MIMO Precoding Matrix Switching for LDPC Mixed Modulation
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Solution Overview
Problem
Implementing the MIMO scheme effectively when using LDPC coding, as existing technologies face challenges in optimizing data processing and transmission quality.
Innovation Solution
A transmission method and device configuration that employs precoding and modulation schemes, such as QPSK, 16QAM, 64QAM, and 256QAM, with adjustable bit lengths and precoding matrices to enhance spatial diversity and data reception quality, specifically using precoding matrices that vary or switch based on symbol number and modulation schemes to optimize signal points in the I-Q plane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If different modulation schemes are used for each symbol in MIMO transmission, then data transfer rate is improved, but data reception quality deteriorates due to reduced Euclidean distance between signal points
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the precoding matrix based on the symbol number and modulation scheme being used. This allows the system to optimize the mapping of coded bits to signal points for each specific modulation scheme (QPSK, 16QAM, 64QAM, 256QAM), ensuring that the Euclidean distance between signal points is maximized for the given constraints. By changing the precoding parameters adaptively, the system achieves both high data transfer rates through varied modulation schemes and maintains data reception quality through optimized signal point distribution.
2Reliability
If LDPC coding is applied in MIMO systems, then error correction capability is improved, but implementation complexity increases due to optimization challenges
Solution Approach 1:
The patent implements dynamics by making the precoding matrix variable rather than fixed. The precoding matrix changes dynamically based on the symbol number and the specific modulation scheme being employed. This dynamic approach allows the system to maintain optimized performance across different operating conditions without requiring complex reconfiguration. The variable precoding matrix adapts to the LDPC-coded data stream, providing error correction capability while managing implementation complexity through a systematic, rule-based adaptation mechanism.
Solution Approach 2:
The system changes parameters of the precoding matrix to match different modulation schemes and symbol positions. This parameter adaptation simplifies the implementation of LDPC coding in MIMO systems by providing a structured method to handle the complexity. Instead of requiring complex optimization algorithms, the system uses predefined parameter sets that correspond to different modulation schemes, making the implementation more manageable while maintaining high error correction capability.
3Manufacturing precision
If precoding matrices are optimized for each modulation scheme, then signal point distribution is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the precoding optimization into separate, dedicated matrices for each modulation scheme (QPSK, 16QAM, 64QAM, 256QAM). Each precoding matrix is specifically optimized for its corresponding modulation scheme, ensuring optimal signal point distribution tailored to the characteristics of that scheme. This segmented approach simplifies the overall system complexity by providing clear, dedicated solutions for each modulation type rather than requiring a single complex adaptive mechanism.
Solution Approach 2:
The system employs dynamic selection of precoding matrices based on the current modulation scheme being used. Rather than using a single fixed matrix or a continuously adaptive matrix requiring complex computation, the system dynamically switches between pre-optimized matrices corresponding to different modulation schemes. This dynamic selection approach achieves optimal signal point distribution for each scheme while keeping system complexity manageable through the use of predetermined matrix sets.
Data Source
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AI summary
An encoder outputs a first bit sequence having N bits. A mapper generates a first complex signal s 1 and a second complex signal s2 with use of bit sequence having X+Y bits included in an input second bit sequence, where X indicates the number of bits used to generate the first complex signal s1, and Y indicates the number of bits used to generate the second complex signal s2. A bit length adjuster is provided after the encoder, and performs bit length adjustment on the first bit sequence such that the second bit sequence has a bit length that is a multiple of X+Y, and outputs the first bit sequence after the bit length adjustment as the second bit sequence. As a result, a problem between a codeword length of a block code and the number of bits necessary to perform mapping by a set of modulation schemes is solved.