Adaptive Sub-Block Decoding for MIMO Interference Control
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Solution Overview
Problem
Existing recursive sub-block decoding algorithms in MIMO systems do not effectively minimize the impact of interference between sub-vectors of information symbols, leading to sub-optimal performance and complexity tradeoffs.
Innovation Solution
A decoder and method that adaptively divide the vector of information symbols based on symbol estimation algorithms, reducing the propagation of decoding errors by minimizing interference between sub-vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If deterministic sub-block division is used in QR-based decoding, then device complexity is reduced, but decoding error performance deteriorates due to interference between sub-vectors
Solution Approach 1:
The patent transforms the static, deterministic sub-block division into a dynamic, adaptive process. The division parameters (number of sub-vectors and their lengths) are no longer fixed but are adapted based on channel conditions and interference characteristics. This allows the system to optimize the trade-off between complexity and performance by adjusting the division strategy according to the actual transmission environment, thereby reducing interference between sub-vectors while maintaining manageable decoding complexity.
Solution Approach 2:
The patent changes the parameters of sub-block division adaptively rather than using fixed deterministic values. By modifying the number of sub-vectors and their respective lengths based on channel state information and interference levels, the system can optimize decoding performance. This parameter adaptation allows the decoder to minimize interference between sub-vectors dynamically, improving reliability without requiring a complete redesign of the decoding architecture.
2Object-affected harmful factors
If more sub-vectors are created in sub-block division, then interference between sub-vectors is reduced, but computational complexity increases
Solution Approach 1:
The patent implements a dynamic approach where the number of sub-vectors is not fixed but adapted based on channel conditions. When channel conditions are poor and interference is high, the system increases the number of sub-vectors to reduce interference. When conditions are good, it reduces the number to lower computational complexity. This dynamic adjustment allows the system to optimize the balance between reducing interference and managing computational load according to actual transmission requirements.
Solution Approach 2:
The patent employs parameter changes by adaptively modifying the number of sub-vectors and their lengths based on channel state information. This allows the system to increase the number of sub-vectors when needed to reduce interference, and decrease them when computational complexity becomes a concern. The adaptive parameter adjustment enables the system to achieve optimal performance-complexity trade-off in varying channel conditions.
Data Source
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AI summary
Embodiments of the invention provide a decoder for decoding a signal received through a transmission channel in a communication system, said signal comprising a vector of information symbols, wherein the decoder comprises: - a processing unit (307) configured to determine at least one candidate set of division parameters and to perform a division of said vector of information symbols into a set of sub-vectors in association with each candidate set of division parameters, each pair of sub-vectors being associated with a division metric; - a selection unit (309) configured to select one of said candidate sets of division parameters according to a selection criterion depending on said division metric; and - a decoding unit (311) configured to determine at least one estimate of each sub-vector associated with said selected set of division parameters by applying a symbol estimation algorithm, wherein the decoder is configured to determine at least one estimate of the vector of information symbols from said at least one estimate of each sub-vector of information symbols.