Recursive Sub-Block MIMO Decoding With Reduced Search Complexity
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Solution Overview
Problem
Existing wireless communication systems face challenges in achieving optimal decoding performance while maintaining reduced complexity, particularly in MIMO systems, where decoders like ML decoders require exponential complexity and trade off performance for complexity reduction, leading to sub-optimal solutions that do not ensure target diversity order.
Innovation Solution
A sub-block decoding method that divides the received signal vector into sub-vectors based on a channel matrix division, using QR decomposition to recursively determine candidate estimates for each sub-block with a reduced number of candidates, allowing for flexible control of diversity order and complexity through threshold estimation and adaptive decoding algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Maximum Likelihood decoders are used, then decoding performance is optimal, but computational complexity increases exponentially with the number of antennas
Solution Approach 1:
The patent divides the channel matrix into multiple sub-matrices and the received signal into sub-vectors, enabling block-by-block decoding. This segmentation transforms the single exhaustive search problem into multiple smaller searches, reducing overall computational complexity while maintaining near-optimal performance through systematic exploration of candidate solutions across blocks.
2Device complexity
If node pruning-based tree search algorithms are used, then computational complexity is reduced, but decoding performance degrades due to discarded nodes
Solution Approach 1:
The patent implements dynamic threshold adjustment where the threshold for candidate retention is adapted based on the search progress and signal characteristics. This dynamic approach allows the algorithm to be more selective when confidence is high and more exhaustive when uncertainty exists, balancing complexity reduction with performance maintenance unlike static pruning methods.
Solution Approach 2:
The algorithm uses feedback from previous block decoding results to inform subsequent block processing. Candidate estimates from earlier blocks serve as constraints and guidance for later blocks, creating a feedback loop that improves overall decoding accuracy while reducing the search space in later stages compared to independent block processing.
3Device complexity
If iterative decoders are used, then computational complexity is reduced, but diversity order and error performance are not ensured
Solution Approach 1:
The patent performs preliminary QR decomposition of the channel matrix to obtain an upper triangular structure before the actual decoding search. This preliminary transformation simplifies the subsequent search by creating a structured problem form that enables efficient candidate generation and evaluation, reducing the complexity burden before the main decoding process begins.
Solution Approach 2:
By dividing the decoding problem into blocks with systematic candidate estimation across blocks, the patent ensures that diversity order is maintained through the structured exploration of multiple candidates. The segmentation approach with candidate retention and threshold-based filtering ensures sufficient search depth to capture diverse path possibilities, unlike iterative methods that may converge prematurely.
Data Source
AI summary
Embodiments of the invention provides a decoder for decoding a signal received through a transmission channel in a communication system, said signal carrying information symbols selected from a given alphabet and being associated with a signal vector, said transmission channel being represented by a channel matrix, wherein said decoder comprises:a sub-block division unit (301) configured to divide the received signal vector into a set of sub-vectors in correspondence with a division of a matrix related to said channel matrix;a candidate set estimation unit (305) for recursively determining candidate estimates of sub-blocks of the transmitted signal corresponding to said sub-vectors, each estimate of a given sub-block being determined from at least one candidate estimate of the previously processed sub-blocks,wherein said candidate set estimation unit is configured to determine a set of candidate estimates for at least one sub-block of the transmitted signal by applying at least one iteration of a decoding algorithm using the estimates determined for the previously processed sub-blocks, the number of candidate estimates determined for said sub-block being strictly inferior to the cardinal of the alphabet and superior or equal to two, the decoder further comprising a signal estimation unit (306) for calculating an estimate of the transmitted signal from said candidate estimates determined for said sub-blocks.