MIMO Receiver Modified Maximum Likelihood Detection Complexity
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Solution Overview
Problem
The complexity of calculations in MIMO communication systems increases exponentially with higher modulation orders, making existing detection schemes inefficient for high-speed data transmission, particularly in systems with multiple transmission antennas.
Innovation Solution
A receiving method and apparatus employing an enhanced Modified Maximum Likelihood (MML) detection scheme, which reduces calculation complexity by rearranging symbols, applying QR decomposition, and using group slicing to determine candidate symbols, thereby reducing the number of Euclidean distances and Log-Likelihood Ratios (LLRs) required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Maximum Likelihood detection scheme is used to achieve optimal detection performance, then detection accuracy is improved, but calculation complexity increases exponentially with modulation order
Solution Approach 1:
The patent segments the detection process into two stages: first performing ML hard decision to obtain initial symbol estimates, then using these estimates to reduce the candidate symbol set for LLR calculation. This segmentation divides the originally exponential complexity into manageable portions, maintaining detection accuracy while reducing overall calculation complexity.
Solution Approach 2:
The patent performs ML hard decision as a preliminary action before LLR calculation. The hard decision results serve as prior information to prune the candidate symbol set, reducing the number of Euclidean distance calculations needed for LLR. This preliminary action maintains optimal detection performance while significantly reducing computational burden.
2Productivity
If modulation order is increased to achieve high-speed data transmission, then data rate is improved, but calculation complexity increases exponentially
Solution Approach 1:
The patent applies partial action by calculating LLR only for a reduced set of candidate symbols rather than all possible symbols. The ML hard decision results identify the most likely symbols, and LLR calculation is performed only for these candidates plus a limited number of neighboring symbols, rather than exhaustively checking all modulation symbols. This maintains high data rates while avoiding exponential complexity growth.
3Reliability
If channel coding is employed in commercialized systems, then error correction capability is improved, but calculation amount increases due to LLR requirement
Solution Approach 1:
The patent extracts and utilizes only the necessary information for channel decoding by calculating LLR for a reduced candidate symbol set rather than all possible symbols. The ML hard decision results are used to identify which symbols require LLR calculation, extracting only the essential computations needed for reliable channel decoding while discarding redundant calculations.
Data Source
AI summary
A receiving method and apparatus in a Multiple-Input Multiple-Output (MIMO) communication system are provided. The method includes receiving reception signals through a plurality of reception antennas, grouping symbols corresponding to the reception signals, respectively, into a preset number of groups, and rearranging symbols of the respective groups, transforming the reception signals by applying QR decomposition to the reception signals, sequentially canceling interference due to each of total possible candidate symbols for a first symbol based on an order of the rearranged symbols in the transformed reception signals, determining a portion of the total possible candidate symbols to be a candidate symbol set for each remaining symbol, except for the first symbol, using the interference-canceled reception signal, and determining log-likelihood ratio values of the first symbol, which are to be used upon decoding the received signals, using candidate symbols for the first symbol and each remaining symbol.