MIMO Configuration Selection via SINR Thresholds
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Solution Overview
Problem
5G telecommunication networks face challenges in optimizing MIMO configurations due to varying radio frequency conditions, leading to potential losses in signal quality, increased noise, and interference, as existing methods like SRS-based and CQI-based approaches have different resource consumption and performance characteristics.
Innovation Solution
A base station determines the MIMO configuration by selectively using CQI values or SRS based on a SINR threshold, adjusting the SINR value to optimize spectral efficiency and minimize interference, allowing for adaptive beamforming and precoding to suit changing channel conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SRS-based method is used to determine MIMO configuration, then measurement precision is improved, but use of energy by stationary object increases
Solution Approach 1:
The patent implements dynamic selection between SRS-based and CQI-based methods based on SINR conditions. The base station adaptively switches measurement approaches: using SRS when channel conditions are good (high SINR) to achieve high precision, and using CQI when conditions are poor (low SINR) to conserve energy. This dynamic adaptation resolves the contradiction by making the measurement precision and energy consumption variable rather than fixed.
Solution Approach 2:
The patent changes the operational parameters of the measurement system based on SINR thresholds. When SINR exceeds a threshold, the system uses SRS-based measurement with higher precision but higher energy consumption. When SINR falls below the threshold, it switches to CQI-based measurement with lower precision but lower energy consumption. This parameter change strategy allows the system to optimize the trade-off between measurement precision and energy usage according to actual channel conditions.
2Use of energy by stationary object
If CQI-based method is used to determine MIMO configuration, then use of energy by stationary object is reduced, but measurement precision deteriorates
Solution Approach 1:
The system dynamically adjusts the measurement method based on SINR conditions. When channel conditions are poor (low SINR), the system selects CQI-based measurement which consumes less energy, accepting reduced precision. When conditions improve (high SINR), it switches to SRS-based measurement for higher precision. This dynamic switching resolves the contradiction by allowing the system to operate at different precision-energy trade-off points depending on conditions.
Solution Approach 2:
The patent implements parameter changes by switching between two distinct measurement modes based on SINR thresholds. The CQI-based mode operates with lower energy consumption and acceptable precision for poor channel conditions, while the SRS-based mode provides high precision when energy can be allocated accordingly. This parameter-based selection strategy resolves the precision-energy contradiction.
3Productivity
If MIMO configuration is optimized for ultra-high speeds, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic MIMO configuration optimization based on SINR conditions. The base station adaptively adjusts MIMO parameters (such as rank indication, precoding matrix indicator) according to real-time channel quality. When SINR is high, more complex MIMO configurations with higher ranks can be used to maximize throughput. When SINR is low, simpler configurations are selected to maintain reliability. This dynamic adaptation enables high productivity when conditions permit while managing complexity when conditions are poor.
Solution Approach 2:
The system changes MIMO operational parameters based on SINR thresholds and measurement results. By selecting appropriate MIMO configurations (different numbers of spatial layers, precoding schemes) according to channel conditions, the system achieves high data transmission speeds when channel quality supports complex configurations, while using simpler configurations when quality is poor, thus managing the productivity-complexity trade-off.
4Object-affected harmful factors
If adaptive beamforming is implemented to reduce interference, then object-affected harmful factors are reduced, but device complexity increases
Solution Approach 1:
The patent implements adaptive beamforming that dynamically adjusts beam directions and precoding based on SINR conditions and channel measurements. The base station uses the selected measurement methods (SRS or CQI) to inform beamforming decisions, creating a coordinated adaptive system. This dynamic beamforming reduces interference and noise by directing energy toward intended receivers and away from others, while the complexity is managed through condition-based adaptation rather than continuous complex processing.
Solution Approach 2:
The system changes beamforming parameters (beam directions, precoding matrices, spatial filters) based on SINR thresholds and channel measurement results. By adapting these parameters according to actual channel conditions, the system effectively reduces interference and noise impact on communications. The parameter-based adaptation allows the system to achieve interference reduction benefits while managing computational complexity through condition-dependent configuration selection.
Data Source
AI summary
In some implementations, a device may determine a signal to interference and noise ratio (SINR) value associated with a communication channel between a user equipment and the device. The device may select, based on the SINR value, a channel quality indicator (CQI) value associated with the communication channel or a sounding reference signal (SRS) from the UE to determine a multiple-input and multiple-output (MIMO) configuration for transmitting data to the UE. The device may determine the MIMO configuration according to the CQI value based on the SINR value being a first value. The device may determine the MIMO configuration according to the SRS based on the SINR value being a second value that is different than the first value. The device may transmit the data to the UE using the MIMO configuration.