Iterative RAKE Receiver Solver Using SINR-Based Stopping
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Solution Overview
Problem
Existing methods for determining combining weights in RAKE receivers, such as Gaussian elimination and iterative algorithms, face computational complexity and numerical instability issues, particularly when the impairment covariance matrix is ill-conditioned, and lack effective stopping criteria to ensure convergence and efficiency.
Innovation Solution
The use of the signal-to-interference-plus-noise ratio (SINR) as a metric to determine the stopping condition for iterative linear systems solvers, allowing for adaptive termination of the iterative process and ensuring convergence to a reliable solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If iterative methods are used to solve linear equations in RAKE receivers, then computational complexity is reduced and numerical stability is improved, but the number of iterations required to achieve convergence is uncertain leading to potential inefficiency
Solution Approach 1:
The patent replaces traditional stopping criteria (residual norm, relative change) with an SINR-based stopping criterion. This substitution leverages the physical meaning of SINR in communication systems to determine convergence, where iterations stop when SINR improvement falls below a threshold or when maximum SINR is reached. This resolves the contradiction by providing a meaningful stopping point that balances computational efficiency with solution accuracy.
Solution Approach 2:
The patent introduces SINR as a new parameter for controlling the iterative process. By monitoring SINR values across iterations and using them as the stopping criterion, the method transforms the abstract mathematical convergence problem into a physically meaningful communication quality metric. This parameter change enables adaptive termination that prevents both premature stopping and unnecessary iterations.
2Reliability
If iterative methods are used with ill-conditioned matrices, then numerical stability is improved, but convergence may be slow or uncertain requiring excessive iterations
Solution Approach 1:
The patent implements feedback by continuously monitoring SINR values across iterations and using this information to control the iterative process. The SINR feedback mechanism detects when the solution has converged to a satisfactory level by checking if SINR improvement becomes negligible or if maximum SINR is achieved. This feedback loop ensures reliable convergence even with ill-conditioned matrices by adapting the stopping criterion to the actual convergence behavior rather than using fixed iteration counts.
3Ease of operation
If empirical experimentation is used to determine stopping criteria, then a fixed number of iterations can be established, but computational power is wasted on extra iterations for most problems
Solution Approach 1:
The patent applies partial action by using SINR-based adaptive stopping that performs iterations only until the communication quality metric (SINR) reaches a satisfactory level. Unlike empirical methods that perform a fixed number of iterations (often exceeding what most problems need), this approach stops iterations partially - exactly when needed - thereby avoiding the computational waste of excessive iterations while still providing a simple operational criterion.
4Productivity
If adaptive stopping criteria based on residual norm are used, then iterations can be halted when a threshold is met, but the choice of threshold requires experimentation and may lead to unnecessary iterations
Solution Approach 1:
The patent achieves universality by using SINR as a multi-functional metric that simultaneously serves as both a convergence indicator and a communication quality measure. Unlike residual norm-based criteria that require separate threshold tuning, the SINR-based criterion is universally applicable across different channel conditions and problem types because SINR inherently adapts to the signal quality. This eliminates the need for separate threshold experimentation while maintaining high iteration efficiency.
Data Source
AI summary
In receiving equipment such as a mobile terminal (1) for decoding received signals coded according to a CDMA method are decoded in an intermediate processor (9). In the decoding, coefficients or elements of a vector w are calculated in a unit or module (13) by an iterative method as a solution to a system of linear equations represented by the matrix equation Rw=h, where R is a matrix of known elements and h is a known vector. For terminating the iteration, a value of the signal to interference plus noise ratio SINR for the received signals is calculated in each iteration step and then this calculated value or a quantity derived therefrom is used as a stopping criterion or generally in a stopping algorithm.