Channel State Information Estimation Using Reconfigurable Intelligent Surfaces
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Solution Overview
Problem
Current methods for channel state information (CSI) estimation in mmWave communication systems with fully-passive reconfigurable intelligent surfaces (RIS) face challenges such as high training overhead, power consumption, and inefficiency due to the need for frequent updates in dynamic channel conditions, especially in scenarios with high user mobility and large RISs.
Innovation Solution
A method using a variational inference-based framework to approximate the intractable posterior distribution of CSI, separately estimating the RIS-BS and UE-RIS channels by forming parametrized tractable statistical models, and leveraging the slow-varying property of RIS-BS channels to reduce signaling overhead and improve spectral efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cascaded channel estimation methods are used in RIS-aided mmWave systems, then the channel state information can be obtained, but the training overhead increases significantly due to the large number of channel coefficients that need to be estimated
Solution Approach 1:
The patent segments the cascaded channel estimation problem into two separate estimations: the RIS-BS channel and the UE-RIS channel. By estimating these channels separately rather than estimating the entire cascaded channel matrix, the number of parameters to be estimated is dramatically reduced, thereby lowering the training overhead while maintaining estimation accuracy
Solution Approach 2:
The patent extracts and leverages the slow-varying property of the RIS-BS channel from the overall channel model. By identifying that the RIS-BS channel changes slowly compared to the UE-RIS channel, the system can reuse previous estimates of the RIS-BS channel, further reducing the training overhead required for channel state information acquisition
2Reliability
If frequent channel estimation updates are performed to track dynamic channel conditions, then the channel state information remains current, but the power consumption and computational resources increase
Solution Approach 1:
The patent applies different update strategies to different channel components based on their dynamic characteristics. The UE-RIS channel, which varies rapidly with user mobility, is estimated frequently using uplink training signals. In contrast, the RIS-BS channel, which varies slowly, is estimated less frequently by reusing previous estimates. This dynamic, differentiated approach maintains channel state information accuracy while reducing overall power consumption and computational burden
Solution Approach 2:
The patent changes the estimation frequency parameter for different channel components based on their temporal characteristics. By adjusting how often each channel is re-estimated according to its variability, the system optimizes the balance between maintaining accurate channel state information and minimizing energy consumption for channel estimation operations
3Productivity
If the number of RIS elements is increased to improve coverage and capacity, then the system performance improves, but the number of channel coefficients to estimate increases, making CSI acquisition infeasible within practical coherence time
Solution Approach 1:
The patent segments the channel estimation task by separating the RIS-BS channel from the UE-RIS channel. This segmentation allows the system to handle the large number of RIS elements without requiring estimation of all possible channel coefficients. By estimating only the essential separate channels and leveraging their different temporal properties, the system achieves scalable channel state information acquisition that works effectively even with large numbers of RIS elements
Solution Approach 2:
The patent extracts the slow-varying RIS-BS channel component from the overall channel model and treats it separately from the fast-varying UE-RIS channel. This extraction allows the system to reuse previous RIS-BS channel estimates over multiple coherence intervals, effectively reducing the channel estimation complexity that would otherwise scale with the number of RIS elements, thereby enabling practical CSI acquisition even with large RIS deployments
Data Source
AI summary
A method of obtaining channel state information of a communication channel between a user device and an access point with plural antennas features steps of forming separate statistical models representative of a first channel portion between the access point and a wave transformer located at a geographically intermediate location between the access point and the user device, which is configured to reflect electromagnetic signals between the access point and the user device and has electronically reconfigurable antennas, and a second channel portion between the wave transformer and the user device; and processing, using respective machine learning algorithms configured to determine parameters of a type of tractable statistical distribution selected to represent both the first and second portions of the channel, a transmitted signal so as to form parametrized tractable statistical distributions respectively defining the separate statistical models of the first and second portions of the communication channel.