Adaptive MU-MIMO Precoding with Hardware Impairment Compensation
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Solution Overview
Problem
In large-scale MIMO wireless communication systems, estimating channel quality information (CQI) for multiple users is challenging due to the impracticality of directly determining MU-MIMO CQI from measurements, especially with system errors and hardware impairments, which affects the selection of proper modulation and channel coding schemes and precoding methods.
Innovation Solution
A method to estimate MU-MIMO CQI using SU-MIMO CQI values, allowing for adaptive precoding by the base station, which collects system information, including CSI errors and hardware impairment data, to choose the precoding method that maximizes the sum rate, and compensates for hardware impairments by modifying the precoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement of MU-MIMO CQI is performed using pilot or test signals transmitted by BS, then accurate channel quality information is obtained, but signaling overhead and system complexity increase significantly
Solution Approach 1:
The patent uses SU-MIMO CQI as an intermediary parameter to estimate MU-MIMO CQI. Instead of directly measuring MU-MIMO CQI which requires extensive feedback, the system measures SU-MIMO CQI for each user individually and uses these measurements as input to estimate the multi-user channel quality. This intermediary approach significantly reduces feedback overhead while providing sufficient accuracy for precoding selection.
Solution Approach 2:
The patent creates a simplified model of MU-MIMO channel conditions by copying and processing individual SU-MIMO CQI measurements. The estimated MU-MIMO CQI is derived as a function of copied SU-MIMO CQI values from multiple users, along with channel correlation coefficients and interference information, avoiding the need for direct MU-MIMO measurement feedback.
2Productivity
If adaptive precoding is implemented to maximize sum rate, then system throughput is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent implements dynamic precoding selection where the base station adaptively chooses between Conjugate Beamforming (CB) and Zero-Forcing (ZF) precoding methods based on real-time channel conditions. The system dynamically calculates sum rate estimates for both precoding methods using the estimated MU-MIMO CQI and selects the method that maximizes throughput, allowing the system to adapt to changing channel correlations and interference levels.
Solution Approach 2:
The patent changes the precoding method parameter based on channel conditions. By comparing estimated sum rates for different precoding methods and selecting the optimal one, the system dynamically adjusts the precoding parameter to match current channel characteristics, thereby maximizing system throughput without requiring complex real-time optimization algorithms.
3Reliability
If hardware impairment data is collected and used to modify precoding, then system reliability is improved, but measurement and processing overhead increase
Solution Approach 1:
The patent performs preliminary characterization of hardware impairments at the user equipment side. The UE measures and stores hardware impairment parameters (such as phase noise, amplitude errors, and distortion characteristics) in advance, so that when precoding is needed, the pre-characterized impairment data can be directly used to compensate for hardware effects without requiring real-time measurement and feedback overhead.
Data Source
AI summary
This invention presents methods for estimating MU-MIMO channel information using SU-MIMO channel information to choose a modulation and channel coding appropriate for the quality of the MU-MIMO channels, for adaptively selecting MU-MIMO precoding methods based on estimations of a plural of UEs and for compensating hardware impairments in MU-MIMO precoding.