Full-Duplex Self-Interference Cancellation Using Kernel Subsets
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Solution Overview
Problem
Existing wireless communication systems face challenges in effectively canceling self-interference in full-duplex configurations, particularly due to nonlinear interference, which complicates signal processing and reduces communication efficiency.
Innovation Solution
Implementing a nonlinear interference cancellation (NLIC) procedure that utilizes a subset of kernels from a set of nonlinear candidate kernels corresponding to a nonlinear self-interference model to cancel self-interference in full-duplex communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complete nonlinear self-interference model with all candidate kernels is used for self-interference cancellation, then the cancellation accuracy is improved, but the computational complexity increases significantly
Solution Approach 1:
The complete set of nonlinear candidate kernels is segmented into multiple subsets, where each subset contains a portion of the candidate kernels. Instead of processing all kernels simultaneously, the system divides the computational task into smaller segments that can be handled more efficiently, reducing the overall computational complexity while maintaining adequate self-interference cancellation performance.
Solution Approach 2:
The system applies partial action by using only a selected subset of candidate kernels rather than the complete set. This partial approach provides a practical compromise where sufficient self-interference cancellation is achieved without the excessive computational burden of processing all possible kernels, aligning with the principle of doing enough rather than everything.
2Reliability
If the number of coefficients to be estimated in the NLIC procedure is increased to improve cancellation accuracy, then the self-interference cancellation performance is improved, but the computational requirements and processing time increase
Solution Approach 1:
The system extracts and selects only the most relevant candidate kernels from the complete set, removing unnecessary kernels that contribute minimally to self-interference cancellation performance. This extraction process reduces the number of coefficients that need to be estimated, thereby decreasing the computational requirements and processing time while maintaining adequate cancellation accuracy.
Solution Approach 2:
The system changes the parameter of kernel selection by adjusting which candidate kernels are included in the subset and how many kernels are used. By modifying this parameter, the system can balance between cancellation performance and computational efficiency, reducing the number of coefficients to estimate without significantly degrading the self-interference cancellation capability.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may transmit a first signal in accordance with a full duplex configuration. The network node may receive a second signal comprising a communication and self-interference associated with the first signal, wherein receiving the second signal comprises cancelling the self-interference based at least in part on a nonlinear interference cancellation (NLIC) procedure associated with a subset of kernels of a set of nonlinear candidate kernels corresponding to a nonlinear self-interference model. Numerous other aspects are described.