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Multi-parameter non-Gaussian distributions for a framework of noise modulation: A case study of Generalized Gaussian Distribution
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DOI：10.1016/j.icte.2026.06.004.png)
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Abstract
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This paper proposes a novel multi-dimensional noise modulator that uses multi-parameter non-Gaussian distributions, such as the Generalized Gaussian Distribution (GGD). In this scheme, information bits are embedded in the random noise waveform using low and high values of parameters of the multi-parameter noise distribution. Three types of detectors are proposed to detect information bits: the Maximum-Likelihood (ML) detector, the Bayesian Hypothesis Testing (BHT) detector, and a low-complexity detector. For the GGD noise case with three parameters, the 8-ary noise modulator is investigated in detail. Simulation results demonstrate satisfactory performance in terms of bit error probability (BEP).
Keywords:
Noise modulation
Non-Gaussian
Generalized Gaussian distribution
Bit error probability
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