Wireless Sensor Network Spectrum Reconstruction via SFFT and COA
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Solution Overview
Problem
Current wireless sensor networks lack an effective solution for deploying sensor nodes and reconstructing spectra in a manner that maintains existing operation and communication modes while achieving high sampling rates and low complexity, especially with the increasing demand for real-time spectrum analysis in 5G and IoT environments.
Innovation Solution
A deployment structure combining Sparse Fast Fourier Transform (SFFT) and Computation Over Air (COA) with spectrum collection sensor nodes and a sink node, where sensor nodes sample signals, perform SFFT, and transmit them for superposition computation, allowing the sink node to reconstruct spectra through frame synchronization and post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional high-rate sampling is used to achieve high sampling rates, then sampling accuracy is improved, but hardware cost and system complexity increase due to expensive high-rate ADC requirements
Solution Approach 1:
The patent segments the high-rate sampling task into multiple low-rate sampling operations performed by different sensor nodes. Instead of using a single high-rate ADC, multiple low-rate ADCs are distributed across different nodes, each performing sampling at lower rates. The segmented sampling operations are then combined through COA to achieve the equivalent effect of high-rate sampling, thereby reducing hardware cost while maintaining sampling accuracy.
Solution Approach 2:
The patent replaces the mechanical/electronic system of a single high-rate ADC with a distributed system combining multiple low-rate ADCs, wireless transmission channels, and signal processing algorithms. The physical hardware complexity of high-rate conversion is substituted by a distributed computational approach using SFFT and COA, achieving the same sampling accuracy through algorithmic rather than purely hardware means.
2Measurement precision
If data from all sensors is collected individually to achieve accurate spectrum reconstruction, then measurement precision is improved, but data transmission amount and processing complexity increase
Solution Approach 1:
The patent merges the data collection and processing operations of multiple sensor nodes into a single unified operation through Computation Over Air. Instead of collecting and processing data from each sensor individually, the patent combines the sampling operations in the air domain, allowing multiple nodes to contribute their sampled data simultaneously through wireless transmission. The sink node then performs unified processing on the combined signal, significantly reducing the total data transmission amount while maintaining spectrum reconstruction accuracy.
Solution Approach 2:
The patent enables the wireless channel itself to perform the computation function through COA. The air interface automatically superimposes and combines the sampled signals from multiple nodes during transmission, eliminating the need for separate data collection and processing steps at each node. This self-service approach allows the communication medium to contribute to the computational task, reducing overall system complexity and data handling requirements.
3Measurement precision
If traditional FFT algorithm is used for spectrum analysis, then spectrum reconstruction is achieved, but computational complexity increases compared to sparse signals
Solution Approach 1:
The patent extracts and exploits the sparsity property of the spectrum signal to simplify the FFT computation. Instead of performing a complete FFT on all frequency components, the patent identifies and processes only the sparse non-zero components using SFFT algorithms. This extraction of the essential sparse information from the full spectrum allows for significantly reduced computational complexity while maintaining the accuracy needed for spectrum reconstruction.
4Measurement precision
If more sensor nodes are deployed to improve spectrum analysis capability, then measurement precision is improved, but system complexity and data processing burden increase
Solution Approach 1:
The patent enables additional sensor nodes to contribute to spectrum analysis without proportionally increasing system complexity. Through COA, the wireless channel automatically combines the contributions from multiple nodes, and the sink node processes the combined signal as a single entity. This self-service mechanism allows the system to scale by adding nodes while maintaining manageable complexity, as the infrastructure automatically handles the aggregation and processing of data from any number of participating nodes.
Data Source
AI summary
A wireless sensor network deployment structure combined with SFFT and COA and a frequency spectrum reconstruction method therefor. The wireless sensor network deployment structure includes: frequency spectrum acquisition sensor nodes dispersed in each region, and a sink node, wherein all the frequency spectrum acquisition sensor nodes have the same structure, and include: a broadband frequency spectrum antenna, a delayer, an ADC, a first baseband processing module, a DAC and a transmitting antenna that are successively connected; all the frequency spectrum acquisition sensor nodes are cooperated to realize SFFT and COA of signals; the signals transmitted by all the frequency spectrum acquisition sensor nodes are superimposed over the air and received by the sink node; and the sink node extracts a data domain from a received signal frame by post-processing, thereby completing frequency spectrum reconstruction. The solution can be easily deployed in an existing wireless sensor network without changing the traditional ADC working mode and communication mode; moreover, the delay is shorter, the sampling rate of the reconstructed frequency spectrum is higher, and the complexity is lower.