Blind Detection of Interference Rank in Wireless Systems
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently detecting the precoding matrix index of interference with high complexity and cost, particularly in LTE systems, where accurate estimation of the interference rank and precoding matrices is necessary for effective signal processing.
Innovation Solution
An apparatus and method for blind detection of interference rank, involving a receiver, serving signal cancellation, and precoding matrix index determination, which includes a blind detection of interference rank function block and a precoding matrix index determination function block to identify the rank and select the appropriate precoding matrix index based on the interference rank, using techniques such as off-diagonal element comparison and probability calculation for low complexity and cost implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If joint detection or symbol-level interference cancellation is deployed to accurately estimate interference parameters, then measurement precision of interference rank and precoding matrices is improved, but device complexity increases
Solution Approach 1:
The detection process is segmented into distinct functional blocks: serving signal cancellation function block, blind detection of interference rank function block, and precoding matrix index determination function block. Each block performs a specific task in sequence, breaking down the complex joint detection problem into manageable stages that reduce overall system complexity while maintaining accuracy.
Solution Approach 2:
The serving signal cancellation function block performs preliminary action by removing the serving signal from the received signal before interference detection. This preprocessing step simplifies the subsequent interference rank detection and precoding matrix estimation by eliminating the dominant serving signal component, thereby reducing the complexity of the overall detection algorithm.
2Device complexity
If blind detection of interference rank is implemented to reduce computational complexity, then device complexity is reduced, but measurement precision of precoding matrix index may deteriorate
Solution Approach 1:
The blind detection of interference rank function block performs self-service by autonomously determining the interference rank without requiring explicit signaling or feedback from the network. It uses the residual signal from serving signal cancellation and applies algorithms such as off-diagonal element comparison and probability calculation to self-determine the rank, thereby reducing computational complexity while maintaining detection accuracy through inherent signal characteristics.
Solution Approach 2:
The precoding matrix index determination function block changes parameters by selecting different precoding matrices based on the detected interference rank. When rank is 1, it selects from four candidate precoding matrices; when rank is 2, it selects from two candidate precoding matrices. This parameter adaptation allows the system to maintain measurement precision by choosing the appropriate number of candidates based on the detected rank, thereby balancing complexity and accuracy.
3Measurement precision
If multiple candidate precoding matrices are evaluated to ensure accurate interference cancellation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system dynamically adapts the number of candidate precoding matrices based on the detected interference rank. The precoding matrix index determination function block adjusts its search space: four candidates when rank is 1, two candidates when rank is 2. This dynamic adaptation reduces the average detection processing time while maintaining interference cancellation accuracy by matching the candidate set size to the actual interference conditions.
Solution Approach 2:
The detection algorithm changes the parameter of candidate matrix count based on the detected rank. By using probability calculation and off-diagonal element comparison, the system efficiently narrows down the candidate set size according to the interference rank, thereby reducing the time required to evaluate multiple candidates while preserving the accuracy needed for effective interference cancellation.
Data Source
AI summary
An apparatus and method are provided. The apparatus includes a receiver configured to receive a signal; a serving signal cancellation function block connected to the receiver and configured to remove a serving signal from the received signal to provide a residual signal; a blind detection of interference rank function block connected to the serving signal cancellation function block and configured to determine a rank of the residual signal, wherein the rank is one of a first rank and a second rank; and a precoding matrix index determination function block connected to the blind detection of interference rank function block and configured to determine a precoding matrix index based on the rank.