Clipping Noise and Power Allocation for OFDM-Based Optical Wireless Communication Using Photon Detection

Jiang, Zhimeng; Gong, Chen; Xu, Zhengyuan

doi:10.1109/lwc.2018.2868105

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…Finally, the output signal is corrupted with a PG noise. For instance, matrix H can model the convolution with a filter impulse response, while φ can represent saturation effects such as clipping [9], [10]. Our model hence reads…”

Section: A Considered Model and Problemmentioning

confidence: 99%

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Global Optimization for Recovery of Clipped Signals Corrupted With Poisson-Gaussian Noise

Marmin

Jezierska

Castella

et al. 2020

IEEE Signal Process. Lett.

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We study a variational formulation for reconstructing nonlinearly distorted signals corrupted with a Poisson-Gaussian noise. In this situation, the data fidelity term consists of a sum of a weighted least squares term and a logarithmic one. Both of them are precomposed by a nonlinearity, modelling a clipping effect, which is assumed to be rational. A regularization term, being a piecewise rational approximation of the 0 function provides a suitable sparsity measure with respect to a preset linear operator. We propose a global optimization approach for such a problem. More specifically, it is first transformed into a generalized moment problem by introducing some auxiliary variables. Then, a hierarchy of semidefinite programming relaxations is built. Numerical examples show the good performance of the proposed approach.

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Section: A Considered Model and Problemmentioning

confidence: 99%

“…2) Nonlinear case: We now choose φ(x) = x δ+|x| with δ = 0.3. Since φ is nonconvex, we linearize it around 0 in (10) in order to apply FB. Figure 1 shows an example of the reconstruction in the nonlinear case where our method performs better than FB.…”

Section: A Sparse Signal Reconstructionmentioning

confidence: 99%

Global Optimization for Recovery of Clipped Signals Corrupted With Poisson-Gaussian Noise

Marmin

Jezierska

Castella

et al. 2020

IEEE Signal Process. Lett.

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“…Tsonev et al model the complete non-linear distortion in an optical wireless communication system in [9] and Mazahir et al calculate the achievable data rate for multi-carrier modulation in bandlimited IM/DD systems in [10]. Furthermore, in [11] the optimal power allocation among the subcarriers in an optical wireless communication OFDM-system is formulated and in [12] a receiver, based on decision aided reconstruction, is proposed to mitigate the clipping noise. All these works utilize the Bussgang theorem [13] to analytically describe the non-linear clipping distortion.…”

Section: Introductionmentioning

confidence: 99%

Spectral Properties of Clipping Noise

2021

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One serious disadvantage of any multicarrier-modulation technique such as orthogonal frequency division multiplexing (OFDM) is its high peak-to-average-power ratio (PAPR) which might lead to signal clipping in several scenarios. To maximize the transmit data rate, it is important to take this non-linear distortion into account. The most common approach is based on the Bussgang theorem, which splits the distortion in a correlated part, represented by a linear damping factor, and uncorrelated additive noise. However, there are two aspects that are not correctly considered by the Bussgang theorem. Firstly, clipping noise shows a frequency-dependent power spectrum which depends on the clipping probability. Secondly, some of the clipping noise power is located outside of the transmission bandwidth, so that it does not influence the transmission quality. In this work, the Bussgang theorem is reviewed in detail and the exact power spectral density of the uncorrelated clipping noise is approximated to determine the signal-to-noise power ratio on every subcarrier separately. Although it is shown that the frequency dependence within the transmission bandwidth is relatively small, at least 36% of the uncorrelated noise power, depending on the clipping level, lays outside of the transmission band. Monte Carlo simulations validate that a simple expression for the power spectral density allows to calculate the symbol error probability of an OFDM transmission system that suffers from clipping. Furthermore, the newly found result can be used to optimize bit allocation tables in bit loading algorithms or to calculate the channel capacity.

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“…The Poisson channel, as a type of standard channel model for optical communications [1], [2], has been widely studied for a long time [3], [4]. Recently, the discrete-time Poisson (DTP) channel has drawn much attention due to the prevalence of intensity modulation in optical wireless communication (OWC) and has been studied in various communication scenarios such as singleinput single-output (SISO) [5], [6], multiple-input multiple-output [7], [8], multicarrier [9], [10] and multiuser scenarios [11], [12]. Although the mathematical model of the Poisson channel is clear and concise, the optimization problem formulated for a given communication system is always challenging and complex.…”

Section: Introductionmentioning

confidence: 99%

Model-Aware End-to-End Learning for SISO Optical Wireless Communication Over Poisson Channel

Si-Ma

Zhu

2020

IEEE Photonics J.

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Faced with the challenge of transceiver design over the Poisson channel, we leverage the deep-learning technique and devise two novel end-to-end learning schemes to fulfill the design task in this paper. One of the schemes accords with the basic principle of the currently available autoencoder (AE) but is specially designed for the Poisson channel with the aid of the square root (SR) transform. The other scheme, following a different design philosophy from AE, is developed based on a double neural network (DNN) model, which regards the receiver and the transmitter as two separate networks. By these designs, the end-to-end learning task can be conducted over Poisson channel. Extensive computer simulations reveal that 1) the transceiver learned by the DNN scheme always performs better than or comparably to the currently available artificially designed transceivers, and 2) compared with the transceiver learned by DNN, the transceiver learned by SR-AE suffers performance loss in some cases, but the SR-AE scheme has a lower complexity to compute the loss function and fewer network parameters. This study takes the first step toward applying end-to-end learning techniques in the field of the Poisson channel and lays a foundation for further works on this topic.

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Clipping Noise and Power Allocation for OFDM-Based Optical Wireless Communication Using Photon Detection

Cited by 15 publications

References 10 publications

Global Optimization for Recovery of Clipped Signals Corrupted With Poisson-Gaussian Noise

Global Optimization for Recovery of Clipped Signals Corrupted With Poisson-Gaussian Noise

Spectral Properties of Clipping Noise

Model-Aware End-to-End Learning for SISO Optical Wireless Communication Over Poisson Channel

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