Adaptive Physical-Layer Channel Estimation for Lower UE Power
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Solution Overview
Problem
Existing channel estimation techniques in physical layer processing of wireless communication systems face challenges in balancing computational complexity and precision, leading to significant power consumption and inefficient use of resources, particularly in scenarios where channel conditions remain static or less variable.
Innovation Solution
A method and system for channel estimation in user equipment (UE) that involves receiving data packets, transmitting channel quality information (CQI) reports and network parameters, and selecting an appropriate channel estimation technique based on received channel information to optimize computational effort and power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high precision channel estimation techniques are used, then channel estimation precision is improved, but computational complexity and power consumption increase
Solution Approach 1:
The patent implements dynamic selection of channel estimation techniques based on real-time channel conditions. The system transitions between different estimation methods (e.g., from high-precision techniques like MMSE to lower-precision techniques like LS) according to channel variability, thereby adapting power consumption to actual needs rather than using fixed high-precision methods continuously.
Solution Approach 2:
The system changes the parameter of estimation precision based on channel conditions. By monitoring channel variability metrics, the system adjusts the precision level of channel estimation, selecting higher precision only when channel conditions warrant it, thus reducing overall power consumption while maintaining adequate performance.
2Measurement precision
If high precision channel estimation techniques are used, then channel estimation precision is improved, but computational complexity increases
Solution Approach 1:
The system dynamically adjusts the complexity of channel estimation algorithms based on channel conditions. When channels are stable, simpler estimation techniques are used; when channels are highly variable, more complex techniques are deployed. This dynamic adaptation reduces average computational complexity while maintaining precision when needed.
Solution Approach 2:
The system changes the precision parameter of channel estimation based on channel variability. By adjusting this parameter, the system controls the trade-off between estimation accuracy and computational complexity, using complex algorithms only when necessary.
3Measurement precision
If channel estimation is performed frequently, then channel estimation precision is improved, but power consumption increases
Solution Approach 1:
Instead of continuous channel estimation, the system implements periodic estimation with variable intervals. The estimation frequency is adjusted based on channel conditions - more frequent when channels are variable, less frequent when stable. This periodic approach with adaptive intervals reduces power consumption while maintaining adequate estimation precision.
Solution Approach 2:
The system dynamically adjusts the timing and frequency of channel estimation operations based on detected channel variability. When channels are stable, estimation is performed less frequently; when variability increases, estimation frequency increases. This dynamic scheduling optimizes the balance between precision and power consumption.
4Device complexity
If simple channel estimation techniques are used, then computational complexity is reduced, but channel estimation precision deteriorates
Solution Approach 1:
The system dynamically selects between simple and complex estimation techniques based on channel conditions. Simple techniques (low complexity) are used when channels are stable, while complex techniques (high precision) are deployed when channels are highly variable. This dynamic selection ensures adequate precision is maintained while minimizing computational complexity.
Solution Approach 2:
The system changes the precision parameter of channel estimation based on channel variability. By adjusting this parameter, the system ensures that precision is sufficient for current conditions without being excessively high when not needed, thus balancing complexity and precision.
5Measurement precision
If channel estimation is performed for all channels, then channel estimation precision is improved, but power consumption increases
Solution Approach 1:
The patent applies different channel estimation strategies to different channels based on their individual characteristics and importance. Critical channels receive high-precision estimation, while less critical channels use simpler methods. This localized approach to estimation quality reduces overall power consumption while maintaining precision where it matters most.
Solution Approach 2:
The system segments channels into different categories based on their variability and importance, applying different estimation techniques to each segment. This segmentation allows the system to optimize power consumption by not applying high-precision estimation uniformly across all channels, but only where necessary.
Data Source
AI summary
Disclosed is a method implemented in a user equipment (UE) for channel estimation in physical layer processing. The method may include: receiving a data packet from a next generation node B (gNB) for the physical layer processing; initiating the physical layer processing of the received data packet; transmitting a message including at least one channel quality information (CQI) report and one or more network parameters to the gNB; receiving channel information from the gNB, wherein the channel information is determined using the at least one of the CQI report, the one or more network parameters, and an uplink packet transmitted by the UE on an uplink channel; and selecting a channel estimation technique based on the received channel information.