Parallel Self-Interference Cancellation in Carrier Aggregation TX Paths
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Solution Overview
Problem
Conventional methods for estimating self-interference in multiple transmitter carrier aggregation systems face high computational complexity due to the increased number of taps required, especially in MISO structures, which is not efficiently addressed by existing recursive least square methods.
Innovation Solution
A parallel or sequential estimation method is employed to reduce computational complexity by forming kernels separately for each transmitter, using a low number of taps for each transmission, allowing for efficient self-interference estimation and cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MISO structure with multiple taps is used for self-interference estimation, then estimation accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent divides the self-interference estimation process into separate parallel estimations for each transmitter. Instead of using a single MISO structure with many taps, the system creates individual estimation paths for TX1 and TX2, each using fewer taps. This segmentation reduces the total number of taps required while maintaining estimation accuracy for each transmitter's contribution to self-interference.
Solution Approach 2:
The patent combines the results of parallel single-input estimations to achieve the overall self-interference cancellation. By merging the cancellation signals from each transmitter's estimation path, the system achieves accurate total self-interference cancellation without requiring the computationally intensive conventional MISO approach with numerous taps.
2Measurement precision
If conventional RLS method is used for self-interference estimation, then estimation accuracy is improved, but computational complexity increases due to matrix inversions
Solution Approach 1:
The patent segments the complex RLS estimation problem into simpler parallel sub-problems, one for each transmitter. Each sub-problem uses fewer taps and requires less computational effort. By solving these smaller problems in parallel and combining results, the system avoids the computationally intensive matrix inversions required by conventional unified RLS methods.
3Device complexity
If number of taps is reduced to decrease computational complexity, then computational complexity is reduced, but self-interference estimation accuracy deteriorates
Solution Approach 1:
The patent demonstrates that by segmenting the estimation task into parallel paths for each transmitter, each path can use fewer taps while the combined result maintains high accuracy. The segmentation allows the system to distribute the estimation burden across multiple simple paths rather than requiring one complex path with many taps.
Solution Approach 2:
Each transmitter's interference is estimated and cancelled independently through its own parallel path, with each path serving itself using minimal taps. This self-service approach allows each estimation path to be simple and computationally efficient while collectively providing accurate overall self-interference cancellation.
Data Source
AI summary
A system and a method are disclosed and include generating a first kernel based on a tap of a first transmission and a plurality of taps of a second transmission; calculating a first weighted coefficient set based on the first kernel; calculating a first self-interference transmission signal estimation based on the first weighted coefficient set; and obtaining a first cancellation signal by subtracting the first self-interference transmission signal estimation from a downlink received signal.