MIMO Detection via Candidate Vector Path Metrics
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Solution Overview
Problem
Current MIMO detection methods face challenges in achieving near-optimal performance with constant complexity, low arithmetic operations, and robustness against large modulation orders and channel code rates, while existing solutions fail to meet these requirements effectively, especially for high-order modulation and spatially correlated channels.
Innovation Solution
A method and apparatus for MIMO detection in User Equipment (UE) that involves computing path metrics and Log-Likelihood Ratios (LLRs) using a list of hypotheses candidate vectors, incorporating Linear Minimum Mean Square Error (LMMSE) estimates and soft parallel interference cancellation, with optional conversion to real-valued signals for reduced complexity and candidate reduction techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full max-log-MAP (MLM) detection is used to achieve near-optimal MIMO detection performance, then detection accuracy is improved, but computational complexity increases to O(MN) which becomes prohibitively large for high-order modulation and large antenna configurations
Solution Approach 1:
The patent applies segmentation by dividing the MIMO detection problem into multiple independent detection stages, where each stage processes a subset of spatial layers. The received signal is sequentially processed through multiple detection nodes, each handling a portion of the overall detection task. This segmentation reduces the computational burden of full MLM detection while maintaining near-optimal performance through the structured processing of candidate vectors at each stage.
2Productivity
If the number of spatial layers N and modulation order M are increased to meet high data rate requirements, then data rate is improved, but the complexity of full MLM detection increases to levels that are far beyond what is doable in a prior art UE
Solution Approach 1:
The patent employs dynamics by implementing an adaptive detection scheme where the number of candidate vectors and processing depth are dynamically adjusted based on channel conditions and modulation order. The detection process adapts its complexity level to match the requirements of different spatial configurations and modulation schemes, allowing the system to handle high data rates while keeping computational complexity within practical limits through dynamic resource allocation.
3Ease of manufacture
If conventional MIMO detection methods are used, then implementation is simpler, but they fail to provide robust performance against large modulation orders and spatially correlated channels
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing candidate vectors and their associated metrics before the actual detection process. The system prepares a set of candidate spatial layer combinations and pre-calculates path metrics for these candidates, which are then used during detection. This preliminary preparation enables the detection algorithm to quickly evaluate and select the most likely transmitted symbols, providing robust performance for high-order modulation while maintaining implementation feasibility through structured pre-processing.
Data Source
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AI summary
UE (120) and method (500) in a UE (120), for MIMO detection of signals received from a radio network node (110), comprised in a wireless communication network (100) The method (500) comprises receiving (501) a signal of the radio network node (110). The method (500) also comprises establishing (510) a list of hypotheses candidate vector. Furthermore, the method (500) in addition comprises computing (511) path metrics of the established (510) list of hypotheses candidate vector, and thereby computing LLRs utilising the computed path metrics for achieving MIMO detection.