MIMO Receiver Group-Wise Covariance Matrix Preprocessing
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Solution Overview
Problem
In massive MIMO environments, the complexity and memory requirements of existing receiver algorithms increase exponentially with the number of received streams, leading to performance deterioration and high implementation costs, while also struggling with interference cancellation from multiple users and neighboring cells.
Innovation Solution
The method involves determining whether to use covariance matrices based on the number of receive antennas, signal layers, and iteration number, generating preprocessing filters to reduce computational complexity, and applying algorithms like the conjugate gradient or Newton method to efficiently generate detection signals, thereby optimizing computational complexity and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing receiver algorithms are used in massive MIMO environments, then detection accuracy can be maintained, but computational complexity and memory requirements increase exponentially with the number of received streams
Solution Approach 1:
The received signal is divided into multiple groups, and detection is performed separately for each group. This segmentation approach reduces the computational complexity from exponential to linear or polynomial with respect to the number of received streams, while maintaining detection accuracy through group-wise processing
Solution Approach 2:
Preprocessing filters are applied to the received signals before detection to reduce interference and improve signal quality. This preliminary action simplifies the subsequent detection process, reducing computational complexity while maintaining or improving detection accuracy
2Productivity
If the number of transmit and receive antennas is increased to improve communication capacity, then throughput increases, but implementation complexity and memory requirements increase
Solution Approach 1:
The large-scale antenna system is divided into multiple groups of transmit and receive antennas. Detection is performed in a group-wise manner, which reduces the computational burden and memory requirements that would otherwise scale exponentially with the total number of antennas, enabling practical implementation of massive MIMO systems
Solution Approach 2:
The invention changes the detection parameter from processing all streams simultaneously to processing groups of streams separately. This parameter change allows the system to support a large number of antennas and streams while keeping computational complexity and memory requirements at manageable levels
3Device complexity
If preprocessing filters are shared in a group unit, then computational complexity is reduced, but performance deterioration may occur
Solution Approach 1:
The system uses group-wise preprocessing filters that are shared within each group. This segmentation approach reduces computational complexity through filter sharing while maintaining receiver performance by ensuring that each group has its own dedicated preprocessing filter that is optimized for that specific group's characteristics
Data Source
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AI summary
Disclosed is a method for processing a reception signal, and a MIMO receiver, the method comprising a step in which: a reference resource element (RE) is selected within an RE group that comprises a plurality of REs; generated is a preprocessing filter which will be shared by the plurality of REs in the RE group on the basis of channel information of the reference RE; on the basis of channel information of the plurality of REs other than the reference RE, generated is a covariance matrix with respect to each of the REs other than the reference RE; and reception signals with respect to each of the plurality of REs are offset by selectively using the preprocessing filter and the covariance matrix, and thereby detection signals with respect to the RE group are generated.