Sensing-Aided OTFS Channel Estimation for Massive MIMO
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Massive MIMO-OTFS systems face high pilot signaling overhead, especially in scenarios with large delay and Doppler spreads, which hinders their ability to support highly-mobile applications like augmented/virtual reality and autonomous vehicles, due to the scaling of overhead with the number of antennas and channel spreads.
Innovation Solution
The approach leverages radar sensing information to aid OTFS channel estimation by extracting delay, Doppler, and angle of departure information, using a sensing-aided sparse recovery algorithm that reduces pilot overhead and improves channel estimation performance, integrating radar sensing with communication systems to infer propagation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pilot-based channel estimation is used in massive MIMO-OTFS systems, then channel estimation accuracy is maintained, but pilot signaling overhead scales excessively with the number of antennas and channel spreads
Solution Approach 1:
The patent performs preliminary channel characterization by collecting channel statistics (delay spread, Doppler spread, angle spreads) before actual channel estimation. This preliminary information is used to configure compressed sensing parameters and reduce the search space, enabling accurate channel estimation with fewer pilots.
Solution Approach 2:
The patent changes the domain of channel representation from time-frequency to delay-Doppler-angle domain, where the channel exhibits sparsity. By transforming the problem into this sparse domain and using channel statistics to guide the transformation, the system achieves accurate estimation with reduced pilot overhead.
2Quantity of substance
If compressed sensing approaches are used to reduce pilot overhead, then pilot overhead is reduced, but channel estimation accuracy deteriorates in scenarios with large delay and Doppler spreads
Solution Approach 1:
The system performs preliminary measurement of channel statistics (delay spread στ, Doppler spread σν, angle spreads σθ) before compressed sensing. These statistics are used to configure the compressed sensing algorithm parameters, including the transformation matrices and sparsity constraints, ensuring accurate recovery even in challenging scenarios with large spreads.
Solution Approach 2:
The patent applies different processing strategies to different domains based on local channel characteristics. The channel estimation is performed in the delay-Doppler-angle domain where sparsity is maximized, while using time-frequency domain processing where appropriate. This localized approach maintains accuracy across diverse channel conditions.
3Reliability
If more pilots are transmitted to improve channel estimation in highly-mobile scenarios, then estimation reliability improves, but system resource consumption increases
Solution Approach 1:
The patent changes the representation parameters of the channel by exploiting its inherent sparsity in the delay-Doppler-angle domain. By formulating channel estimation as a sparse recovery problem and using channel statistics to guide the sparsity promotion, reliable estimation is achieved with minimal pilots, significantly reducing system resource consumption.
Solution Approach 2:
The patent replaces the traditional pilot-based mechanical measurement approach with a compressed sensing-based intelligent inference approach. Instead of densely sampling the channel with pilots, the system uses statistical information and sparsity constraints to infer channel characteristics, reducing energy consumption while maintaining reliability.
Data Source
AI summary
A computer system is disclosed that is configured to perform a method that includes receiving one or more radar data frames from one or more antennas of a base station or a user equipment device in an environment; processing the one or more radar data frames to identify one or more attributes of one or more static objects and one or more dynamic objects in the environment; and estimating one or more channels for the user equipment device and the base station based on the one or more attributes of the one or more static objects and the one or more dynamic objects.