Recursive Sub-Block Decoding for Low-Complexity MIMO Detection
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Solution Overview
Problem
Current MIMO systems face challenges in adapting to increasing data rates for real-time services while requiring low-complexity decoders that consume low power and computational resources, as existing decoding algorithms like Maximum Likelihood decoders have high computational complexity and sub-optimal decoders offer limited performance.
Innovation Solution
A sub-block decoding method is introduced, where the data signal is divided into sub-vectors based on an upper triangular matrix, allowing for reduced complexity decoding by determining sub-block decoding parameters and using various decoding algorithms such as ZF, MMSE, or sequential decoding to estimate each sub-vector of information symbols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Maximum Likelihood decoding algorithms are used to achieve optimal error rate and diversity order, then decoding performance is improved, but computational complexity increases significantly
Solution Approach 1:
The patent divides the received signal vector into multiple sub-vectors and the channel matrix into sub-matrices, enabling parallel processing of smaller decoding problems. This segmentation reduces the computational complexity from exponential in the total number of antennas to a manageable level while preserving diversity order through coordinated processing of all sub-vectors.
Solution Approach 2:
The patent transforms the original high-dimensional decoding problem into multiple lower-dimensional sub-problems by exploiting the upper triangular structure of the channel matrix. This dimensional decomposition allows sequential processing of sub-vectors, reducing computational burden while maintaining optimal decoding performance through the structured approach.
2Reliability
If the number of deployed antennas is increased to provide more signal replicas and improve diversity order, then reliability is improved, but decoding complexity increases
Solution Approach 1:
By segmenting the decoding task across multiple sub-vectors corresponding to different antenna groups, the patent enables parallel processing that scales efficiently with the number of antennas. Each sub-vector processing unit handles a manageable portion of the total diversity order, avoiding exponential complexity growth.
3Productivity
If constellation size is increased to achieve higher data rates, then productivity is improved, but decoding complexity increases
Solution Approach 1:
The patent applies segmentation to the signal space by dividing the received signal into sub-vectors that can be processed independently. This reduces the effective constellation size for each decoding operation, lowering computational complexity while the combined processing of all sub-vectors maintains the full data rate capability of the larger constellation.
Data Source
AI summary
Embodiments of the invention provide a decoder for decoding a data signal received through a transmission channel in a communication system, said transmission channel being represented by an upper triangular matrix, said signal carrying transmitted symbols, each symbol carrying a set of information bits, wherein said decoder comprises:a processing unit (213) configured to determine at least one sub-block decoding parameter given a target quality of service metric;A sub-block decoding unit (214) configured to divide said data signal into a number of sub-vectors based on said at least one sub-block decoding parameter in accordance with a division of said upper triangular matrix into a number of sub-matrices, said sub-block decoding unit (214) being further configured to determine at least one estimate of each sub-vector of transmitted symbols from said sub-vectors, and determine an estimate of the transmitted symbols from said estimates.