Photonic Compressive Sensing Receiver Architecture
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Solution Overview
Problem
Current receiver architectures face challenges in efficiently processing very wideband signals across tens or hundreds of GHz bandwidths due to limitations in electronic ADC performance, leading to high power consumption, large size, and high cost, especially when signals are sparsely occupied in the surveillance bandwidth.
Innovation Solution
The photonic compressive sensing receiver (PCSR) architecture uses photonic components for both sampling and compression, enabling a few high bit-depth ADCs to efficiently cover a large surveillance bandwidth by employing modulated wideband converters with pseudorandom sampling and dynamic scaling of sampling rates based on signal occupancy, allowing for scalable and efficient data conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Nyquist-rate sampling is used to cover very wide surveillance bandwidths, then complete signal coverage is achieved, but power consumption and system size become impractical
Solution Approach 1:
The surveillance bandwidth is divided into multiple contiguous frequency bands, each processed by a separate processing branch. This segmentation allows the system to monitor wide bandwidths while using multiple lower-rate ADCs instead of a single high-rate ADC, reducing overall power consumption and complexity.
Solution Approach 2:
The system dynamically adjusts the number of active processing branches and sampling rates based on signal occupancy detection. When signals are sparsely occupied, fewer branches are activated, reducing power consumption while maintaining complete coverage capability when needed.
2Productivity
If multiple high-speed ADCs are used to cover wide bandwidths, then signal acquisition capability is improved, but device complexity and cost increase
Solution Approach 1:
The system segments the wideband signal processing into multiple parallel branches, each handling a subset of the total bandwidth. This allows using multiple moderate-speed ADCs instead of fewer ultra-high-speed ADCs, reducing individual component complexity while maintaining overall acquisition capability.
Solution Approach 2:
Multiple processing branches share common components including the surveillance bandwidth filter, local oscillators, and digital signal processing resources. This multi-functionality reduces overall system complexity and cost while maintaining the ability to acquire signals across the entire wide bandwidth.
3Speed
If pseudorandom sampling is used to downconvert wide spectral range, then sub-Nyquist performance is achieved, but signal processing complexity increases
Solution Approach 1:
Pseudorandom sampling waveforms are applied in advance to downconvert the wide spectral range to a lower frequency before digital processing. This preliminary analog downconversion reduces the bandwidth that must be processed digitally, improving efficiency while the structured nature of the pseudorandom waveforms allows for manageable processing complexity through known aliasing patterns.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach results in a highly efficient and scalable system that reduces power consumption, size, and cost by dynamically adjusting sampling rates and activating/deactivating components based on signal occupancy, while maintaining high surveillance capabilities across extremely wide bandwidths.
Implementation Method 1
supply periodic spreading signals as modulation signals for modulating the outputs of corresponding optical sources, which are continuous-output sources and have distinct spectral outputs
Implementation Method 2
The optical modulator mixes the broadband signal from driver amplifier with the combined, multi-spectral optical signal. The resulting modulated optical signal includes spectrally distinct components corresponding to each of the optical sources
Implementation Method 3
Each optical output signal is then supplied to a corresponding optical receiver branch, each branch including at least a photodetector receiver
Data Source
AI summary
A photonic implementation of the modulated wideband converter (MWC) is described. The highly scalable compressive sensing receiver architecture uses photonic components for analog front-end compression and downconversion, allowing scalable data conversion over an extremely wide instantaneous surveillance bandwidth, limited only by the peak anticipated signal occupancy and application-dependent size, weight, and power constraints.