Abstract
The dynamic range of a signal is a critical parameter in many practical applications. Especially in communication engineering high dynamic range mostly is considered as an important problem for technical reasons. norm minimization, or in other words an anti-sparse penalty, naturally spreads the signal evenly. The advantage of spreading is the optimally reduced dynamic range of transformed signals which is a pleasant feature for many application, e.g. peak to average power ratio (PAPR) reduction for orthogonal frequency-division multiplexing (OFDM) systems. In this study, some of the main proximal splitting algorithms are deployed for l ∞ -norm minimization. The stochastic model of anti-sparsity is investigated with the empirical results of proximal methods and already existing l ∞ -norm minimization methods. A flexible prior is proposed to model anti-sparsity and it is used for more realistic PAPR performance analysis.
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