RBIR Link Abstraction for MIMO-ML Receiver Performance Prediction
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Solution Overview
Problem
The Effective Exponential SINR (EESM) method for link performance prediction is cumbersome for adaptive modulation with Hybrid Automatic Repeat reQuest (HARQ) and difficult to extend to maximum likelihood detection (MLD) in single-input and multiple-output (MIMO) systems, requiring scalar normalization parameters and post-processing SINR, which increases complexity.
Innovation Solution
Employing a mutual information method for PHY abstraction/link performance prediction using the Received Bit Information Rate (RBIR) metric, directly mapping RBIR to Block Error Rate (BLER) for MLD receivers, particularly in SISO and MIMO systems, to reduce complexity and adapt to various modulation types and antenna configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If EESM method is used for link performance prediction, then prediction capability is provided, but device complexity increases due to requiring scalar normalization parameters for each MCS and post-processing SINR computation
Solution Approach 1:
The patent extracts the essential information needed for link performance prediction by directly using mutual information from the receiver output, eliminating the need for separate SINR computation and MCS-specific normalization parameter calculation. This extraction approach reduces complexity while maintaining prediction accuracy.
Solution Approach 2:
The mutual information metric serves as a universal performance indicator that works across different modulation schemes, coding rates, and receiver types (SISO, MIMO, ML, MMSE) without requiring scheme-specific parameters. This universal metric replaces the EESM approach that needed separate β parameters for each MCS.
2Reliability
If EESM method is used for adaptive modulation with HARQ, then link performance can be evaluated, but ease of operation deteriorates because codewords in different modulation types cannot be combined in retransmissions
Solution Approach 1:
The mutual information metric provides a universal basis for evaluating link performance that is independent of modulation type or coding scheme. This enables HARQ operations to combine codewords from different modulation types (e.g., QPSK and 16QAM) using the same metric, simplifying the operation of adaptive modulation with HARQ.
3Reliability
If EESM method is extended to MLD in SISO or MIMO cases, then link performance prediction is attempted, but device complexity increases and adaptability decreases because EESM uses post-processing SINR which is not suitable for ML detection
Solution Approach 1:
The patent changes the fundamental parameter used for link performance prediction from post-processing SINR (EESM approach) to mutual information computed directly from receiver outputs. This parameter change makes the method naturally suitable for ML detection in SISO and MIMO systems, improving both adaptability and accuracy.
Data Source
AI summary
A PHY abstraction mapping between the link level and system level performance is presented based on mapping between the mean RBIR (Received Bit Information Rate) of the transmitted symbols and their received LLR values after symbol-level ML detection in SISO/MIMO wireless systems, such as WiMAX. In MIMO antenna configuration, the mapping is presented for both vertical and horizontal encoding. An embodiment of this invention provides the PER/BLER prediction in the actual system, enabling the system to use more aggressive methods to improve the system performance.