CNN-Based Universal Signal Demodulator
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently decoding and demodulating signals across various protocols and modulation schemes due to the need for separate, dedicated hardware and high-power processors, leading to increased complexity and cost.
Innovation Solution
A CNN-based demodulator/decoder system that uses trainable convolutional neural networks to automatically decode signals by learning filters from labeled data, allowing for adaptation to different protocols and modulation schemes without requiring new hardware, thus reducing design time and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate dedicated decoders with predefined settings are used for each protocol and modulation scheme, then decoding accuracy is improved, but device complexity and hardware cost increase
Solution Approach 1:
The patent implements a universal decoder that can handle multiple protocols and modulation schemes through software configuration rather than dedicated hardware for each protocol. The system uses a single decoder architecture that can be programmatically adapted to different protocols (NFC Type A, B, Felica, MIFARE, Bluetooth variants, Wi-Fi scenarios) through configurable parameters and algorithms, eliminating the need for separate hardware decoders for each protocol while maintaining decoding accuracy
Solution Approach 2:
The patent changes the approach from fixed hardware settings to configurable software parameters. The decoder uses programmable parameters such as correlation thresholds, integration times, and algorithm selections that can be adjusted based on the specific protocol and modulation scheme being used, allowing one hardware platform to adapt to multiple protocols without physical reconfiguration
2Adaptability or versatility
If software defined radio is used to move decoder from hardware to software implementation, then adaptability is improved, but power consumption increases
Solution Approach 1:
The patent implements dynamic adaptability where the decoder can switch between different decoding algorithms and parameter sets based on the detected protocol and signal characteristics. The system dynamically adjusts correlation window sizes, integration periods, and threshold values according to the specific modulation scheme being used, allowing efficient software-based adaptation without requiring maximum processor power for all operations
Solution Approach 2:
The patent segments the decoding process into distinct stages that can be independently optimized: signal detection, protocol identification, parameter configuration, and actual decoding. This segmentation allows the system to activate only the necessary processing stages for each protocol type, reducing overall power consumption compared to continuously running full-processor software decoding for all possible protocols
Data Source
AI summary
Presented are systems and methods for automatically decoding and demodulating radio signals in a communication network. Embodiments utilize a one-dimensional (1D) convolutional neural network (CNN) as the key architecture of a decoder that utilizes one or more 1D convolution windows to perform convolution operations on to-be-decoded or demodulated input signals received by the communication network. In embodiments, this may be achieved by receiving, at a CNN correlator implemented in a decoder, an input signal that comprises unknown data and applying to the input signal, in the discrete time domain, a convolution to obtain a CNN correlator output to which an activation may be applied to decode the input signal and output the decoded signal.