Transform Matrix for Wireless Communication Symbols
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Solution Overview
Problem
Existing wireless communication approaches are inferior in one or more aspects such as data rate, error rate, robustness, power consumption, and complexity.
Innovation Solution
A method for transferring data symbols between a transmitter and a receiver by specifying communication symbols through the application of a transform to a representation of the data symbols, where the transform matrix has specific properties such as sparsity and angle distortion mitigation thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional wireless communication approaches are used, then implementation is simpler, but data rate is lower and error rate is higher
Solution Approach 1:
The patent applies a transform matrix with specific parameter constraints (sparsity threshold ≥0.5, angle distortion mitigation threshold ≥2N-1, orthogonality threshold =0) to transform data symbols into communication symbols. This parameter-based transformation improves data rate and error rate performance while maintaining controlled computational complexity through the structured matrix properties.
2Reliability
If conventional wireless communication approaches are used, then implementation is simpler, but robustness is lower
Solution Approach 1:
The transform matrix is designed with specific parameter thresholds including sparsity ≥0.5, angle distortion mitigation ≥2N-1, and orthogonality =0. These parameter constraints ensure improved robustness against channel impairments while keeping the transformation computationally manageable through the structured mathematical properties.
3Reliability
If conventional wireless communication approaches are used, then power consumption is lower, but error rate is higher
Solution Approach 1:
The transform matrix with constrained parameters (sparsity ≥0.5, angle distortion mitigation ≥2N-1, orthogonality =0) improves error rate performance. The sparsity constraint in particular reduces the number of non-zero elements, which directly lowers computational operations and thus power consumption while maintaining improved reliability.
4Manufacturing precision
If a dense transform matrix is used, then transformation is more complete, but computational complexity increases and sparsity is reduced
Solution Approach 1:
The transform matrix is designed with a sparsity threshold parameter set to ≥0.5, which explicitly controls the ratio of zero-valued elements. This parameter setting achieves an optimal balance where sufficient transformation precision is maintained through the structured zeros and non-zeros, while computational complexity is reduced by limiting the number of non-zero operations.
Data Source
AI summary
A method is disclosed for transferring data symbols between a transmitter and a receiver. The method comprises specifying communication symbols through application of a transform to a representation of the data symbols, and conveying a signal indicative of the communication symbols from the transmitter to the receiver. When expressed as a transform matrix, the transform has at least the following properties: that a ratio (between number of zero-valued elements of the transform matrix and number of non-zero-valued elements of the transform matrix) equals or exceeds a sparsity threshold, and that a number of columns of the transform matrix that has a zero-valued element sum equals or exceeds an angle distortion mitigation threshold. When the method is performed by the transmitter, the method may further comprise providing the communication symbols by applying the transform to amplitude representations of the data symbols, and conveying the signal indicative of the communication symbols may comprise transmitting the signal. When the method is performed by the receiver, conveying the signal indicative of the communication symbols may comprise receiving the signal, and the method may further comprise estimating the data symbols by applying a reverse operation of the transform to angle representations of the received signal. Corresponding apparatuses, transmitter, receiver, communication device and computer program product are also disclosed.