[Paper Review] Transforming Fading Channel from Fast to Slow: Intelligent Refracting Surface Aided High-Mobility Communication

Zixuan Huang, Beixiong Zheng+1 more|arXiv (Cornell University)|Jun 4, 2021

Advanced Wireless Communication Technologies4 citations

TL;DR

This paper proposes a two-stage transmission protocol for intelligent refracting surface (IRS)-assisted high-mobility communication, leveraging quasi-static user-IRS and line-of-sight (LoS) BS-IRS channels to enable efficient channel estimation and passive beamforming. The method transforms a fast-fading channel into a slow-fading one, achieving full IRS passive beamforming gain with significantly reduced training overhead.

ABSTRACT

Intelligent reflecting/refracting surface (IRS) has recently emerged as a promising solution to reconfigure wireless propagation environment for enhancing the communication performance. In this paper, we study a new IRS-aided high-mobility communication system by employing the intelligent refracting surface with a high-speed vehicle to aid its passenger's communication with a remote base station (BS). Due to the environment's random scattering and vehicle's high mobility, a rapidly time-varying channel is typically resulted between the static BS and fast-moving IRS/user, which renders the channel estimation for IRS with a large number of elements more challenging. In order to reap the high IRS passive beamforming gain with low channel training overhead, we propose a new and efficient transmission protocol to achieve both IRS channel estimation and refraction optimization for data transmission. Specifically, by exploiting the quasi-static channel between the IRS and user both moving at the same high speed as well as the line-of-sight (LoS) dominant channel between the BS and IRS, the user first estimates the LoS component of the cascaded BS-IRS-user channel, based on which IRS passive refraction is designed to maximize the corresponding IRS-refracted channel gain. Then, the user estimates the resultant IRS-refracted channel as well as the non-IRS-refracted channel for setting an additional common phase shift at all IRS refracting elements so as to align these two channels for maximizing the overall channel gain for data transmission. Simulation results show significant performance improvement of the proposed design as compared to various benchmark schemes. The proposed on-vehicle IRS system is further compared with a baseline scheme of deploying fixed intelligent reflecting surfaces on the roadside to assist high-speed vehicular communications, which achieves significant rate improvement.

Motivation & Objective

• Address the challenge of high training overhead and rapid channel variation in IRS-aided high-mobility vehicular communications.
• Overcome the difficulty of CSI acquisition in fast-fading environments due to high user mobility and random scattering.
• Enable efficient passive beamforming with minimal channel training by exploiting quasi-static user-IRS and LoS BS-IRS links.
• Achieve full IRS passive beamforming gain in high-mobility scenarios while stabilizing the overall channel for reliable transmission.

Proposed method

• Proposes a two-stage protocol: Stage I estimates the LoS component of the cascaded BS-IRS-user channel using pilot signals.
• In Stage I, IRS passive refraction is optimized to maximize the IRS-refracted channel gain based on the estimated LoS component.
• Stage II estimates both the IRS-refracted and non-IRS-refracted channels to set a common phase shift across all IRS elements.
• The common phase shift aligns the two channels to maximize the overall channel gain for data transmission.
• Utilizes spatial correlation and subsurface grouping to reduce training overhead by estimating effective channels per sub-surface.
• Employs gradient-based optimization for IRS phase shifts using the channel gain expression involving beamforming vectors and phase parameters.

Experimental results

Research questions

• RQ1 How can channel estimation be efficiently performed in high-mobility IRS-aided systems with rapidly time-varying channels?
• RQ2 Can the fast-fading nature of the BS-user link be mitigated through intelligent IRS reconfiguration?
• RQ3 What is the optimal two-stage protocol to achieve full passive beamforming gain with minimal training overhead?
• RQ4 How does exploiting the quasi-static user-IRS channel improve training efficiency compared to conventional IRS systems?
• RQ5 To what extent does the proposed method convert a fast-fading channel into a slow-fading one in high-mobility scenarios?

Key findings

• The proposed two-stage protocol achieves full IRS passive beamforming gain in high-mobility scenarios despite rapidly time-varying channels.
• The overall BS-user channel is transformed from fast fading to slow fading, enabling more reliable data transmission.
• Simulation results show significant rate improvement over a baseline roadside IRS scheme due to drastically reduced channel training time.
• The method effectively leverages the quasi-static user-IRS channel and LoS dominant BS-IRS link to minimize training overhead.
• The Cramér-Rao bound (CRB) analysis confirms the accuracy of phase shift estimation, with CRB values derived for phase parameters ψx and ψy.
• The proposed scheme outperforms fixed roadside IRS deployments in high-speed vehicular environments due to reduced feedback and training requirements.