A generalization of Polyak's convergence result for subgradient optimization
Mathematical Programming: Series A and B
Understanding digital subscriber line technology
Understanding digital subscriber line technology
Convex Optimization
PSD-constrained PAR reduction for DMT/OFDM
EURASIP Journal on Applied Signal Processing
An active-set approach for OFDM PAR reduction via tone reservation
IEEE Transactions on Signal Processing
A variable target value method for nondifferentiable optimization
Operations Research Letters
IEEE Transactions on Signal Processing
Reduction of peak-to-average power ratio in transform domain communication systems
IEEE Transactions on Wireless Communications
A polynomial phasing scheme to realize minimum crest factor for multicarrier transmission
WTS'10 Proceedings of the 9th conference on Wireless telecommunications symposium
Hi-index | 0.00 |
We introduce a novel subgradient optimization-based framework for iterative peak-to-average power ratio (PAR) reduction for multicarrier systems, such as wireless orthogonal frequency division multiplexing (OFDM) and wireline discrete multitone (DMT) very high-speed digital subscriber line (DMT-VDSL) systems. The proposed approach uses reserved or unused tones to minimize the peak magnitude of the DMT symbol vector where these tone values are iteratively updated through a subgradient search. The algorithms obtained through this framework have very simple update rules, and therefore, low computational complexities in general. Since the approach is based on the direct update of some frequency domain parameters, the power spectral density (PSD) level constraints that exist in the communications standards can easily be incorporated into the algorithms. This feature also enables simple compensation for the effects of transmit filter on PAR. Furthermore, we can locate the Active Set PAR reduction method for real baseband signals as a special case of the subgradient approach and provide its natural extension to handle complex baseband DMT signals. In addition to the peak level cost function, we also introduce the K-peak energy cost function which is also used to develop effective subgradient algorithms as illustrated by the simulation examples.