Transmit Antenna Selection Throughput Prediction for MIMO Mode Switching
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently predicting and adapting transmit antenna selection (TAS) modes to optimize throughput in dynamic wireless networks, particularly in 5G and beyond, due to varying channel conditions and user equipment (UE) characteristics.
Innovation Solution
A base station (BS) processor performs transmit antenna selection (TAS) throughput prediction using a linear model that maps channel quality indicator (CQI) to signal-to-noise ratio (SNR), incorporating input metrics like CQI, rank indicator, modulation and coding scheme, and beamforming loss, to select between multiple input multiple output (MIMO) modes based on predicted throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional throughput prediction methods are used in wireless networks, then the system complexity is low, but the throughput prediction accuracy is insufficient for dynamic network conditions
Solution Approach 1:
The patent segments the throughput prediction into multiple linear models, each dedicated to specific MIMO modes (TAS, MIMO). Each model processes specific input metrics (CQI, SNR, beamforming loss, SRS) independently, allowing the system to select appropriate models based on current network conditions, thereby improving accuracy without requiring a single overly complex model.
Solution Approach 2:
The system dynamically selects between different linear models based on current network conditions and MIMO mode configurations. The processor adapts the prediction approach by choosing appropriate models for TAS or MIMO modes, enabling the system to respond to changing wireless conditions while maintaining manageable complexity through conditional model selection rather than a monolithic complex model.
2Measurement precision
If multiple input metrics are considered for throughput prediction, then the prediction accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent extracts and processes only the most critical input metrics (CQI, SNR, beamforming loss, SRS) that have the greatest impact on throughput prediction. By focusing on these key parameters rather than processing all available network data, the system achieves high prediction accuracy while minimizing computational power consumption through selective metric extraction and processing.
Solution Approach 2:
The system transforms input metrics into standardized parameters suitable for linear model processing. The processor performs parameter mapping (e.g., CQI to SNR conversion) and normalization to convert diverse input metrics into a unified format that linear models can efficiently process, reducing computational complexity while preserving the essential information needed for accurate throughput prediction.
3Productivity
If dynamic MIMO mode selection is implemented, then the network throughput is optimized, but the system complexity and processing overhead increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring separate linear models for different MIMO modes (TAS and MIMO) before runtime. The processor prepares the prediction framework in advance with mode-specific models, so when dynamic mode selection is needed, the system can quickly switch between pre-prepared models rather than complexly determining modes in real-time, thus optimizing throughput while managing system complexity through advance preparation.
Data Source
AI summary
An embodiment provides for calculating transmit antenna selection (TAS) throughput prediction for MIMO mode selection using a linear model that may use a piece-wise linear mapping from effective downlink (DL) signal to interference and noise ratio (SINR) in a decibel (dB) domain to spectral efficiency (SE) and the DL SINR may be estimated by uplink SINR, beamforming loss and a channel quality indicator value.