A genetic algorithm (GA) is used to de sign Spectrally Modulated, Spectrally Encoded (SMSE) waveforms while characterizing the impact of parametric variation on coexistence. As recently proposed, the SMSE framework supports cognition-based, software defined radio (SDR) applications and is well-suited for coexistence analysis. For initial proof-of-concept, two SMSE waveform parameters (number of carriers and carrier bandwidth) are optimized in a coexistent scenario to characterize SMSE impact on Direct Sequence Spread Spectrum (DSSS) bit error performance. Given optimization via GA techniques have been successfully applied in many engineering fields, as well as operations research, they are viable candidates for robust SMSE waveform design. As demonstrated, the analytic SMSE framework is well-suited for parametric optimization via GA techniques.
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