Signal Slice Processing for Noisy Carrier Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional body communication systems face challenges in efficiently processing high-speed raw data due to power consumption and memory size constraints, particularly when dealing with noisy signals and uncertain carrier frequencies, which leads to increased computational and memory demands.
Innovation Solution
The use of 'slices' to represent signal data, combined with a warping operation, allows for efficient detection and decoding of signals with modest hardware and memory resources, enabling the creation of filters with selectably wide or narrow pass-bands and transforming slices to find an incoming carrier frequency in noisy environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a narrow bandwidth digital filter is applied to remove noise, then noise rejection is improved, but the likelihood of capturing the signal when carrier frequency is uncertain deteriorates
Solution Approach 1:
The patent segments the frequency search process into multiple discrete frequency points. Instead of using a single narrow filter that must be perfectly centered, the system divides the frequency range into segments and sequentially applies the narrow filter at each segment center frequency. This allows thorough frequency search while maintaining noise rejection, resolving the contradiction between narrow bandwidth filtering and signal capture reliability under frequency uncertainty.
2Reliability
If the receiver iteratively adjusts the center frequency of the narrow filter to search for the carrier, then signal detection capability is improved, but power consumption and time consumption worsen
Solution Approach 1:
The patent creates a digital copy of the received signal data and processes this copy through the iterative frequency search algorithm. By working with digital data copies rather than continuously adjusting hardware filters, the system achieves thorough frequency search capability while maintaining lower power consumption. The digital signal processing approach allows repeated analysis of the same data without additional hardware adjustments or re-acquisition.
3Measurement precision
If high resolution ADC is used to capture the signal, then signal fidelity is improved, but memory requirements worsen
Solution Approach 1:
The patent applies local quality by using high-resolution ADC only during the initial signal capture phase, then converting the captured data to a lower-resolution format for subsequent processing. The high-fidelity data is retained only for the duration needed to perform frequency search and signal detection, after which lower precision is sufficient. This localized use of high precision reduces overall memory requirements while maintaining signal fidelity when needed.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
A method may comprise receiving and sampling a signal. The signal may encode a data packet. A slice may be generated and stored comprising a pair of values for each of a selected number of samples of the signal representing a correlation of the signal to reference functions in the receiver. The presence of the data packet may then be detected and the detected packet decoded from the stored slices. The generating and storing slices may be carried out as the received signal is sampled. The sampled values of the signal may be discarded as the slices are generated and stored. The slice representation of the signal can be manipulated to generate filters with flexible bandwidth and center frequency.