MIMO Channel Feedback via Sparse Basis Transformation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In massive MIMO systems with high space correlation, existing channel feedback methods are inefficient, leading to high overhead and complexity due to the need for explicit feedback of pre-coding matrix indicators from multiple users to the base station.
Innovation Solution
A method and apparatus that compress high-dimensional channel matrices using sparsifying basis functions, transforming channel information into a sparse domain, and feeding back a compressed codebook index to reduce overhead and complexity, allowing for effective channel feedback in multi-antenna systems with high space correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit feedback of pre-coding matrix indicators is used in massive MIMO systems, then channel information accuracy is improved, but feedback overhead and system complexity increase significantly
Solution Approach 1:
The patent extracts only the essential channel information characteristics needed for massive MIMO operation by transforming the channel matrix into a sparse representation. Instead of feeding back the complete channel matrix or traditional pre-coding matrix indicators, the system extracts sparse coefficients that capture the dominant channel features, significantly reducing feedback overhead while preserving accuracy.
Solution Approach 2:
The patent changes the representation parameters of channel information from traditional dense matrix form to a sparse domain representation. By applying sparsifying basis functions and selecting only significant coefficients, the system transforms the parameter structure to reduce dimensionality while maintaining the essential information needed for accurate channel state feedback.
2Reliability
If traditional channel feedback methods are used in high space correlation systems, then channel state information is maintained, but computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the channel matrix into sparse components by applying sparsifying basis functions. This segmentation separates the significant channel information from redundant data, allowing the system to process and maintain channel state information with reduced computational complexity by focusing only on the essential sparse coefficients.
Solution Approach 2:
The patent introduces sparsifying basis functions as an intermediary transformation layer between the channel matrix and the feedback process. This intermediary representation simplifies the computational complexity by providing a compressed domain where channel information can be maintained and processed more efficiently while preserving reliability.
3Device complexity
If channel compression is applied in massive MIMO feedback, then feedback overhead is reduced, but channel information precision may degrade
Solution Approach 1:
The patent applies local quality by preserving the most significant channel coefficients while discarding less important ones. The sparsifying transformation identifies and retains only the dominant channel characteristics, allocating feedback resources to the most critical information components, thereby maintaining precision where it matters most while reducing overall overhead.
Solution Approach 2:
The patent uses partial action by transmitting only a subset of channel coefficients rather than the complete channel state. By selecting and feeding back only the most significant sparse coefficients, the system achieves adequate channel information precision with reduced feedback overhead, applying just enough information to maintain performance.
Data Source
AI summary
An apparatus and a method for feeding a channel back in a wireless communication system using multiple input multiple output antennas (MIMO) are provided. The method includes receiving a signal from a transmitter and configuring a channel matrix for the received signal, configuring a basis transformed sparse channel using a sparsifying basis function with respect to the channel matrix, selecting a channel part to be fed back to the transmitter from the transformed sparse channel, and creating a codebook by quantizing the selected channel part, and feeding a codebook index corresponding to the created codebook back to the transmitter.