Adaptive sparse-binary waveform design for all-spectrum channelization

George Sklivanitis; Panos P. Markopoulos; Stella N. Batalama; Dimitris A. Pados

doi:10.1117/12.2262311

5 May 2017 Adaptive sparse-binary waveform design for all-spectrum channelization

George Sklivanitis, Panos P. Markopoulos, Stella N. Batalama, Dimitris A. Pados

Proceedings Volume 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics; 102110B (2017) https://doi.org/10.1117/12.2262311
Event: SPIE Commercial + Scientific Sensing and Imaging, 2017, Anaheim, CA, United States

Abstract

We introduce maximum-SINR, sparse-binary waveforms that modulate data information symbols over the entire continuum of the available/device-accessible spectrum. We present an optimal algorithm that designs the proposed waveforms by maximizing the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum- SINR linear filter at the receiver. In addition, we propose a suboptimal, computationally-efficient algorithm. Simulation studies compare the proposed sparse-binary waveforms with their conventional non-sparse binary counterparts and demonstrate their superior SINR performance. The post-filtering SINR and bit-error rate (BER) improvements attained by the proposed waveforms are also experimentally verified in a software-defined radio testbed operating in multipath laboratory environment, in the presence of colored interference.

Conference Presentation

Citation Download Citation

George Sklivanitis, Panos P. Markopoulos, Stella N. Batalama, and Dimitris A. Pados "Adaptive sparse-binary waveform design for all-spectrum channelization", Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 102110B (5 May 2017); https://doi.org/10.1117/12.2262311

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