Name
Title | ||
|---|---|---|
Channel Estimation of IRS-Aided Communication Systems with Hybrid Multiobjective Optimization |
Abstract | ||
|---|---|---|
In this paper, we propose a compressive channel estimation technique for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel estimation are converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and a sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed algorithm achieves competitive error performance compared to existing channel estimation algorithms. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICC42927.2021.9500433 | IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021) |
Keywords | DocType | ISSN |
Intelligent reflecting surface (IRS), channel estimation, millimeter wave communications, compressed sensing, hybrid evolutionary algorithm | Conference | 1550-3607 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Chen | 1 | 5 | 1.42 |
| Jie Tang | 2 | 89 | 10.90 |
| Hengbin Tang | 3 | 4 | 1.78 |
| Xiu Yin Zhang | 4 | 143 | 27.26 |
| Daniel K. C. So | 5 | 226 | 25.73 |
| Kai-Kit Wong | 6 | 3777 | 281.90 |