Semantic Signal Processing for Re-hosting CR/SDR Implementations
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Semantic Signal Processing for Re-hosting CR/SDR Implementations
SP/Radio Primitive Recognition
Jiadi Yu, Yingying Chen
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SSP Framework
Abstract conceptual primitives (“Thing, Place, Path, Action, Cause”) from existing implementations of signal processing modules/systems in source code
Represent the implementation profile of signal processing modules/systems based on cognitive linguistics
Parse cognitive-linguistics-based representation and generate implementation code in the target platform
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Radio-Level Abstraction– Abstract primitives at Radio-level
• Analyze the Code-level primitives to recognize Radio-level primitives
Algebraic calculation: +, -, *, /Logic calculation: xor, nor, andType conversionsRelational Operator:
==,! =Conditional control: if… else…, while :
Code levelSignal SourcesSignal SinksFiltersSignal ModulationSignal DemodulationSource codingSynchronizationEqualizationAGCOFDM locks :
Radio level
Primitives of Semantic Radio
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Radio-Level Abstraction (cont’)
SourcesCode
Radio levelXML
Presentation
Code level XML
Presentation
Inference EngineKnowledge
Base
RadioPrimitives
Radio LevelAbstraction
TargetCode
Code level
Abstraction
SP module recognition
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Learning Based Inference Engine– Inference engine is able to understand the what level
primitives in the semantic presentation need to parsing
– Inference engine is able to know what primitives need to generate target code and what primitives just use code from code library
– Machine knows how to implement any-level primitives in the target code
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Learning Based Inference Engine
Inference Engine
Radio/CodePresentation
TargetCodeParser
Higher-level
Reinforcement learning
Knowledge Base
Learning Agent
InformationInquiry
CodeGenerate
Conceptual Primitives
lower-level
SP module recognition
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SP/Radio Primitive Recognition • Objective
– Automated recognition of functionality of a SP/Radio primitive
– Automated recognition of functions from knowledge library to perform desired action
– Recognize the equivalence of two different implementations
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Primitive Recognition - Potential Approaches
– Context-based• Function names
• Comments
– Behavior pattern• Tree-based pattern recognition
• Machine learning -based pattern recognition
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Context-based Recognition
• Information retrieval
from Function names/Comments
– Function names
Direct comparison
Fuzzy matching and identification
– Comments Keyword-based
Machine learning models
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• The representation architecture based on cognitive linguistics of the signal processing implementation is a Tree Structure.
Tree-based Pattern Recognition
• Each signal processing module can be represented as a behavior pattern using lower-level primitives
• Each signal processing module can be represented as a tree architecture.
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Tree-based Pattern Recognition
Primitive Recognition
Tree architecture
analyze
Knowledge base
Tree representation Source
Target
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An Example of QPSK
• two QPSK implementations
Tree representatio
n
Binary Tree representatio
n
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Tree-based Pattern Recognition(Cont’)
• Advantage
Direct comparison Accuracy can be high
• Disadvantage
Compare with all modules/functions of Knowledge base Slow, high computational cost
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Machine Learning-based Pattern Recognition
– Based on the correlation between the radio primitive and identified features
– Potential Features
• Lower-level primitives– Example: lookup table
• Hierarchical architecture- Example: QPSK includes a lookup table primitive
• Numerical attributes- Example: integers, real numbers
• Input/output variable types and ranges- Example: Input/output parameters of a filter is array
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A Simple Filter Example
The basic elementfor the simplefilter include:
LOOPACCUMLATION MULTIPLYARRAY
void main(){for(i = 0; i < N ; i = i + 1){
k = N - i;temp = tap[i] * input[k];sum = sum + temp;
}} The code segments probably
implement functionality of a filter 15
Machine Learning-based Pattern Recognition
(Cont’)
• Advantage
Fast & simple
• Disadvantage
Accuracy can be low
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ML and Tree-based Pattern Recognition
• Low computational cost and high accuracy
ML-based Pattern Recognition
Tree-based Pattern Recognition
First step
Second step
similar primitives
Primitive Recognition
Source Target
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Thank You
Comments & Questions?
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