Generative channel estimation for intelligent reflecting surface-aided wireless communication

IF 2.1 4区计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Wireless Networks Pub Date : 2024-03-07 DOI:10.1007/s11276-024-03688-3

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Abstract

Intelligent reflecting surface (IRS) has emerged as a viable technology to enhance the spectral efficiency of wireless communication systems by intelligently controlling wireless signal propagation. In wireless communication governed by the IRS, the acquisition of channel state information (CSI) is essential for designing the optimal beamforming. However, acquiring the CSI is difficult as the IRS does not have radio frequency chains to transmit/receive signals and the capability to process the signals is also limited. The cascaded channel linking the base station (BS) and a user through the IRS does not necessarily adhere to a specific channel distribution. Conventional and deep learning-based techniques for channel estimation face challenges: the pilot overhead and compromised estimation accuracy due to assumptions of prior channel distribution and noisy signal. To overcome these issues a novel generative cascaded channel estimation (GCCE) model based on a generative adversarial network (GAN) is proposed to estimate the cascaded channel. The GGCE model reduces the reliance on pilot signals, effectively minimizing pilot overhead, by deriving CSI from received signal data. To enhance the estimation accuracy, the channel correlation information is provided as a conditioning factor for the GCCE model. Additionally, a denoising network is integrated into the GCCE framework to effectively remove noise from the received signal. These integrations collectively enhance the estimation accuracy of the GCCE model compared to the initial GAN setup. Experimental results illustrate the superiority of the proposed GCCE model over conventional and deep learning techniques when provided with the same pilot count.

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智能反射面辅助无线通信的生成信道估计

摘要智能反射面（IRS）已成为一种可行的技术，可通过智能控制无线信号传播来提高无线通信系统的频谱效率。在由 IRS 控制的无线通信中，获取信道状态信息（CSI）对于设计最佳波束成形至关重要。然而，由于 IRS 没有发射/接收信号的无线电频率链，而且处理信号的能力也有限，因此获取 CSI 十分困难。通过 IRS 连接基站（BS）和用户的级联信道并不一定遵循特定的信道分布。传统的信道估计技术和基于深度学习的信道估计技术都面临着挑战：先导开销和由于假定先验信道分布和噪声信号而降低的估计精度。为了克服这些问题，我们提出了一种基于生成对抗网络（GAN）的新型生成级联信道估计（GCCE）模型来估计级联信道。GGCE 模型从接收到的信号数据中推导出 CSI，从而减少了对先导信号的依赖，有效地降低了先导开销。为了提高估计精度，信道相关信息被作为 GCCE 模型的一个条件因子。此外，GCCE 框架还集成了去噪网络，以有效去除接收信号中的噪声。与最初的 GAN 设置相比，这些集成共同提高了 GCCE 模型的估计精度。实验结果表明，在相同先导计数的情况下，拟议的 GCCE 模型优于传统技术和深度学习技术。

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来源期刊

Wireless Networks 工程技术-电信学

CiteScore

7.70

自引率

3.30%

发文量

314

审稿时长

5.5 months

期刊介绍： The wireless communication revolution is bringing fundamental changes to data networking, telecommunication, and is making integrated networks a reality. By freeing the user from the cord, personal communications networks, wireless LAN''s, mobile radio networks and cellular systems, harbor the promise of fully distributed mobile computing and communications, any time, anywhere. Focusing on the networking and user aspects of the field, Wireless Networks provides a global forum for archival value contributions documenting these fast growing areas of interest. The journal publishes refereed articles dealing with research, experience and management issues of wireless networks. Its aim is to allow the reader to benefit from experience, problems and solutions described.