Spiking Neural Network Decoding for Low-Power Wireless Signals
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Solution Overview
Problem
Decoding wireless signals using ANNs consumes significant power, processing resources, and memory, and errors lead to retransmissions, increasing network overhead and congestion.
Innovation Solution
Implementing spiking neural networks (SNNs) for wireless signal decoding and encoding, which generate rate or latency coded outputs, reducing resource consumption and improving decoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ANNs are used for wireless signal decoding, then decoding capability is provided, but power consumption and resource usage increase significantly
Solution Approach 1:
The patent transforms the continuous activation functions and weights of ANNs into discrete spiking events and temporal patterns in SNNs. By changing the parameter representation from continuous to discrete temporal domains, the system achieves comparable decoding capability with significantly reduced power consumption, as SNNs only consume energy during spike events rather than continuous computation.
Solution Approach 2:
The patent replaces the traditional ANN computational mechanism with an SNN mechanism that mimics biological neural processing. This substitution uses temporal coding and spike-based communication instead of continuous activation values, enabling the system to maintain decoding reliability while reducing the computational overhead and energy consumption associated with traditional ANN operations.
2Reliability
If ANNs are used for wireless signal decoding, then decoding is performed, but processing resources and memory consumption increase
Solution Approach 1:
The patent converts ANN parameters (continuous weights and activations) into SNN parameters (discrete spike times and rates). This parameter transformation reduces the memory requirements for storing model parameters and decreases the computational complexity of forward propagation, as SNNs utilize event-driven processing that only activates when spikes occur, rather than requiring continuous computation across all neurons.
3Device complexity
If traditional decoding is used, then processing is simpler, but decoding accuracy decreases leading to retransmissions
Solution Approach 1:
The patent introduces temporal dynamics into the decoding process through SNNs, where the timing and rate of spikes carry information about the decoded symbols. This dynamic temporal processing enables the system to achieve high decoding accuracy by exploiting the temporal patterns in the received signals, while maintaining a relatively simple architecture compared to deep ANN models.
Data Source
AI summary
In some implementations, a wireless signal decoder may include a receiver configured to receive a wireless signal and convert the wireless signal into a digital signal. The wireless signal decoder may further include a processor configured to input the digital signal into a spiking neural network (SNN) and receive at least one predicted data symbol as output from the SNN. The at least one predicted data symbol may include a rate coded output or a latency coded output.