From Event: SPIE Commercial + Scientific Sensing and Imaging, 2017
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.
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 (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 12, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2262311.
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