RIS Phase Shift Selection Without Channel Knowledge
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Solution Overview
Problem
Existing methods for optimizing reconfigurable intelligent surfaces (RIS) in wireless systems face challenges in determining the optimal configuration without channel knowledge, leading to increased energy consumption and hardware costs, and are susceptible to blockage and absorption loss, particularly in massive MIMO and mmWave systems.
Innovation Solution
A probabilistic method using a Bayesian approach with statistical learning to estimate the optimal phase shifts of RIS, selecting codewords based on a posteriori probability distributions and acquisition functions to maximize achievable rate, independent of channel or codebook type knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If blind methods are used to estimate optimal RIS configuration without channel knowledge, then channel acquisition challenges are addressed, but the size of pilots required increases and measurement precision deteriorates
Solution Approach 1:
The patent applies preliminary action by performing probabilistic inference and statistical learning in advance to estimate channel parameters before actual communication occurs. The base station pre-computes probability distributions of channel states and uses these to select optimal RIS configurations, thereby reducing the need for extensive pilot signals during actual operation.
Solution Approach 2:
The patent introduces probabilistic distributions and statistical learning models as intermediaries between the RIS and the communication system. Instead of directly measuring channels with large pilots, the system uses probabilistic models to infer channel characteristics, acting as an intermediary that reduces measurement requirements while maintaining estimation accuracy.
2Use of energy by moving object
If probabilistic methods with statistical learning are used to estimate optimal phase shifts, then channel knowledge requirement is reduced, but computational complexity increases
Solution Approach 1:
The patent segments the computational task by dividing it into distinct modules: probabilistic inference for channel parameter estimation, statistical learning for pattern recognition, and optimization algorithms for RIS configuration selection. This segmentation allows each module to be optimized independently and facilitates parallel processing, reducing overall computational complexity.
Solution Approach 2:
The patent changes parameters by transforming the optimization problem from direct channel measurement to probabilistic parameter estimation. By working with probability distributions and statistical parameters rather than raw channel measurements, the system reduces computational burden while maintaining energy efficiency goals.
3Productivity
If massive MIMO systems are deployed to meet 5G KPIs, then system performance is improved, but energy consumption and hardware cost increase
Solution Approach 1:
The patent makes the RIS a multi-functional element that serves multiple purposes: it acts as a passive reflector for signal propagation, an intelligent beamformer for directional communication, and a channel estimation assistant for reducing pilot overhead. This universality allows the system to achieve massive MIMO performance without proportionally increasing active hardware and energy consumption.
Solution Approach 2:
The patent employs passive reflecting elements in the RIS that are low-cost and consume minimal energy compared to active antennas in traditional MIMO systems. These passive elements can be deployed in large numbers to achieve spatial diversity and beamforming effects without the high energy cost of active RF chains, effectively providing a cost-effective alternative to full massive MIMO deployment.
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
The method effectively estimates phase shifts that maximize achievable rate, is robust to white noise, and reduces the need for channel knowledge, improving energy efficiency and reducing hardware costs in wireless systems.
Implementation Method 1
the signal at the reconfigurable intelligent surface being transformed by a matrix function of phase shifts introduced by the reconfigurable intelligent surface
Data Source
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AI summary
The disclosed invention relates to a method for optimizing the use of reconfigurable intelligent surfaces in a wireless system, the system comprising at least a base station including several antennas configured to transmit to and receive data from at least one user equipment including at least one antenna, the system further comprising at least one reconfigurable intelligent surface including several reflective elements for establishing the data transmission, a channel being formed between the base station and the reconfigurable intelligent surface, and a channel being formed between the user equipment and the reconfigurable intelligent surface, said channels being function of the angle of arrival and of departure of paths between the reconfigurable intelligent surface and the base station or the user equipment, the signal at the reconfigurable intelligent surface being transformed by a matrix function of phase shifts introduced by the reconfigurable intelligent surface.