Optical communication systems represent the backbone of modern communication networks. Since their deployment, different fiber technologies have been used to deal with optical fiber impairments such as dispersion-shifted fibers and dispersion-compensation fibers. In recent years, thanks to the introduction of coherent detection based systems, fiber impairments can be mitigated using digital signal processing (DSP) algorithms. Coherent systems are used in the current 100 Gb/s wavelength-division multiplexing (WDM) standard technology. They allow the increase of spectral efficiency by using multilevel modulation formats, and are combined with DSP techniques to combat linear fiber distortions. In addition to linear impairments, the next generation 400 Gb/s and 1 Tb/s WDM systems are also more affected by the fiber nonlinearity due to the Kerr effect. At high input powers, fiber nonlinear effects become more important and their compensation is required to improve the transmission performance. Several approaches have been proposed to deal with the fiber nonlinearity. In this paper, after a brief description of the Kerr-induced nonlinear effects, a survey on fiber nonlinearity compensation (NLC) techniques is provided. We focus on the well-known NLC techniques and discuss their performance, as well as their implementation and complexity. An extension of the inter-subcarrier nonlinear interference canceler approach is also proposed. A performance evaluation of the well-known NLC techniques and the proposed approach is provided in the context of Nyquist and super-Nyquist superchannel systems.

Spectrum sensing and awareness are challenging requirements in cognitive radio (CR). To adequately adapt to the changing radio environment, it is necessary for the CR to detect the presence and classify the on-the-air signals. The wireless industry has shown great interest in orthogonal frequency division multiplexing (OFDM) technology. Hence, classification of OFDM signals has been intensively researched recently. Generic signals have been mainly considered, and there is a need to investigate OFDM standard signals, and their specific discriminating features for classification. In this paper, realistic and comprehensive mathematical models of the OFDM-based mobile Worldwide Interoperability for Microwave Access (WiMAX) and third-Generation Partnership Project Long Term Evolution (3GPP LTE) signals are developed, and their second-order cyclostationarity is studied. Closed-from expressions for the cyclic autocorrelation function (CAF) and cycle frequencies (CFs) of both signal types are derived, based on which an algorithm is proposed for their classification. The proposed algorithm does not require carrier, waveform, and symbol timing recovery, and is immune to phase, frequency, and timing offsets. The classification performance of the algorithm is investigated versus signal-to-noise ratio (SNR), for diverse observation intervals and channel conditions. In addition, the computational complexity is explored versus the signal type. Simulation results show the efficiency of the algorithm is terms of classification performance, and the complexity study proves the real time applicability of the algorithm.

This paper investigates the estimation and prediction of the sparse time-varying channel in underwater acoustic communication systems. The estimation approach exploits the sparse structure of the delay-Doppler representation of the channel. Various state-of-the-art matching pursuit (MP)-type algorithms for sparse signal reconstruction are compared. Among the considered algorithms, sparsity adaptive MP (SaMP) and its variant adaptive step size SaMP have the advantage of not requiring a priori knowledge of the sparsity level and outperform the other algorithms in terms of mean squared error (MSE). Moreover, due to the fast time-varying nature and the extremely limited bandwidth of the UWA channels, a channel prediction that can provide up-to-date channel state information is necessary for reliable symbol detection. This paper proposes an adaptive channel prediction scheme that extrapolates the channel knowledge estimated from a block of training symbols, and the predicted channel is used to decode consecutive data blocks. The proposed scheme does not require any a priori knowledge of channel dynamic model and noise statistics, and is able to provide future channel estimates based solely on current channel estimates. Furthermore, the proposed scheme operates in the delay-Doppler domain, and thus has a remarkably lower computational complexity when compared with the channel prediction in time domain. To further improve the prediction accuracy, past detected symbols are fed back to assist the proposed predictor with an up-to-date channel estimate. Simulation results of the proposed channel estimation and prediction demonstrate a good tradeoff between the MSE/bit error rate and the computational complexity.

This paper presents a novel recovery algorithm based on sparsity adaptive matching pursuit (SaMP) and a new near-optimal pilot placement scheme, for compressed sensing (CS)-based sparse channel estimation in orthogonal frequency division multiplexing (OFDM) communication systems. Compared with other state-of-the-art recovery algorithms, the proposed algorithm possesses the feature of SaMP of not requiring a priori knowledge of the sparsity level, and moreover, adjusts the step size adaptively to approach the true sparsity level. Furthermore, we focus on the pilot pattern design in sparse channel estimation. Although a brute-force search guarantees the optimal pilot pattern, it is prohibitive to examine all possibilities due to high computational complexity. It is known that by minimizing the mutual coherence of the measurement matrix when the signal is sparse on the unitary discrete Fourier transform (DFT) matrix, the optimal set of pilot locations is a cyclic difference set (CDS). Based on this, we propose an efficient near-optimal pilot placement scheme in cases where CDS does not exist. Simulation results show that the proposed channel estimation algorithm, with the new pilot placement scheme, offers a better tradeoff between the performance-in terms of mean-squared-error (MSE) and bit-error-rate (BER)-and complexity, when compared to other estimation algorithms.