Robust Multicode Detector for HSDPA Interference Rejection
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Solution Overview
Problem
CDMA systems face interference due to channel dispersion, leading to orthogonality loss between symbols and complexity issues in multi-user detection, particularly for mobile terminals where knowledge of all active codes is difficult to obtain and matrix inversion operations are computationally challenging.
Innovation Solution
A robust interference model is used to compute interference rejection terms by scaling spreading waveform correlations with signal powers and compensating for noise, allowing for efficient suppression of Multiple Access Interference (MAI) and Intersymbol Interference (ISI) in CDMA systems, employing a RAKE receiver followed by a joint detector that includes a Minimum Mean Squared Error (MMSE) detector with a virtual user concept for mobile terminals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MMSE detector is used, then MAI and ISI suppression performance is improved, but device complexity and computational difficulty increase due to matrix inversion operations and requirement of knowing all active codes
Solution Approach 1:
The patent transforms the conventional MMSE detector parameters by eliminating the need for matrix inversion and modifying the interference rejection term computation. The new approach uses a simplified set of equations that compute interference rejection terms directly from received signals and known codes, changing the computational parameters from matrix-based to scalar-based operations, thereby reducing complexity while maintaining performance
Solution Approach 2:
The patent extracts and removes the computationally intensive matrix inversion operation from the MMSE detection process. By taking out this complex operation and replacing it with direct computation of interference rejection terms using only locally known codes and received signal measurements, the patent simplifies the detector structure while preserving the essential interference suppression capability
2Measurement precision
If conventional MMSE detector is used, then interference suppression is improved, but ease of operation deteriorates due to difficulty of obtaining knowledge of all active codes in mobile terminals
Solution Approach 1:
The patent enables the mobile terminal to perform interference suppression using only its own locally stored code information and received signal measurements. The detector serves itself by computing interference rejection terms from locally available data without requiring external provision of all active codes, making the system self-sufficient and easier to implement in mobile terminals
Solution Approach 2:
Instead of requiring the mobile terminal to know all active codes (conventional approach), the patent inverts the approach by using only the codes known to the mobile terminal to compute interference rejection terms. This inversion of the information requirement makes the system practical for mobile terminal implementation while maintaining interference suppression performance
Data Source
AI summary
Detecting a symbol of interest comprises despreading a received signal to obtain despread values corresponding to the symbol of interest and to one or more interfering symbols, combining the despread values to generate combined values for the symbol of interest and the interfering symbols, computing spreading waveform correlations between the spreading waveform for the symbol of interest and the spreading waveforms for the interfering symbols, computing interference rejection terms representing the interference present in the combined value for the symbol of interest attributable to the interfering symbols based on the spreading waveform correlations, and generating an estimate of the symbol of interest by combining the combined values with the interference rejection terms. The interference rejection terms are computed by scaling the spreading waveform correlations by corresponding signal powers and compensating the estimates for noise. This provides a robust interference model that avoids numerical problems associated with conventional joint detection.