Channel Estimation and Passive Beamforming for Intelligent Reflecting Surface: Discrete Phase Shift and Progressive Refinement (1912.10646v2)

Abstract: Prior studies on Intelligent Reflecting Surface (IRS) have mostly assumed perfect channel state information (CSI) available for designing the IRS passive beamforming as well as the continuously adjustable phase shift at each of its reflecting elements, which, however, have simplified two challenging issues for implementing IRS in practice, namely, its channel estimation and passive beamforming designs both under the constraint of discrete phase shifts. To address them, we consider in this paper an IRS-aided single-user communication system with discrete phase shifts and design the IRS training reflection matrix for channel estimation as well as the passive beamforming for data transmission, both subject to the constraint of discrete phase shifts. We show that the training reflection matrix design for discrete phase shifts greatly differs from that for continuous phase shifts, and thus the corresponding passive beamforming should be optimized by taking into account the correlated channel estimation error due to discrete phase shifts. Specifically, we consider a practical block-based transmission, where each block has a finite (insufficient) number of training symbols for channel estimation. A novel hierarchical training reflection design is proposed to progressively estimate IRS elements' channels over multiple blocks by exploiting IRS-elements grouping and partition. Based on the resolved IRS channels in each block, we further design the progressive passive beamforming at the IRS with discrete phase shifts to improve the achievable rate for data transmission over the blocks.

• The paper presents a hierarchical channel estimation framework that uses discrete phase shifts and a successive refinement algorithm to enhance passive beamforming.
• It leverages realistic IRS modeling by grouping elements and employing block-based training to progressively estimate channels under practical constraints.
• Extensive numerical results validate significant performance gains, highlighting the method’s potential for efficient and cost-effective wireless deployments.

Channel Estimation and Passive Beamforming for Intelligent Reflecting Surface: Discrete Phase Shift and Progressive Refinement

The paper discusses the challenges of implementing Intelligent Reflecting Surfaces (IRS) in wireless communication systems, particularly focusing on channel estimation and passive beamforming with discrete phase shifts. IRS has emerged as a promising technology to enhance wireless communication by dynamically altering the phase and amplitude of reflected signals to improve signal quality and energy efficiency. The innovation is particularly appealing because IRS can significantly reduce hardware costs and energy consumption compared to traditional solutions that involve active components.

Key Contributions

1. Practical Setup Modeling: The authors consider an IRS-aided communication system with a single-user. The setup models realistic constraints found in practical implementations, including the deployment of IRS phase shifters with discrete, rather than continuous, phase shifts. This consideration marks a departure from many past studies that assumed perfect Channel State Information (CSI) with continuous phase shift capabilities, which simplifies the intricacy of real-world constraints.
2. Channel Estimation with Discrete Phase Shifts: The paper proposes a hierarchical channel estimation framework using block-based transmission. Each block contains a small number of training symbols. The design uses two vectors for IRS training, a basis training reflection vector and an intra-group training reflection vector. These progressively estimate the individual IRS elements' channels over multiple blocks. This constitutes a significant step for IRS channel estimation under practical, discrete phase shift conditions.
3. Proposed Reflection Design: The authors present a novel hierarchical training reflection design that optimizes channel estimation by leveraging IRS elements' groupings and partitions. The performance improvement is linked to effectively resolving IRS channels progressively over time, thereby enhancing the potential data transmission capability.
4. Successive Refinement Algorithm: The paper introduces a low-complexity algorithm to design passive beamforming for IRS. The iterative refinement process seeks to optimize rate achievable with discrete phase shifts, thereby aligning the reflection coefficient settings for IRS with the estimated channels effectively. This method offers computational advantages, making the solution applicable to real-world system requirements.
5. Extensive Numerical Results: The paper supports the proposed methodologies with extensive simulation results. These results demonstrate a notable performance enhancement over existing benchmark methods, showcasing the efficiency of IRS channel estimation and passive beamforming with discrete phase adjustment.

Implications and Future Directions

The implications of this research are twofold: theoretical and practical. From a theoretical standpoint, it provides a framework for progressively refining channel estimation for systems implementing IRS. Practically, this research has significant implications for real-world deployment, as it addresses the limitations of relying on continuous phase shift estimations.

The progressive refinement approach allows for IRS technology to be incrementally adopted and optimized, which aids in integrative deployment strategies for future wireless networks. As IRS continues to develop, future research could explore multi-user and multi-antenna scenarios further, potentially expanding the scope of IRS in various communication environments. Additionally, research into adaptive algorithms that can handle dynamic changes in wireless environments could significantly benefit from this foundational work.

Overall, the research presents practical solutions for evolving technologies and offers a valuable approach to improving wireless communications via IRSs under realistic constraints. This work aligns with the ongoing implementation of advanced wireless communication systems aiming to achieve higher data rates and reduce latency and energy consumption.