1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
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 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.
© Copyright 2019 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.