Name
Abstract | ||
|---|---|---|
Recently, channel sparsity has been considered as a nature of wireless channels in many researches of intelligent communications, and an increasing number of investigations are conducted by exploiting sparsity of wireless channels, such as deep-learning-based channel estimation and compressive sensing. Growing evidence from channel measurements show that the sparse or approximate sparse distribution assumption of wireless channel is reasonable, however, the observed sparse structure in wireless channels is mostly based on intuitive analysis. Several fundamental aspects of channel sparsity have not been well investigated, and among them, we find that choosing a reasonable measure of channel sparsity has not been fully addressed. To fill the gap, this paper presents several measures for wireless channel sparsity from propagation view and validates them based on realistic channel measurements and data mining methods. Following the spirit that a sparse representation implies a small number of elements contain a large proportion of the energy, the four measures of the number of multipath components (MPCs), channel degrees of freedom (DoF), the Gini index, and the Ricean K factor are selected as the potential measures of channel sparsity and fully compared, and the channel diversity measure is used as an indicator of channel sparsity to show the interdependency between different measures and channel sparsity. The measurement-based analysis shows that the channel DoF and Gini index provide the best sensitivity and accuracy for measuring channel sparsity, whereas the number of MPCs has the worst performance. Moreover, the widely used channel parameter of Ricean K factor is found to have fairly good sensitivity to channel sparsity and can be used for channel sparsity evaluation. The results in the paper can be used to accurately measure sparsity of wireless channel for the design of intelligent communications. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCCN.2020.3013270 | IEEE Transactions on Cognitive Communications and Networking |
Keywords | DocType | Volume |
Measure of channel sparsity,sparse channels,data mining,wireless channels,intelligent communications | Journal | 7 |
Issue | ISSN | Citations |
1 | 2332-7731 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Zhang | 1 | 9 | 20.42 |
| Ruisi He | 2 | 528 | 55.85 |
| Bo Ai | 3 | 1581 | 185.94 |
| Shuguang Cui | 4 | 521 | 54.46 |
| Haoxiang Zhang | 5 | 0 | 0.34 |