Compressed Sensing Joint Channel Estimation in Multi-User MIMO
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In large scale multi-user MIMO LTE networks, especially in C-RAN settings, conventional channel estimation methods become under-determined as the number of users exceeds the number of training signal observations, leading to poor joint channel estimation and impaired downlink beam forming and precoding performance.
Innovation Solution
The method involves receiving training signals from multiple users, estimating a maximum delay spread, forming a well-conditioned low rank training matrix, selecting and estimating active taps, and iteratively subtracting their contributions from the data set until the residual signal norm falls below a minimum, exploiting channel sparsity to stabilize the estimation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If joint channel estimation is performed for multiple users, then channel estimation accuracy is improved, but the system becomes under-determined when the number of users exceeds the number of training signal observations
Solution Approach 1:
The patent changes the parameter of channel representation from full channel matrix to sparse channel taps in time domain. By transforming the channel estimation problem into the time domain and exploiting the sparsity of active taps, the system can estimate channels for more users than training observations without becoming under-determined.
Solution Approach 2:
The patent segments the channel estimation problem by identifying and estimating only the active taps (significant channel coefficients) rather than estimating all channel parameters. This segmentation approach reduces the number of unknowns and allows the system to handle more users than training signals.
2Quantity of substance
If the number of users increases beyond the number of training signal observations, then system capacity is improved, but the training matrix becomes ill-conditioned and joint channel estimation performance deteriorates
Solution Approach 1:
The patent transforms the channel estimation from frequency domain to time domain, where the channel can be represented as sparse taps. This parameter transformation allows the system to maintain reliable estimation even when users exceed training observations, by focusing on the essential sparse components rather than the full channel matrix.
Solution Approach 2:
The patent extracts only the significant active taps from the full channel matrix, discarding the negligible components. By taking out only the essential channel information (active taps), the system can reliably estimate channels for a large number of users even with limited training observations.
Data Source
AI summary
Methods and systems for performing compressed time domain joint channel estimation in a multi-user MIMO wireless network include receiving data corresponding to transmission of training signals from a plurality of users to a base station over a MIMO wireless network, determining a limited data set by limiting the received data in a time domain according to an estimated maximum delay spread, forming a well-conditioned low rank training matrix by identifying a channel model, estimating an active tap from the formed well-conditioned low rank training matrix, and subtracting a contribution of the selected active tap from the limited data set.