Semantic Signal Processing for Re-hosting CR/SDR Implementations SP/Radio Primitive Recognition Jiadi Yu, Yingying Chen 1

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 2

Radio-Level Abstraction – Abstract primitives at Radio-level Analyze the Code-level primitives to recognize Radio-level primitives Algebraic calculation: +, -, *, / Logic calculation: xor, nor, and Type conversions Relational Operator ： == ，！ = Conditional control: if… else…, while : Code level Signal Sources Signal Sinks Filters Signal Modulation Signal Demodulation Source coding Synchronization Equalization AGC OFDM locks : Radio level Primitives of Semantic Radio 3

Radio-Level Abstraction (cont’) Sources Code Radio level XML Presentation Code level XML Presentation Inference Engine Knowledge Base Radio Primitives Radio Level Abstraction Target Code Code level Abstraction SP module recognition 4

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 5

Learning Based Inference Engine Inference Engine Radio/Code Presentation Target Code Parser Higher- level Reinforcement learning Knowledge Base Learning Agent Information Inquiry Code Generate Conceptual Primitives lower-level SP module recognition 6

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 7

Primitive Recognition - Potential Approaches – Context-based Function names Comments – Behavior pattern Tree-based pattern recognition Machine learning -based pattern recognition 8

Context-based Recognition Information retrieval from Function names/Comments – Function names Direct comparison Fuzzy matching and identification – Comments Keyword-based Machine learning models 9

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. 10

Tree-based Pattern Recognition Primitive Recognition Tree architecture analyze Knowledge base Tree representation Source Target 11

An Example of QPSK two QPSK implementations Tree representation Binary Tree representation 12

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 13

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 14

A Simple Filter Example The basic element for the simple filter include: LOOP ACCUMLATION MULTIPLY ARRAY 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 16

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 SourceTarget 17

Thank You Comments & Questions? 18