Name
Abstract | ||
|---|---|---|
In this paper, we propose a fast channel estimation algorithm for wideband millimeter wave (mmWave) massive MIMO systems, where the hybrid precoding architectures are adopted. Stimulated by the joint sparsity of different subcarriers, a distributed compressed sensing based strategy is presented to reduce the required pilot. overhead. Based on a hierarchical channel model, a fast. sparse Bayesian learning method, which can drastically release the relaxed evidence low bound, is designed to accelerate the convergence rate. Simulation results verify that the proposed algorithm is capable of achieving substantially higher estimation accuracy and convergence rate as compared to other existing Bayesian channel estimation strategies. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/PIMRC.2018.8580782 | 2018 IEEE 29TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC) |
Field | DocType | Citations |
Wideband,Bayesian inference,Computer science,Algorithm,Communication channel,Real-time computing,Rate of convergence,Compressed sensing,Orthogonal frequency-division multiplexing,Precoding,Bayesian probability | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qixuan Zhang | 1 | 0 | 0.34 |
| Zhipeng Lin | 2 | 42 | 13.17 |
| Tiejun Lv | 3 | 669 | 97.19 |