper/2310.16058/) (2023)

## Abstract (arXiv)

This paper addresses the problem of joint downlink channel estimation and user grouping in massive multiple-input multiple-output (MIMO) systems, where the motivation comes from the fact that the channel estimation performance can be improved if we exploit additional common sparsity among nearby users. In the literature, a commonly used group sparsity model assumes that users in each group share a uniform sparsity pattern. In practice, however, this oversimplified assumption usually fails to hold, even for physically close users. Outliers deviated from the uniform sparsity pattern in each group may significantly degrade the effectiveness of common sparsity, and hence bring limited (or negative) gain for channel estimation. To better capture the group sparse structure in practice, we provide a general model having two sparsity components: commonly shared sparsity and individual sparsity, where the additional individual sparsity accounts for any outliers. Then, we propose a novel sparse Bayesian learning (SBL)-based framework to address the joint channel estimation and user grouping problem under the general sparsity model. The framework can fully exploit the common sparsity among nearby users and exclude the harmful effect from outliers simultaneously. Simulation results reveal substantial performance gains over the existing state-of-the-art baselines.