Iterative MU-MIMO Beamforming Matrix Optimization
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Solution Overview
Problem
Conventional MU-MIMO systems face limitations in maximizing data rate capacity due to interference and noise, as they lack efficient methods for iteratively generating beamforming and matched filter matrices based on real-time channel estimates and feedback information.
Innovation Solution
An iterative method for generating beamforming and matched filter matrices in MU-MIMO systems, where current matrices are updated based on iteration counts and differences, channel noise values computed from noise power and path loss measurements, and modulation types, enabling concurrent signal transmission with reduced interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional MU-MIMO systems transmit multiple data streams concurrently to multiple users, then the system supports higher data rates and better spectral efficiency, but interference between users increases and makes it difficult to maximize aggregate channel capacity
Solution Approach 1:
The patent applies preliminary action by pre-computing beamforming matrices and matched filter matrices based on channel estimates before actual data transmission. The system performs iterative matrix computations using channel state information to optimize beamforming weights and matched filters in advance, thereby reducing interference and maximizing channel capacity before the actual concurrent transmission occurs.
2Productivity
If beamforming matrices are computed using iterative methods based on channel estimates and feedback, then data rate capacity increases and interference is suppressed, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by performing iterative beamforming matrix computations only for selected users in each iteration rather than all users simultaneously. The system computes beamforming matrices for a subset of users, updates channel estimates, and repeats the process, thereby reducing computational complexity in each step while still achieving improved data rate capacity and interference suppression through the iterative refinement process.
Data Source
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AI summary
A method and system for an iterative multiple user multiple input multiple output (MU-MIMO) communication system are presented. In one aspect, a current iteration beamforming matrix may be generated based on a current iteration matched filter matrix for each of a plurality of user devices. A subsequent iteration matched filter matrix may be generated based on the current iteration beamforming matrix for each of the plurality of user devices. A subsequent iteration beamforming matrix may be generated based on the subsequent iteration matched filter matrix for each of the plurality of user devices. A succeeding iteration beamforming matrix may be generated based on an iteration count value and/or based on one or more difference values. The one or more difference values may be computed based on the plurality of subsequent iteration beamforming matrices and the plurality of current iteration beamforming matrices generated for the plurality of user devices.