MIMO MCS Selection via Error Vector Magnitude
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Solution Overview
Problem
Standard RSSI measurements are insufficient to accurately determine the total error rate performance in MIMO systems, leading to suboptimal MCS selection in wireless communication systems, particularly due to the inability to account for burst errors caused by fading and interference.
Innovation Solution
The method involves computing an average error vector magnitude (EVM) to calculate signal-to-noise ratio (SNR), which is then used with a pre-stored or dynamically adjusted SNR vs. MCS table to optimize the selection of modulation, coding rate, and number of streams, allowing for accurate MCS determination by either the receiver or transmitter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard RSSI measurement is used for MCS selection, then the system is simple to operate, but the measurement precision is insufficient to accurately determine total error rate performance
Solution Approach 1:
The patent introduces Error Vector Magnitude (EVM) as an intermediary measurement that captures both random bit errors and burst errors. EVM serves as a mediator between the simple RSSI measurement and the complex total error rate performance assessment, providing a more accurate indicator for MCS selection without requiring direct measurement of all error types.
Solution Approach 2:
The patent replaces the traditional RSSI-based measurement system with an EVM-based measurement system. This substitution transitions from a simple signal strength measurement to a more comprehensive quality metric that accounts for modulation accuracy and various error sources, thereby improving measurement precision for MCS selection.
2Reliability
If convolutional coding is used to correct bit errors, then random bit errors can be corrected, but burst errors cannot be easily corrected
Solution Approach 1:
The patent changes the approach from attempting to correct all error types with convolutional coding to using EVM as a predictive parameter for MCS selection. By monitoring EVM, the system can proactively adjust MCS to prevent both random and burst errors, rather than relying solely on post-error correction capabilities.
Solution Approach 2:
The patent implements feedback through EVM measurement and reporting mechanisms. The receiver measures EVM and provides feedback to the transmitter, enabling dynamic MCS adjustment based on actual channel conditions including both random and burst error potentials, thereby improving adaptability across different error types.
3Productivity
If a single MCS is selected based on RSSI, then the selection process is simple, but the data rate and error correction capabilities are not optimized
Solution Approach 1:
The patent transitions from static RSSI-based MCS selection to dynamic EVM-based MCS selection. EVM provides real-time feedback on channel quality including the impact of fading and interference, enabling the system to dynamically adjust MCS to optimize data rate while maintaining reliability under varying channel conditions.
Solution Approach 2:
The patent uses EVM measurement to perform preliminary assessment of channel conditions before selecting MCS. By evaluating EVM in advance, the system can predict potential error rates and select appropriate MCS proactively, rather than reacting to errors after transmission, thereby optimizing productivity while managing complexity.
Data Source
AI summary
An accurate total error rate performance can be measured using a computed error vector magnitude (EVM) per stream. Using this EVM, the receiver or the transmitter can advantageously generate an optimized modulation and coding scheme (MCS) that corresponds to a specific number of streams, modulation and coding rate for the transmitter. For example, the receiver can compute an SNR from the EVM and then use an SNR vs. MCS table to determine the optimized MCS. In contrast, the transmitter can receive an EVM-to-RSSI mapping and an EVM-to-MCS mapping from the receiver. These mappings and an EVM can facilitate selecting the optimized MCS.