Sub-Band Spectrum Compression for Lower Wideband Processing Load
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Solution Overview
Problem
Wideband networks face increased computational complexity and memory requirements due to the translation of time domain signals into multiple frequency components, which is exacerbated by higher bandwidths, leading to higher processing loads and memory usage.
Innovation Solution
A sub-band processing system that partitions the frequency spectrum into sub-bands, using lossy compression to reduce data while maintaining original spectral relationships, and employs a Discrete Fourier Transform or Fast Fourier Transform to decompose signals into bins, allowing for lossless data reconstruction with minimal perceptual error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequency domain based adaptive filtering is used to process wideband signals, then filtering capability is improved, but computational complexity increases
Solution Approach 1:
The frequency spectrum is divided into multiple sub-bands, with each sub-band processed independently. This segmentation reduces the computational complexity of frequency domain filtering by breaking down the wideband signal processing into narrower, more manageable sub-band processing tasks, while maintaining the overall filtering capability through combination of sub-band results.
2Productivity
If bandwidth is increased to improve network throughput, then data transmission capacity is improved, but processing load increases
Solution Approach 1:
By segmenting the wideband signal into multiple sub-bands, the processing load for each individual sub-band is reduced compared to processing the entire wideband signal as a single frequency domain operation. This allows the system to handle increased bandwidth and higher throughput while maintaining manageable processing requirements through parallel or sequential sub-band processing.
Data Source
AI summary
A sub-band processing system that reduces computational complexity and memory requirements includes a processor and a local or distributed memory. Logic stored in the memory partitions a frequency spectrum of bins into a smaller number of sub-bands. The logic enables a lossy compression by designating a magnitude and a designated or derived phase of each bin in the frequency spectrum as representative. The logic renders a lossless compression by decompressing the lossy compressed data and providing lost data based on original spectral relationships contained within the frequency spectrum.