Broadband Satellite Decoding With Shared Iterations Across Channels
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Solution Overview
Problem
Traditional satellite communication systems face challenges in efficiently demodulating and decoding multiple channels simultaneously due to the need for separate tuners and demodulators for each channel, which increases complexity and cost, and requires significant processing power for error correction, especially when channels have different signal-to-noise ratios.
Innovation Solution
A multi-channel demodulator and decoder system that uses a single broadband tuner and shared iterative decoding resources to process multiple channels within a contiguous bandwidth section, optimizing processing capacity and reducing complexity by allocating resources based on channel quality and signal-to-noise ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate tuners and demodulators are used for each channel, then channel reception capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple channel reception functions into a single demodulator by using frequency-domain processing. Instead of having separate demodulators for each channel, the system uses a single demodulator that can process multiple frequency-multiplexed channels simultaneously through FFT-based techniques, thereby reducing device complexity while maintaining multi-channel reception capability.
Solution Approach 2:
The demodulator is designed with multi-functionality to handle multiple channels across different frequency bands. By implementing a universal demodulation architecture that can adapt to different channel configurations and frequency allocations, the system achieves versatile channel reception without requiring dedicated hardware for each channel.
2Reliability
If separate decoders are used for each channel, then decoding reliability is improved, but processing power requirements increase
Solution Approach 1:
The patent merges multiple channel decoding operations into a single decoder by exploiting the frequency-domain structure of the received signal. The decoder processes all channels simultaneously using shared computational resources, including common FFT/IFFT operations and iterative decoding mechanisms, thereby reducing overall processing power requirements while maintaining decoding reliability for each channel.
Solution Approach 2:
The system changes the processing domain from time-domain separate channel processing to frequency-domain joint processing. By transforming the received signal into the frequency domain using FFT, the system can efficiently extract and decode multiple channels with different modulation schemes and code rates using a unified decoding framework, reducing computational complexity and processing power consumption.
3Measurement precision
If more iterations are allocated for error correction, then decoding accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements dynamic iteration allocation where the number of decoding iterations is adaptively adjusted based on channel conditions, signal quality metrics, and priority requirements. The system can increase iterations for channels with poor signal quality or high priority, while using fewer iterations for channels with good quality or lower priority, thereby optimizing the trade-off between decoding accuracy and processing time.
Solution Approach 2:
The system applies partial iteration strategies where not all channels require the maximum number of iterations. By using early stopping criteria and quality-based thresholds, the system performs sufficient iterations to achieve acceptable decoding accuracy for most channels without unnecessarily increasing processing time, applying excessive iterations only when specifically needed for difficult-to-decode channels.
Data Source
AI summary
Multiple channels of received data are processed by a multiple channel demodulation and error correction decoding engine. The statistical uncertainty of processing channels with an iterative decoder are averaged across all the channels to reduce the total processing power required of the decoding engine compared to processing each channel with a separate engine. A set of input buffers holds blocks of data for each channel needing decoding. A quality measure is computed on each input block to set the priority and iteration allocation of decoding in the common decoder. The input RF signal is digitized by a broadband tuner that processes some or all of the channels to feed the multiple channel demodulator and decoder. Multiple decoded video data streams are output.