Iterative Impairment Co-variance Matrix Estimation for G-RAKE Receivers

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Solution Overview

Problem

In downlink communication networks, existing methods struggle to accurately estimate the impairment co-variance matrix for Generalized RAKE receivers, especially at high geometries and moderate to high mobile speeds, due to the lack of unused Walsh codes and reliance on noisy Common Pilot Channel signals.

Innovation Solution

An iterative method is employed to increase the accuracy of impairment co-variance matrix estimation by performing de-spread operations on High Speed-Downlink Shared Channel symbols, using channel estimates and modulation scheme information to form and refine recovered symbols, and iteratively calculating the impairment co-variance matrix, which is then used to determine G-RAKE combining weights and estimate Signal-to-Interference-plus-Noise Ratio.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If parametric G-RAKE based on CPiCH is used, then device complexity is reduced, but measurement precision of impairment co-variance matrix deteriorates

Engineering Contradiction:

Improvereceiver complexityVSAvoidimpairment co-variance matrix estimation accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent introduces an iterative data-aided estimation process that uses recovered symbols as intermediaries to improve the accuracy of impairment co-variance matrix estimation. The process recovers symbols from the received signal, uses these recovered symbols to estimate the impairment co-variance matrix, and iterates to refine both symbol recovery and matrix estimation, thereby achieving high precision without requiring unused Walsh codes or increasing overall receiver complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent implements a feedback mechanism where the estimated impairment co-variance matrix is used to improve symbol recovery, and the recovered symbols are fed back to refine the matrix estimation. This iterative feedback loop allows the system to progressively improve estimation accuracy by using the output of one iteration as input for the next, resolving the contradiction between complexity and precision

Inventive Principle:

Principle #23Feedback

2Measurement precision

If non-parametric G-RAKE with cross-slot average is used, then measurement precision improves, but adaptability to sudden interference changes deteriorates

Engineering Contradiction:

Improveimpairment co-variance matrix estimation accuracyVSAvoidresponse to sudden interference changes

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent employs periodic iteration within each time slot, performing multiple rounds of symbol recovery and matrix estimation rather than relying on cross-slot averaging. This periodic action within-slot allows the system to achieve high measurement precision through iterative refinement while maintaining adaptability to sudden interference changes by not depending on historical data from previous slots

Inventive Principle:

Principle #19Periodic action

3Reliability

If Minimum Mean Square Error G-RAKE is used, then robustness improves, but measurement precision deteriorates at high SINR regions

Engineering Contradiction:

ImproverobustnessVSAvoidimpairment co-variance matrix estimation accuracy at high SINR

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The patent implements a dynamic iterative process that adapts to different SINR conditions. The algorithm dynamically adjusts the number of iterations and convergence criteria based on the observed signal conditions, allowing it to maintain robustness at low SINR while achieving high precision at high SINR regions where MMSE G-RAKE fails

Inventive Principle:

Principle #15Dynamics

4Measurement precision

If iterative symbol recovery is performed, then measurement precision of recovered symbols improves, but processing time increases

Engineering Contradiction:

Improverecovered symbols estimation accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent implements early termination criteria in the iterative process, allowing the algorithm to skip remaining iterations when convergence is achieved or when a predefined accuracy threshold is met. This rushing through of unnecessary iterations significantly reduces processing time while maintaining high measurement precision for cases that converge quickly

Inventive Principle:

Principle #21Skipping (Rushing through)

Data Source

PatentEP2561635B1Method and arrangement of increasing impairment co-variance matrix estimation accuracy

Publication Date: 2016.06.22 TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

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AI summary

The present invention relates to a method and an arrangement (20) of increasing impairment co-variance matrix Ru estimation accuracy in downlink in a user equipment (18) in a communication network system. De-spread is performed on HS-DSCH symbols to form a matrix X of de-spread symbols (100). The matrix X and channel estimates hc from CPiCH and modulation scheme information are used to form a matrix S of recovered symbols in hard value (101 ). The matrix X and the channel estimates hc1 the modulation scheme information and the matrix S output from the previous step are used to increase the estimation accuracy of the matrix S (102). The previous step (102) is repeated until the output symbols are the same as the input symbols or the number of iterations reaches a pre-defined maximum value (103). The matrix X and the matrix S with increased estimation accuracy are used to form an impairment co-variance matrix Ru estimate (104). The impairment co-variance matrix Ru estimate and the channel estimates hc from CPiCH are used to determine G-RAKE combining weight w and to estimate CPiCH SINR (105).