Name
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. l(infinity)-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(infinity)-norm minimization. The stochastic model of anti-sparsity is investigated with the empirical results of proximal methods and already existing l(infinity)-norm minimization methods. A flexible prior is proposed to model anti-sparsity and it is used for more realistic PAPR performance analysis. |
Year | Venue | Keywords |
|---|---|---|
2017 | Signal Processing and Communications Applications Conference | Anti-Sparse Representation,Anti-Sparse Prior,Proximal Gradient Methods,PAPR Distribution |
Field | DocType | ISSN |
Mathematical optimization,Dynamic range,Computer science,Stochastic process,Telecommunications engineering,Minification,Stochastic modelling,Multiplexing,High dynamic range,Orthogonal frequency-division multiplexing | Conference | 2165-0608 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Metin Vural | 1 | 1 | 1.39 |
| Peter Jung | 2 | 154 | 23.80 |
| Slawomir Stanczak | 3 | 521 | 89.71 |