Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Fast fourier transforms: a tutorial review and a state of the art
Signal Processing
Vector Space Projections: A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics
Convex Optimization
A low complexity multicarrier PAR reduction approach based on subgradient optimization
Signal Processing - Special section: Multimodal human-computer interfaces
A Modified Split-Radix FFT With Fewer Arithmetic Operations
IEEE Transactions on Signal Processing
Minimizing the Peak-to-Average Power Ratio of OFDM Signals Using Convex Optimization
IEEE Transactions on Signal Processing
An active-set approach for OFDM PAR reduction via tone reservation
IEEE Transactions on Signal Processing
An overview of peak-to-average power ratio reduction techniques for multicarrier transmission
IEEE Wireless Communications
An adaptive projected subgradient approach to learning in diffusion networks
IEEE Transactions on Signal Processing
A unified view of adaptive variable-metric projection algorithms
EURASIP Journal on Advances in Signal Processing
A polynomial phasing scheme to realize minimum crest factor for multicarrier transmission
WTS'10 Proceedings of the 9th conference on Wireless telecommunications symposium
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One of the main issues of the orthogonal frequency-division multiplexing (OFDM) modulation is the high peak-to-average power ratio (PAPR) of the transmitted signal, which adversely affects the complexity of power amplifiers. In this paper, we consider transmitters that reduce the PAPR by slightly disturbing the symbols in carriers used to transmit information and by sending dummy symbols--i.e., symbols not conveying information--in unused carriers. The optimal choice of the data and dummy symbols is determined by the solution of a convex optimization problem. To reduce the PAPR with low complexity, we apply a modified version of the adaptive projected subgradient method to a sequence of convex cost functions closely related to the original optimization problem. The resulting algorithm achieves near-optimal PAPR in practical scenarios, generalizes existing algorithms based on Polyak's method, and can easily handle multiple constraints.