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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

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

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

10. 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

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

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

13. 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

14. 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

15. 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

16. Machine Learning-based Pattern Recognition (Cont’) Advantage Fast & simple Disadvantage Accuracy can be low 16

17. 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

18. Thank You Comments & Questions? 18