Sub-Block MIMO Decoding for Lower-Complexity Signal Detection
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Solution Overview
Problem
Current MIMO systems face challenges in adapting to increasing data rates for real-time services while maintaining low computational complexity and power consumption, as existing decoding algorithms require high computational resources and do not fully exploit the system's diversity order.
Innovation Solution
A decoder that divides the data signal into sub-vectors based on an upper triangular matrix, using sub-block decoding parameters to reduce complexity and achieve desired quality of service specifications, employing techniques like QR decomposition and lattice reduction for efficient decoding.
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 and the channel matrix into multiple sub-vectors and sub-matrices. Instead of performing ML decoding on the entire system at once, the decoder processes each sub-block separately, significantly reducing the computational complexity while maintaining good decoding performance through iterative refinement of estimates.
2Productivity
If the number of deployed antennas and constellation size are increased to meet increasing data rate demands, then system capacity is improved, but decoder complexity increases
Solution Approach 1:
By segmenting the decoding problem into sub-blocks, the patent enables the system to handle larger numbers of antennas and higher constellation sizes without proportionally increasing decoder complexity. Each sub-block can be decoded independently with reduced computational burden.
Solution Approach 2:
The patent transforms the high-dimensional decoding problem into multiple lower-dimensional sub-problems by exploiting the structure of the channel matrix and dividing the signal space, effectively reducing the complexity growth associated with increased system capacity.
3Device complexity
If sub-optimal decoders like ZF or MMSE are used to reduce computational complexity, then decoder complexity is reduced, but decoding performance and diversity order are limited
Solution Approach 1:
The patent applies segmentation to enable iterative decoding where sub-optimal decoders are used on sub-blocks, and their outputs are refined through multiple passes, progressively improving performance to approach ML decoding levels while maintaining lower complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the estimates from sub-block decoding are used to update and refine subsequent decoding iterations, allowing the system to improve performance over multiple passes without requiring full ML complexity in a single step.
Data Source
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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.