Waveform Pattern Generator for Arbitrary Data Stream Simulation
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Solution Overview
Problem
Existing waveform generators are limited in generating long data patterns and are restricted by the length and content of the input signals, failing to produce signals that accurately represent real-world transmission variations.
Innovation Solution
The system generates output signals by assigning respective waveform patterns to groups of successive data values in an input data stream, allowing for arbitrary signal generation based on data content, including transitions and historical dependencies, using a set of predefined patterns and rules to create a continuous waveform shape that corresponds to the input data stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing waveform generators are used to generate output signals, then the generation process is limited by the length and content of stored input signals, but the ability to generate long and arbitrary data patterns is required
Solution Approach 1:
The input data stream is divided into groups of successive data values, with each group assigned a respective waveform pattern. This segmentation allows the system to process and generate arbitrary long data patterns by handling them in manageable groups rather than requiring complete storage of entire long sequences.
Solution Approach 2:
Waveform patterns are pre-defined and stored in the waveform pattern memory for different data groups. When a group of successive data values is received, the corresponding pre-defined waveform pattern is immediately assigned and generated, enabling continuous arbitrary pattern generation without storing the entire input sequence in advance.
2Duration of action of moving object
If arbitrary waveform generation is implemented, then long data patterns can be generated, but the system requires processing and storing of extensive data sequences
Solution Approach 1:
The system segments the arbitrary long input data stream into groups of successive data values. Only the current group needs to be processed and assigned a waveform pattern, rather than storing the entire long sequence. This enables generation of extended duration patterns with minimal data storage requirements.
Solution Approach 2:
Waveform patterns are pre-defined and stored in memory for different possible data groups. When processing the input stream, the system only needs to retrieve the appropriate pre-defined pattern for the current group, avoiding the need to store and process extensive raw input data sequences.
3Productivity
If groups of successive data values are processed with assigned waveform patterns, then continuous arbitrary waveform generation is achieved, but the system complexity increases
Solution Approach 1:
The processing system divides the input data stream into groups and assigns waveform patterns to each group independently. This modular approach enables continuous waveform generation through systematic processing of successive groups, maintaining productivity while organizing complexity into manageable segments.
Solution Approach 2:
The waveform pattern memory stores universal patterns that can be assigned to different groups of successive data values based on their characteristics. This multi-functional approach allows the same hardware structure to handle various data patterns and transitions, achieving continuous arbitrary waveform generation without proportionally increasing system complexity.
Data Source
AI summary
An output signal is generated from a received input data stream representing a sequence of digital data values. For each group of successive data values in the sequence of data values, a respective waveform pattern is assigned in dependence of the data content of the respective group of successive data values. The output signal is generated by generating the assigned respective waveform patterns corresponding to the input data stream.