Trellis Pruning for Peak-to-Average Power Ratio Reduction

TL;DR

This paper proposes a trellis pruning technique using nonlinear convolutional coding to significantly reduce PAPR in filtered QPSK and 16-QAM modulations, with minimal impact on channel capacity.

Contribution

It introduces a novel trellis pruning method that controls PAPR by selectively removing edges in the convolutional encoder trellis, balancing PAPR reduction and capacity.

Findings

PAPR is significantly reduced through trellis pruning.

The method maintains near-original channel capacity.

Simulation confirms effectiveness across modulation schemes.

Abstract

This paper introduces a new trellis pruning method which uses nonlinear convolutional coding for peak-to-average power ratio (PAPR) reduction of filtered QPSK and 16-QAM modulations. The Nyquist filter is viewed as a convolutional encoder that controls the analog waveforms of the filter output directly. Pruning some edges of the encoder trellis can effectively reduce the PAPR. The only tradeoff is a slightly lower channel capacity and increased complexity. The paper presents simulation results of the pruning action and the resulting PAPR, and also discusses the decoding algorithm and the capacity of the filtered and pruned QPSK and 16-QAM modulations on the AWGN channel. Simulation results show that the pruning method reduces the PAPR significantly without much damage to capacity.