MIMO Receiver RE Grouping for Complexity Reduction
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Solution Overview
Problem
In massive MIMO systems, the increasing number of data streams leads to high computational complexity and memory requirements, causing performance deterioration and increased system implementation costs, while existing algorithms struggle with interference cancellation, especially in multi-user and multi-cell environments.
Innovation Solution
The method involves forming resource element (RE) groups based on channel correlation, selecting a reference RE, and generating detection signals using channel information from the reference RE, which reduces computational complexity and operational burden by sharing preprocessing filters among REs, thereby improving interference cancellation performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of transmit and receive antennas is increased to improve communication capacity, then the throughput and interference cancellation capability are improved, but the computational complexity and memory requirements increase significantly
Solution Approach 1:
The patent divides the channel estimation process into two stages: first estimating channels at a coarse granularity (e.g., per resource block or per group of resource elements), then refining the estimation at a finer granularity only where needed. This segmentation reduces the overall computational complexity while maintaining accurate channel knowledge for detection.
Solution Approach 2:
The patent performs preliminary channel estimation and preprocessing before the actual signal detection. By pre-computing channel statistics, correlation matrices, and preliminary detection results, the system reduces the computational burden during real-time detection operations in massive MIMO scenarios.
2Reliability
If the number of antennas is increased to improve interference cancellation, then the ability to cancel interference is improved, but the operational burden and implementation cost increase
Solution Approach 1:
The patent applies interference cancellation techniques selectively rather than uniformly across all antenna pairs and resource elements. By identifying and canceling only the most significant interference components (partial action) or applying enhanced cancellation where interference is detected (excessive action), the system achieves good interference cancellation performance without the full operational burden of exhaustive processing.
3Measurement precision
If detailed channel estimation is performed for all resource elements, then the detection accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the time-frequency resource grid into multiple groups or clusters, performing channel estimation at different resolutions for different segments. High-precision estimation is applied only to critical segments (e.g., those containing reference signals or experiencing high interference), while lower-precision estimation suffices for other segments, thereby reducing overall computational complexity.
Solution Approach 2:
The patent applies different channel estimation accuracies to different spatial, temporal, or frequency locations based on local channel conditions. In regions with high correlation or low interference, coarser estimation is sufficient; in regions with rapid channel variation or high interference, finer estimation is applied. This local quality approach maintains detection accuracy where needed while reducing complexity elsewhere.
Data Source
AI summary
Disclosed are a method and a receiver for processing a received signal, the method dividing a plurality of resource elements (RE) to create RE groups by considering the inter-relationships between the channels of the plurality of REs, selecting a reference RE for each RE group, and generating, for each RE group, a detection signal from a received signal on the basis of channel information of the reference RE.