Configurable Surface Channel Estimation for RIS Mobility Tracking
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Solution Overview
Problem
Channel estimation in reconfigurable intelligent surface (RIS)-aided mmWave communication systems is challenging due to the passive nature of RIS elements and the complexity introduced by multiple-input multiple-output (MIMO) systems, particularly in scenarios with non-stationary user equipment (UE) and non-line-of-sight conditions.
Innovation Solution
A three-stage framework for channel estimation involving hierarchical beam searching to estimate the BS-RIS channel, iterative reweighting for the RIS-UE channel, and channel tracking using algorithms like Extended Kalman Filter (EKF) and Least Mean Square (LMS) to accurately determine channel characteristics and adapt to UE mobility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beamforming search is performed to obtain trained reflection coefficients and AoA, then channel estimation accuracy is improved, but estimation overhead and computational complexity increase
Solution Approach 1:
The channel estimation process is divided into three distinct stages: (1) beamforming search to obtain trained reflection coefficients and AoA, (2) estimation of ideal channel characteristics using the trained parameters, and (3) calculation of actual channel characteristics using a relation between trained and estimated reflection coefficients. This segmentation allows each stage to be optimized independently and reduces overall computational complexity by breaking down the complex estimation task into manageable components.
Solution Approach 2:
The method performs preliminary beamforming search and obtains trained reflection coefficients before performing the actual channel estimation. By conducting the beamforming search in advance and storing the trained reflection coefficients, the system reduces the computational burden during subsequent channel estimation operations, as the preliminary beamforming results can be reused.
2Reliability
If multiple reflection coefficients are estimated for RIS elements, then channel characteristics are improved, but processing time and computational load increase
Solution Approach 1:
The system dynamically adapts the channel estimation process based on the specific characteristics of the RIS channel. By using the relation between trained and estimated reflection coefficients, the system can efficiently estimate channel characteristics without requiring exhaustive estimation of all possible reflection coefficient combinations, thus reducing processing time while maintaining reliability.
Solution Approach 2:
The method transforms the channel estimation problem by changing the parameters being estimated. Instead of directly estimating all channel parameters simultaneously, the system estimates reflection coefficients and AoA separately, then uses a mathematical relation to derive the final channel characteristics. This parameter transformation reduces the dimensionality of the estimation problem and decreases processing time.
3Productivity
If RIS elements are configured with multiple reflection coefficients, then communication performance is improved, but system complexity and overhead increase
Solution Approach 1:
The channel estimation framework is designed to be universally applicable to RIS-aided communication systems with varying configurations. The same three-stage estimation process works for different numbers of RIS elements, reflection coefficient configurations, and channel conditions, reducing system complexity by using a unified approach rather than requiring separate estimation methods for different system configurations.
Solution Approach 2:
The system uses the trained reflection coefficients from beamforming search as a template or copy to estimate the actual channel characteristics. By copying the structure and using the mathematical relation between trained and estimated reflection coefficients, the system avoids the need for completely independent estimation of all channel parameters, thus reducing overall system complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables reliable channel estimation and tracking in mmWave RIS-MIMO systems, improving communication performance by aligning reflected signals constructively and compensating for phase shifts, thereby enhancing signal reception quality.
Implementation Method 1
The elements of RIS can reflect, refract, absorb, or focus the incoming waves toward any desired direction
Implementation Method 2
The elements of RIS can reflect, refract, absorb, or focus the incoming waves toward any desired direction
Implementation Method 3
The elements of RIS can reflect, refract, absorb, or focus the incoming waves toward any desired direction
Implementation Method 4
The elements of RIS can reflect, refract, absorb, or focus the incoming waves toward any desired direction
Data Source
AI summary
The present disclosure relates to channel estimation, at a receiving device of a communication system employing a (re)configurable surface. The channel estimation includes beamforming search to obtain trained reflection coefficients of the configurable surface and an angle of arrival, AoA, of the signals at the receiving device. Then, based on the configurable surface and the obtained AoA at the receiving device, reflection coefficients of the configurable surface are derived for an ideal channel portion between the transmitting device and the configurable surface. According to a relation between the trained reflection coefficients and the estimated reflection coefficients, the estimation of the characteristics of a channel between the transmitting device and the configurable surface is performed. The channel estimation may be employed in user mobility tracking.