THz UM-MIMO Channel Estimation for Widely Spaced Subarrays
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Solution Overview
Problem
Existing channel estimation methods for Ultra-Massive Multiple Input Multiple Output (UM-MIMO) systems at Terahertz (THz) band are inadequate due to the enlarged subarray spacing in Widely-Spaced Multi-Subarray (WSMS) structures, which invalidates planar-wave assumptions and requires accurate antenna-level Channel State Information (CSI) for effective channel estimation.
Innovation Solution
A subarray-based sparse channel representation codebook is proposed, utilizing first and second codebook matrices for characterizing channels between devices with spaced subarrays, combined with low complexity recovery algorithms like STRE and GRE to enhance estimation accuracy and reduce complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Widely-Spaced Multi-Subarray (WSMS) structures are used to increase antenna elements, then channel estimation accuracy deteriorates due to invalidated planar-wave assumptions, but spectral efficiency can be improved with proper characterization
Solution Approach 1:
The antenna array is segmented into multiple subarrays with specific spacing configurations. The channel estimation process is divided into characterizing individual subarrays and then combining their characteristics, allowing accurate modeling of the widely-spaced structure without requiring invalid planar-wave assumptions across the entire array
Solution Approach 2:
The invention changes the fundamental parameters of channel characterization by transitioning from planar-wave assumptions to spherical-wave models that accurately represent the electromagnetic field distribution in WSMS structures. This involves modifying the mathematical models to account for curved wavefronts and distance-dependent phase variations across subarrays
2Measurement precision
If traditional channel estimation methods are used, then computational complexity is low, but estimation accuracy is insufficient for WSMS structures
Solution Approach 1:
The invention performs preliminary characterization of each subarray's channel response before combining them into the complete array model. Recovery algorithms pre-process received signals to extract subarray-specific parameters, which are then synthesized to form the overall channel estimate, reducing the computational burden of direct full-array processing
Solution Approach 2:
The invention introduces intermediary recovery algorithms (STRE and GRE) that act as mediators between the raw received signals and the final channel estimate. These algorithms provide intermediate processing steps that efficiently bridge the gap between simple traditional methods and complex exact solutions, achieving high accuracy with manageable complexity
Data Source
AI summary
Embodiments of the present disclosure disclose devices, methods, apparatuses and computer readable storage media of channel estimation for Ultra-Massive Multiple Input Multiple Output (UM-MIMO) at Terahertz (THz) band. The method comprises obtaining a first codebook matrix associated with a first antenna arrangement at the first device and a second codebook matrix associated with a second antenna arrangement at a second device, wherein a first plurality of subarrays in the first antenna arrangement are spaced at a predetermined distance from each other, and wherein a second plurality of subarrays in the second antenna arrangement are spaced at a predetermined distance from each other, and characterizing a channel between the first device and the second device at least based on the first and the second codebook matrices.