Multi-Antenna Interference Removal via Channel Matrix Decomposition
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Solution Overview
Problem
Existing multi-antenna systems face performance deterioration and transmission power loss due to constant data transfer rates and single-antenna usage per Mobile Station, leading to suboptimal Quality of Service and capacity discrepancies as Signal-to-Noise Ratio increases.
Innovation Solution
The method involves decomposing the channel matrix at the transmitting end to calculate interference signals for each antenna, summing transmission signals, and multiplying by the decomposed matrix to remove interference, allowing for optimized data transfer rates and transmission power allocation across multiple antennas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If constant data transfer rate and single-antenna usage per Mobile Station are employed, then implementation simplicity is maintained, but channel capacity and Quality of Service deteriorate
Solution Approach 1:
The channel matrix is decomposed into multiple components through QR decomposition, separating the transmission space into orthogonal subspaces. This segmentation allows different Mobile Stations to utilize different spatial layers, enabling multi-antenna usage while maintaining manageable computational complexity through structured matrix operations.
Solution Approach 2:
The patent transitions from single-antenna transmission to multi-antenna spatial multiplexing by adding the spatial dimension. Through QR decomposition of the channel matrix, the system creates orthogonal spatial layers that allow multiple data streams to be transmitted simultaneously across multiple antennas, effectively utilizing the spatial dimension to increase channel capacity.
2Ease of operation
If constant data transfer rate is used for each Mobile Station, then system control is simplified, but transmission power optimization and Quality of Service are compromised
Solution Approach 1:
The patent introduces dynamic data transfer rates for each Mobile Station based on channel conditions and spatial layer allocation. The QR decomposition enables the system to adaptively assign different numbers of spatial layers to different Mobile Stations, allowing dynamic optimization of data rates and transmission power while maintaining Quality of Service through flexible resource allocation.
3Device complexity
If linear pre-coding schemes (ZF or MMSE) are employed, then transmitting end implementation is simplified, but transmission power loss and performance deterioration occur
Solution Approach 1:
The patent applies QR decomposition to pre-process the channel matrix at the transmitting end, creating an orthogonal basis before signal transmission. This preliminary transformation enables the system to pre-cancel interference through structured matrix operations, reducing the need for high transmission power while maintaining implementation simplicity through standardized decomposition algorithms.
4Reliability
If non-linear pre-coding (DPC-based) is used, then channel and transmission signal optimization is achieved, but data transfer rate flexibility and multi-antenna usage are restricted
Solution Approach 1:
The patent segments the transmission space into orthogonal subspaces through QR decomposition, creating a structured framework that combines the benefits of linear and non-linear pre-coding. This segmentation allows the system to apply interference cancellation techniques within each orthogonal subspace while maintaining overall data transfer rate flexibility and multi-antenna usage capability through the layered spatial structure.
Data Source
AI summary
An apparatus and method for removing interference in a transmitting end of a multi-antenna system is provided. The method includes decomposing a channel matrix including channel coefficients for a plurality of terminals, calculating a value proportional to an interference signal for each of antennas, and calculating a sum of a transmission signal and the calculated value for each terminal and multiplying the calculated sum by the decomposed channel matrix. Accordingly, channel capacity can be improved by optimizing a data transfer rate and transmission power for each terminal.