Two-Timescale Channel Estimation for Reconfigurable Intelligent Surface Aided Wireless Communications (1912.07990v2)

Abstract: Channel estimation is challenging for the reconfigurable intelligent surface (RIS)-aided wireless communications. Since the number of coefficients of the cascaded channel among the base station (BS), the RIS and the user equipments (UEs) is the product of the number of BS antennas, the number of RIS elements, and the number of UEs, the pilot overhead can be prohibitively high. In this paper, we propose a two-timescale channel estimation framework to exploit the property that the BS-RIS channel is high-dimensional but quasi-static, while the RIS-UE channel is mobile but low-dimensional. Specifically, to estimate the quasi-static BS-RIS channel, we propose a dual-link pilot transmission scheme, where the BS transmits downlink pilots and receives uplink pilots reflected by the RIS. Then, we propose a coordinate descent-based algorithm to recover the BS-RIS channel. Since the quasi-static BS-RIS channel is estimated less frequently than the mobile channel be, the average pilot overhead can be reduced from a long-term perspective. Although the mobile RIS-UE channel has to be frequently estimated in a small timescale, the associated pilot overhead is low thanks to its low dimension. Simulation results show that the proposed two-timescale channel estimation framework can achieve accurate channel estimation with low pilot overhead.

• The paper introduces a two-timescale estimation framework separating quasi-static BS-RIS and dynamic RIS-UE channels to significantly reduce pilot overhead.
• It employs a dual-link pilot transmission with a coordinate descent algorithm, achieving lower NMSE compared to traditional MVU methods.
• The framework promises efficient RIS deployments in next-generation networks by enhancing spectral efficiency and estimation accuracy.

Two-Timescale Channel Estimation for RIS-Aided Wireless Communications

This paper explores a significant advancement in channel estimation for reconfigurable intelligent surface (RIS) aided wireless communications by introducing a novel two-timescale channel estimation framework. The inherent challenge in RIS-aided systems is estimating the high number of channel coefficients present in the cascaded channel between the base station (BS), the RIS, and the user equipment (UEs). Due to the involvement of multiple components—BS antennas, RIS elements, and UEs—the pilot overhead required for channel estimation is typically immense. This research addresses these challenges by leveraging the distinct two-timescale properties of the channel and proposes methods to significantly reduce pilot overheads while maintaining accurate channel estimation.

Key Contributions

1. Exploitation of Two-Timescale Channel Properties:
   • The paper identifies that the BS-RIS channel is high-dimensional but quasi-static since both the BS and RIS are fixed, whereas the RIS-UE channel is mobile but low-dimensional due to the mobility of UEs.
   • This dual characteristic is utilized to develop a two-timescale estimation process whereby the quasi-static BS-RIS channel is estimated over a large timescale and the dynamic RIS-UE channel over a smaller timescale.
2. Dual-Link Pilot Transmission and Channel Estimation Algorithm:
   • The paper proposes a dual-link pilot transmission scheme to effectively estimate the high-dimensional quasi-static BS-RIS channel. This entails BS transmitting downlink pilots and receiving uplink pilots reflected by the RIS, using a novel coordinate descent-based algorithm to recover the BS-RIS channel.
   • The data demonstrates that while the quasi-static BS-RIS channel is less frequently estimated, leading to reduced overhead, the mobile RIS-UE channel requires frequent but low-dimensional updates, resulting in a more efficient overhead over time.
3. Implementation and Complexity:
   • The pilot transmission and channel estimation are designed to handle systems with large numbers of elements efficiently, demonstrated through simulations that show reduced mean square error (MSE) in channel estimation and lower pilot overhead compared to existing state of the art methods.

Numerical Results and Implications

The simulation results highlight that the proposed framework achieves a reduced pilot overhead compared to traditional full-cascade channel estimation methods such as the minimum variance unbiased (MVU) estimator, particularly under high-dimensional scenarios. Moreover, the normalized mean square error (NMSE) results reveal that the proposed framework provides more accurate estimates than existing multi-user channel estimation techniques. This performance gain is achieved by taking advantage of the unique two-timescale nature of these channel segments, thereby aligning estimation frequency and resource investment with actual channel dynamics.

Theoretical and Practical Implications

This framework presents significant theoretical implications by providing a methodical approach to exploit channel properties in RIS-assisted communication, paving the way for the integration of RIS in 6G networks. Practically, the reduced pilot overhead directly translates into more efficient use of spectral resources, enhancing overall system throughput and performance. Future developments may focus on extending this framework to adapt to other wireless communication environments and exploring additional methods for differential channel modeling and estimation that could further optimize the trade-off between accuracy and overhead in varying conditions.

In conclusion, the paper offers a sophisticated approach for channel estimation in RIS-aided communications, balancing the need for precision with the constraints of overhead. Its findings are poised to impact future RIS deployments and their integration into next-generation wireless networks by supplementing our current methodologies with a more strategic and resource-efficient framework.