High Dimensional Channel Statistics Conversion via PADS
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Solution Overview
Problem
Existing communication systems face challenges in efficiently converting high dimensional channel statistics across different frequency bands, particularly in frequency division duplex (FDD) systems where channel reciprocity does not hold.
Innovation Solution
A method and device for calculating high dimensional channel characteristics in one frequency band, estimating a power angle delay spectrum (PADS), determining a domain conversion matrix, and generating high dimensional channel characteristics in another frequency band, utilizing the estimated PADS and domain conversion matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If channel reciprocity is used in FDD systems to obtain channel information for downlink from uplink measurements, then channel state information can be obtained without additional measurements, but channel reciprocity does not hold for high dimensional channel statistics across different carrier frequencies
Solution Approach 1:
The patent introduces a domain conversion matrix as an intermediary to bridge the gap between uplink and downlink channel statistics. This matrix transforms the uplink channel covariance matrix into the downlink channel covariance matrix by incorporating frequency-dependent phase rotation and spatial correlation components, thereby enabling accurate channel state information acquisition without direct downlink measurements.
Solution Approach 2:
The patent changes the parameters of the channel statistics representation by transforming from the original frequency-domain channel covariance to a converted domain that accounts for frequency differences between uplink and downlink. The domain conversion matrix incorporates frequency offset parameters and spatial correlation parameters to adapt the channel statistics to different carrier frequencies.
2Measurement precision
If high dimensional channel statistics are processed directly in the original domain, then complete channel information is captured, but computational complexity becomes prohibitively large
Solution Approach 1:
The patent transforms the high dimensional channel statistics from the original frequency-domain representation to a converted domain using domain conversion matrices. This dimensional transformation reorganizes the data structure to separate frequency-dependent and spatial components, reducing the computational complexity while preserving the essential channel characteristics through the transformed covariance matrix.
Data Source
AI summary
A method includes calculating first high dimensional channel characteristics of a first frequency band in accordance with measurements of the first frequency band; estimating a power angle delay spectrum (PADS) in accordance with the first high dimensional channel characteristics of the first frequency band; determining a domain conversion matrix for a second frequency band in accordance with the PADS; and generating second high dimensional channel characteristics of the second frequency band in accordance with the domain conversion matrix and the first high dimensional channel characteristics of the first frequency band.