Name
Title | ||
|---|---|---|
Channel Distribution Learning: Model-Driven GAN-Based Channel Modeling for IRS-Aided Wireless Communication |
Abstract | ||
|---|---|---|
Intelligent reflecting surface (IRS) is a promising new technology that is able to create a favorable wireless signal propagation environment by collaboratively reconfiguring the passive reflecting elements yet with low hardware and energy cost. In IRS-aided wireless communication systems, channel modeling is a fundamental task for communication algorithm design and performance optimization, which however is also very challenging since in-depth domain knowledge and technical expertise in radio signal propagations are required, especially for modeling the high-dimensional cascaded base station (BS)-IRS and IRS-user channels (also referred to as the reflected channels). In this paper, we propose a model-driven generative adversarial network (GAN)-based channel modeling framework to autonomously learn the reflected channel distribution, without complex theoretical analysis or data processing. The designed GAN (also named as IRS-GAN) is trained to reach the Nash equilibrium of a minimax game between a generative model and a discriminative model. For the single-user case, we propose to incorporate the special structure of the reflected channels into the design of the generative model. While for the multiuser case, we extend the IRS-GAN and present a multiuser IRS-GAN (abbreviated as IRS-GAN-M), where the distributions of the reflected channels associated with different users are learned simultaneously with reduced number of network parameters (as compared to the naive scheme that assigns a dedicated IRS-GAN for each user). Moreover, theoretical analysis is presented to prove that the minimax game in the IRS-GAN-M framework has a global optimum if the generative and discriminative models are given with enough capacity. Simulation results are presented to validate the effectiveness of the proposed IRS-GAN framework. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCOMM.2022.3176316 | IEEE Transactions on Communications |
Keywords | DocType | Volume |
Intelligent reflecting surface (IRS),generative adversarial network (GAN),deep learning,channel modeling,multiuser communications,multiple-output single-input (MISO) | Journal | 70 |
Issue | ISSN | Citations |
7 | 0090-6778 | 0 |
PageRank | References | Authors |
0.34 | 30 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Yi | 1 | 252 | 36.97 |
| Ming-Min Zhao | 2 | 109 | 12.08 |
| Minjian Zhao | 3 | 224 | 34.77 |