Performance Analysis and Comparison of Clipped and Filtered OFDM Systems With Iterative Distortion Recovery Techniques

Abstract

A well-known drawback of orthogonal frequencydivision multiplexing (OFDM) is its signal with high peakto-average power ratio (PAPR). Among a number of PAPR reduction techniques, clipping and filtering (CAF) is the simplest approach, which effectively reduces the PAPR of band-limited OFDM signals at the cost of increasing in-band distortion. In order to mitigate the performance degradation caused by the inband distortion, several iterative distortion recovery techniques have been proposed in the literature, and they are largely classified into time-domain (TD) and frequency-domain (FD) compensation approaches: The former and latter are represented by the decision-aided reconstruction (DAR) and clipping noise cancellation (CNC), respectively. To date, however, their theoretical performance limits have not been studied. In this work, we revisit the performance limits of CAF and derive a closed-form signal-to-distortion power ratio (SDR) expression. Furthermore, we introduce a time-domain distortion model for characterizing the OFDM signal with CAF, based on which we make performance comparison between the two compensation approaches. Theoretical analysis and simulations in terms of their achievable symbol error rate (SER) reveal that, unlike the FD counterpart, the TD compensation may suffer from unrecoverable distortion when filtering after clipping is applied at the transmitter.

On Spectral Efficiency of OFDM Signals Based on Windowing

“…Therefore, in practice, for a given acceptable level of OOB emission (or spectral mask), a careful balance between windowing and IBO adjustments should be taken into consideration. Also, in order to enhance PA efficiency, the use of PAPR reduction (such as clipping and filtering [12] as well as the clipping noise recovery schemes [21]) should be mandatory, and this in turn increases the total efficiency, provided that the adopted PAPR reduction schemes themselves do not reduce the spectral efficiency due to their additional overhead.…”

“…We summarize known theoretical expressions over nonlinear channels (see [20,21] and the references therein for details). Under the assumption that s(t) is modeled as a complex Gaussian random process with its PSD given by P s (f ), the PSD of ŝ(t) can be expressed as…”

“…(f ) = P s (f ). The coefficient C n in (68) depends on the nonlinear function and in the case of (66), it is given by [21] C n = (n + 1) where…”

Highly Accurate Technique for CO-OFDM Channel Estimation Technique Using Extreme Learning Machine (ELM)

“…The atmospheric turbulence for the vertical FSO link becomes weaker as the altitude of transmissions increases, consequently, the model assumed the lognormally distributed channels for the weak turbulence regime is stated in equation (15) as 𝐼 𝑚𝑛 = 𝑒 𝑍 𝑚𝑛 (15) Where, 𝑍 𝑚𝑛 is a Gaussian random variable with the mean 𝜇 𝑍 𝑚𝑛 = 𝜇 𝑍 and the variance 𝜎 𝑍 𝑚𝑛 2 . Assume that 𝜇 𝑍 𝑚𝑛 = 𝜇 𝑍 and 𝜎 𝑍 𝑚𝑛 2 = 𝜎 𝑍 2 for all 𝑚 ∈ {1, ⋯ , 𝑀} and 𝑛 ∈ {1, ⋯ , 𝑁} for the independent and identically distributed channels.…”

“…Nevertheless, channel estimation has attracted limited attention in the field of CO-OFDM systems in recent years. Many channel estimation methods were reported in the CO-OFDM system, such as least square (LS), maximum likelihood (ML), linear minimum mean square error (LMMSE) algorithms, and deep neural network, [15]. Recently, a neural network, [5] was used as a nonlinear model to approximate the relationship between the channel impulse response and its corresponding subcarrier index.…”