Symbol Vector-Level Combining for MIMO IR HARQ Error Reduction
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Solution Overview
Problem
MIMO IR HARQ systems often employ sub-optimal receiver structures, leading to increased bit- and symbol-error rates, reduced application quality, and increased system latency, necessitating improved decoding strategies and symbol vector-level combining techniques.
Innovation Solution
The implementation of a symbol vector-level combining technique in MIMO IR HARQ systems, where information bits are encoded into a mother code, transmitted, and decoded using log-likelihood ratio values to iteratively lower the information rate, enabling improved error-floor reduction and information rate increase with reasonable computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sub-optimal receiver structures are used in MIMO IR HARQ systems, then device complexity is reduced, but bit-error rate and symbol-error rate increase
Solution Approach 1:
The receiver structure is segmented into distinct functional modules: MIMO detector for signal detection, symbol combiner for integrating multiple transmissions, and decoder for error correction. This modular segmentation enables optimal performance at each stage while managing overall complexity through organized functional decomposition.
Solution Approach 2:
The patent transitions from traditional bit-level combining to symbol vector-level combining, adding a dimensional aspect to the combining operation. By operating in the symbol vector domain rather than simple bit domains, the system achieves better error performance through multi-dimensional signal processing that captures more information.
2Reliability
If sub-optimal receiver structures are used in MIMO IR HARQ systems, then device complexity is reduced, but symbol-error rate increases
Solution Approach 1:
The receiver structure is segmented into distinct functional modules: MIMO detector for signal detection, symbol combiner for integrating multiple transmissions, and decoder for error correction. This modular segmentation enables optimal performance at each stage while managing overall complexity through organized functional decomposition.
Solution Approach 2:
The patent transitions from traditional bit-level combining to symbol vector-level combining, adding a dimensional aspect to the combining operation. By operating in the symbol vector domain rather than simple bit domains, the system achieves better error performance through multi-dimensional signal processing that captures more information.
3Reliability
If optimal decoding strategies are implemented, then bit-error rate decreases, but computational complexity increases
Solution Approach 1:
The decoding process is segmented into separate stages: MIMO detection, symbol combining, and channel decoding. Each stage processes information independently with optimized algorithms, avoiding the need for computationally prohibitive joint optimization while achieving near-optimal overall performance.
Solution Approach 2:
By performing combining at the symbol vector level rather than bit level, the patent operates in a higher-dimensional space that provides more redundancy information for decoding. This dimensional approach improves error performance without proportionally increasing computational complexity because the combining operation itself is mathematically efficient.
4Reliability
If optimal decoding strategies are implemented, then symbol-error rate decreases, but computational complexity increases
Solution Approach 1:
The decoding process is segmented into separate stages: MIMO detection, symbol combining, and channel decoding. Each stage processes information independently with optimized algorithms, avoiding the need for computationally prohibitive joint optimization while achieving near-optimal overall performance.
Solution Approach 2:
By performing combining at the symbol vector level rather than bit level, the patent operates in a higher-dimensional space that provides more redundancy information for decoding. This dimensional approach improves error performance without proportionally increasing computational complexity because the combining operation itself is mathematically efficient.
5Productivity
If iterative transmissions with symbol vector-level combining are performed, then information rate increases, but device complexity increases
Solution Approach 1:
The transmitter functionality is segmented into distinct modules: encoder for error correction coding, puncturing module for selective bit transmission, and symbol mapper for modulation. This segmentation enables flexible control over transmitted information rate while maintaining manageable complexity through specialized processing at each stage.
Solution Approach 2:
The system dynamically changes transmission parameters including puncturing patterns and symbol mapping schemes across different transmissions. By varying these parameters, the system adapts the information rate to channel conditions and achieves higher effective throughput while keeping individual transmission instances relatively simple.
Data Source
AI summary
Techniques are provided for transmitting and receiving a mother code in an incremental redundancy hybrid automatic repeat-request protocol. A set of information bits corresponding to a message may be encoded and interleaved to produce the mother code. Each bit position of the mother code may be mapped to an output symbol, and each output symbol may be mapped to an antenna for transmission. One or more transmissions of symbols contained in the output symbols may be performed, where each transmission may include puncturing the mother code by selecting one or more symbols from the of output symbols, and transmitting each symbol in the one or more symbols on an antenna corresponding to that symbol. The mother code may be decoded, in part, by determining combinable bits contained within a set of received symbols, and computing one or more log-likelihood ratio values corresponding to each symbol in the set of received symbols.