976 research outputs found
Amplitude and Sign Adjustment for Peak-to-Average-Power Reduction
In this letter, we propose a method to reduce the peak-to-mean-envelope-power ratio (PMEPR) of multicarrier signals by modifying the constellation. For-ary phase-shift keying constellations, we minimize the maximum of the multicarrier signal over the sign and amplitude of each subcarrier. In order to find an efficient solution to the aforementioned nonconvex optimization problem, we present a suboptimal solution by first optimizing over the signs, and then optimizing over the amplitudes given the signs. We prove that the minimization of the maximum of a continuous multicarrier signal over the amplitude of each subcarrier can be written as a convex optimization problem with linear matrix inequality constraints. We also generalize the idea to other constellations such as 16-quadrature amplitude modulation. Simulation results show that by an average power increase of 0.21 dB, and not sending information over the sign of each subcarrier, PMEPR can be decreased by 5.1 dB for a system with 128 subcarriers
Peak to average power reduction using amplitude and sign adjustment
In this paper, we propose a method to reduce the peak to mean envelope power ratio (PMEPR) of multicarrier signals by modifying the constellation. For MPSK constellations,
we minimize the maximum of the multicarrier signal over the
sign and amplitude of each subcarrier. In order to find an efficient solution to the aforementioned non-convex optimization problem, we present a suboptimal solution by first optimizing over the signs using the result of [1], and then optimizing over the amplitudes given the signs. We prove that the minimization of the maximum of a multicarrier signal over the amplitude of each subcarrier can be written as a convex optimization problem with linear matrix inequality constraints. We also generalize the idea to other
constellations such as 16QAM. Simulation results show that by an average power increase of 0.21 db and not sending information over the sign of each subcarrier, PMEPR can be decreased by 5.1 db for a system with 128 subcarriers
Democratic Representations
Minimization of the (or maximum) norm subject to a constraint
that imposes consistency to an underdetermined system of linear equations finds
use in a large number of practical applications, including vector quantization,
approximate nearest neighbor search, peak-to-average power ratio (or "crest
factor") reduction in communication systems, and peak force minimization in
robotics and control. This paper analyzes the fundamental properties of signal
representations obtained by solving such a convex optimization problem. We
develop bounds on the maximum magnitude of such representations using the
uncertainty principle (UP) introduced by Lyubarskii and Vershynin, and study
the efficacy of -norm-based dynamic range reduction. Our
analysis shows that matrices satisfying the UP, such as randomly subsampled
Fourier or i.i.d. Gaussian matrices, enable the computation of what we call
democratic representations, whose entries all have small and similar magnitude,
as well as low dynamic range. To compute democratic representations at low
computational complexity, we present two new, efficient convex optimization
algorithms. We finally demonstrate the efficacy of democratic representations
for dynamic range reduction in a DVB-T2-based broadcast system.Comment: Submitted to a Journa
EVM and Achievable Data Rate Analysis of Clipped OFDM Signals in Visible Light Communication
Orthogonal frequency division multiplexing (OFDM) has been considered for
visible light communication (VLC) thanks to its ability to boost data rates as
well as its robustness against frequency-selective fading channels. A major
disadvantage of OFDM is the large dynamic range of its time-domain waveforms,
making OFDM vulnerable to nonlinearity of light emitting diodes (LEDs). DC
biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM
(ACO-OFDM) are two popular OFDM techniques developed for the VLC. In this
paper, we will analyze the performance of the DCO-OFDM and ACO-OFDM signals in
terms of error vector magnitude (EVM), signal-to-distortion ratio (SDR), and
achievable data rates under both average optical power and dynamic optical
power constraints. EVM is a commonly used metric to characterize distortions.
We will describe an approach to numerically calculate the EVM for DCO-OFDM and
ACO-OFDM. We will derive the optimum biasing ratio in the sense of minimizing
EVM for DCO-OFDM. Additionally, we will formulate the EVM minimization problem
as a convex linear optimization problem and obtain an EVM lower bound against
which to compare the DCO-OFDM and ACO-OFDM techniques. We will prove that the
ACO-OFDM can achieve the lower bound. Average optical power and dynamic optical
power are two main constraints in VLC. We will derive the achievable data rates
under these two constraints for both additive white Gaussian noise (AWGN)
channel and frequency-selective channel. We will compare the performance of
DCO-OFDM and ACO-OFDM under different power constraint scenarios
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