MIMO Receiver Adaptive Candidate Symbol Selection
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Solution Overview
Problem
In Multiple-Input Multiple-Output (MIMO) systems, the computational complexity of Maximum Likelihood (ML) detection increases exponentially with modulation order and number of antennas, leading to inefficient LLR calculations and abnormal operations, particularly when channel decoding is required.
Innovation Solution
An adaptive reception method and apparatus that adjust the number of candidate symbol vectors based on channel conditions, using a hard decision part, candidate symbol selector, interference canceller, and symbol estimator to reduce computational complexity and prevent abnormal LLR calculations by selecting neighbor symbols and canceling interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Maximum Likelihood (ML) detection is used to achieve optimal reception performance, then detection accuracy is improved, but computational complexity increases exponentially with modulation order and number of antennas
Solution Approach 1:
The patent segments the detection process into multiple stages: initial hard decision, candidate symbol selection, interference cancellation, and LLR calculation. By dividing the ML detection into sequential steps with decreasing complexity, the system achieves near-ML performance while reducing overall computational burden.
Solution Approach 2:
The patent performs partial ML detection by considering only a subset of candidate symbol vectors rather than all possible combinations. The hard decision provides an initial estimate, and only neighboring candidate symbols are evaluated, representing a partial action that achieves sufficient performance without exhaustive computation.
2Measurement precision
If the number of candidate symbol vectors is increased to improve LLR calculation accuracy, then detection performance is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary hard decision to obtain an initial symbol estimate before proceeding to candidate symbol evaluation. This preliminary action provides a starting point that reduces the search space, allowing accurate LLR calculation with fewer candidate symbols and reduced processing time.
Solution Approach 2:
The patent applies different processing quality to different candidate symbols: the initial hard decision candidate receives full processing, while neighboring candidates receive simplified evaluation. This local differentiation in processing quality maintains accuracy for critical candidates while reducing overall computational burden.
3Measurement precision
If interference cancellation is performed to improve signal detection accuracy, then reception performance is improved, but device complexity and processing overhead increase
Solution Approach 1:
The patent extracts and removes interference components from the received signal by identifying and canceling contributions from other transmit antennas. This extraction approach isolates the desired signal from interference, improving detection accuracy while maintaining manageable processing overhead through selective interference removal.
4Reliability
If the receiver processes all candidate symbol vectors to prevent abnormal LLR calculations, then calculation reliability is improved, but computational complexity increases
Solution Approach 1:
The patent performs partial processing of candidate symbol vectors by evaluating only those necessary for reliable LLR calculation. Rather than processing all possible candidates, the system identifies and processes a sufficient subset that prevents abnormal calculations while maintaining reliability.
Data Source
AI summary
Detection apparatus and method for achieving performance close to a Maximum Likelihood (ML) detection having optimal performance and reducing computational complexity in a Multiple-Input Multiple-Output (MIMO) system including a plurality of transmit antennas and receive antennas are provided. The apparatus includes a hard decision part for confirming an initial hard decision value of a receive symbol vector; a candidate symbol selector for selecting candidate symbols restricted to neighbor values of the initial hard decision value; and an interference canceller for canceling interference in the selected candidate symbols and selecting a final candidate symbol from the received symbols using a result of the interference cancellation.