Hybrid Wireless Processing Chains with DNNs and Static Modules
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Solution Overview
Problem
Evolving wireless communication systems, particularly 5G and beyond, face challenges in providing sufficient data throughput due to signal distortions and complexity in processing higher frequency ranges, which increases costs and complexity, especially with user mobility introducing dynamic changes in transmission environments.
Innovation Solution
Implementing hybrid wireless communications processing chains that combine deep neural networks (DNNs) and static algorithm modules to adapt to changing conditions, reducing complexity while maintaining adaptability by using DNNs for dynamic modifications and static algorithms for simplified processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If higher frequency ranges are used to increase data capacity, then data throughput is improved, but signal reliability deteriorates due to multipath fading, scattering, atmospheric absorption, and diffraction
Solution Approach 1:
The patent replaces traditional signal processing methods with machine learning-based processing chains. The ML processing chain adapts to changing channel conditions caused by higher frequency propagation characteristics, dynamically adjusting processing parameters to maintain reliability while utilizing high-frequency bandwidth for increased throughput.
Solution Approach 2:
The patent implements dynamic adaptation to user mobility and changing channel conditions through machine learning models that continuously learn and adjust to the propagation environment. This allows the system to handle the dynamic nature of higher frequency signals affected by mobility, maintaining reliable communication despite rapid channel variations.
2Productivity
If complex hardware is used to transmit and receive higher frequencies, then data capacity is improved, but processing costs and device complexity increase
Solution Approach 1:
The patent substitutes complex hardware processing with software-based machine learning processing chains. By moving the complexity from the hardware domain to the software/algorithm domain, the system can achieve high-frequency signal processing capabilities without proportionally increasing hardware complexity and cost.
Solution Approach 2:
The patent changes the processing approach by using machine learning models that can adapt their parameters based on channel conditions. This allows the system to handle high-frequency signals with variable processing complexity rather than requiring consistently complex hardware, optimizing the balance between capacity and complexity.
3Device complexity
If traditional signal processing is used, then device simplicity is maintained, but adaptability to changing transmission environments deteriorates
Solution Approach 1:
The patent introduces dynamic adaptability through machine learning models that can learn and adjust to changing transmission environments. The ML processing chain continuously adapts to user mobility and channel variations, providing environmental versatility while maintaining a relatively simple device architecture through software-based adaptation rather than complex hardware reconfiguration.
Data Source
AI summary
Techniques and apparatuses are described for hybrid wireless communications processing chains that include deep neural networks (DNNs) and static algorithm modules. In aspects, a first wireless communication device communicates with a second wireless device using a hybrid transmitter processing chain. The first wireless communication device selects a machine-learning configuration (ML configuration) that forms a modulation deep neural network (DNN) that generates a modulated signal using encoded bits as an input. The first wireless communication device forms, based on the modulation ML configuration, the modulation DNN as part of a hybrid transmitter processing chain that includes the modulation DNN and at least one static algorithm module. In response to forming the modulation DNN, the first wireless communication devices processes wireless communications associated with the second wireless communication device using the hybrid transmitter processing chain.