Blind Detection of Interfering Cell Transmission Parameters
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Solution Overview
Problem
In the context of LTE Release 12's NAICS feature, existing technologies face challenges in blind detection of transmission parameters of interfering cells, particularly in joint demodulation, due to unknown combinations of modulation type, power ratio, precoding schemes, and transmission modes, leading to increased complexity and computational overhead.
Innovation Solution
A method and system for selecting the most likely hypothesis in joint demodulation by performing an exhaustive search over serving cell symbols and projecting interfering cell symbols, minimizing a whitened noise parabola, and calculating a cumulative measure of likelihood for each hypothesis, which reduces computational complexity by reusing calculations across hypotheses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search is performed over all possible transmission parameter combinations for blind detection, then detection accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the exhaustive search process by dividing transmission parameters into groups (modulation type, power ratio, precoding schemes, transmission modes) and performing searches in a structured sequence rather than evaluating all combinations simultaneously. This reduces computational complexity while maintaining detection accuracy.
Solution Approach 2:
The patent performs preliminary actions by first narrowing down the search space using semi-static cell configuration information exchanged between base stations before the UE performs blind detection. This pre-filtering reduces the number of hypotheses the UE must evaluate, thereby reducing computational complexity.
2Object-affected harmful factors
If joint demodulation is performed for serving cell and interfering cell transmissions, then interference suppression is improved, but processing complexity increases
Solution Approach 1:
The patent introduces dynamic hypothesis selection where the UE evaluates multiple hypotheses about interfering cell transmission parameters and selects the most likely one based on measured metrics. This dynamic approach allows the system to adapt processing complexity to actual interference conditions rather than always performing full joint demodulation.
Solution Approach 2:
The patent changes parameters by representing interfering cell transmission parameters as discrete hypotheses with specific values for modulation type, power ratio, precoding matrices, and transmission modes. This parameterization allows efficient evaluation and comparison of different interference scenarios without requiring full blind search.
3Measurement precision
If multiple hypotheses are tested for interfering cell parameters, then blind detection accuracy is improved, but measurement time increases
Solution Approach 1:
The patent applies partial action by testing a limited set of most likely hypotheses rather than all possible parameter combinations. The system evaluates hypotheses in order of likelihood and can stop when a sufficient match is found, reducing measurement time while maintaining adequate detection accuracy.
Solution Approach 2:
The patent substitutes mechanical exhaustive search with a more efficient hypothesis testing mechanism that uses semi-static configuration information to guide the search. This replacement reduces the time required for blind detection by avoiding unnecessary evaluations of unlikely parameter combinations.
Data Source
AI summary
Joint demodulation of a desired transmission and an interfering transmission received from an interfering cell with an unknown combination of transmission parameters is performed. For each subcarrier, an exhaustive search for the serving cell symbols and projection for the interfering cell symbols is performed for tested hypotheses of the interfering cell, by minimizing a whitened noise parabola for each combination of searched hypothesis and hyper constellation point of the serving cell. A constellation point for the interfering cell that is closest to the minimum point of the parabola is selected, where coefficients of the parabola are calculated once for each subgroup of four modulation types of the interfering cell. A measure of likelihood for each of the tested hypotheses is calculated. A cumulative measure of likelihood for each of the tested hypotheses is calculated, and the most likely hypothesis is selected based on the cumulative measure of likelihood.