In this paper, we focus on orthogonal time frequency space (OTFS) modulated transmissions over doubly-selective channels. Specifically, we investigate the state-of-the-art techniques for effective detection in high-mobility scenarios and introduce innovative strategies aimed at achieving an optimal trade-off between complexity and performance. The proposed solutions are based on the Ungerboeck observation model and exploit the structure of the resulting channel matrix in the Doppler-delay domain. In particular, the first proposed method, denoted as HBCJRG, belongs to the family of message-passing algorithms, whose strength lies in a scheduling scheme that prioritizes the strongest interferers during the detection process. In contrast, the second proposed approach, denoted as ICG, is based on interference cancellation. Differently from the HBCJRGapproach, whose required computational load grows quadratically with the constellation cardinality, the ICGalgorithm is characterized by extremely low complexity. Simulation results demonstrate that the latter strategy allows to achieve an optimal trade-off between complexity and performance when compared to existing state-of-the-art solutions.

In the context of signal detection in the presence of an unknown time-varying channel parameter, receivers based on the Expectation Propagation (EP) framework appear to be very promising. EP is a message-passing algorithm based on factor graphs with an inherent ability to combine prior knowledge of system variables with channel observations. This suggests that an effective estimation of random channel parameters can be achieved even with a very limited number of pilot symbols, thus increasing the payload efficiency. However, achieving satisfactory performance often requires ad-hoc adjustments in the way the probability distributions of latent variables - both data and channel parameters - are combined and projected. Here, we provide, for the first time, an analysis of EP-based algorithms for the classical problem of coded transmission on a strong Wiener phase noise channel, employing soft-input soft-output decoding. The analysis includes possible improvements over the native application of EP, in order to identify its limitations and propose new strategies which reach the performance benchmark while maintaining low complexity, with a primary focus on challenging scenarios where the state-of-the-art algorithms fail.

We investigate the performance of free-space optical communication systems in the presence of atmospheric turbulence to assess the advantages that a coherent communication system can bring with respect to a conventional intensity modulation and direct detection (IM/DD) system. The perspective is an information-theoretic one, hence we evaluate the mutual information and the corresponding outage probability of both channels, with various traditional symbol constellations, as a pragmatic approximation to the capacity, or to the outage capacity, of those channels. In addition, we analyze non-uniform symbol constellations to evaluate the possible shaping gain that can be achieved under different channel conditions. We propose a method to quantify the gain that the coherent solution can achieve, in terms of signal-to-noise ratio (SNR), so that it can be compared, on a techno-economical basis, against the higher cost that it implies.

Expectation Propagation (EP) is a promising framework in message-passing algorithms based on factor-graphs. The inherent ability to combine prior (partial) knowledge of system variables with channel observations suggests that an effective estimation of random channel parameters can be achieved even with a very limited number of pilot symbols, thus increasing the payload efficiency. Yet, the way in which the probability distributions of latent variables (both data and parameters) are combined and projected often requires ad-hoc adjustments to reach satisfactory performance. Here, we apply EP to a classical problem of LDPC-coded transmission on a strong Wiener phase noise channel and discuss how and why, even in the simple case of binary modulation, EP can fail or succeed.

The recent developments in the field of direct-to-Earth (DTE) for low-Earth-orbit (LEO) satellite optical links have shown the potential benefits of on-off-keying-based communications with the variable data rate (VDR) technique, in contrast to the traditional constant data rate (CDR) approach. In this paper, relevant link level aspects are analyzed, namely: time, frame, and amplitude synchronization, showing that reliable and performing techniques allow to fully exploit the advantages offered by the VDR strategy.

Internet traffic detection and classification has been thoroughly studied in the last decade, but this is still a hot topic as regards the Internet of Things (IoT), a communication paradigm that is going to involve different aspects of our daily life. As a consequence, researchers started applying traditional methods for traffic classification also to the traffic flows coming and addressed to smart devices. In this paper, we created a large integrated dataset of IoT traffic flows, coming from four different network scenarios, in order to have a benchmark for future research. Moreover, we used this dataset to test the effectiveness of a deep learning network model, made of different hidden layers, and we compare its outcomes with the ones obtained through traditional machine learning approaches, demonstrating the superiority of our deep learning architecture in both a binary and multinomial classification.

A new maximum-likelihood sequence detection receiver for spectrally efficient linear modulations on bandlimited bandpass nonlinear channels is proposed. The receiver is based on oversampling the received signal corrupted by noise and nonlinear distortion. Contrary to other solutions in the literature, in the proposed technique there is no need for a bank of matched filters, and the receiver front end reduces to a single lowpass filter. For a given peak power level, a performance gain can be achieved over more traditional approaches to transmission on nonlinear channels, such as those based on predistortion, if a moderate spectral expansion is allowed. To analyze the receiver performance, the concept of distance spectrum is employed, since the minimum distance alone cannot account for a reliable performance evaluation. Both analysis and simulation are carried out for realistic narrowband nonlinear channels, possibly employing reduced-state sequence detection. Appreciable gain margins are confirmed to be possible in these realistic cases.

Based on a polynomial representation of a memoryless bandpass nonlinearity, a new realization of an optimal receiver is proposed to perform maximum likelihood detection of data sequences transmitted over nonlinear, possibly time-dispersive, channels. The receiver employs oversampling of the observed signal to compute proper branch metrics for a Viterbi processor. Error performance is compared to that of an optimal receiver for the linear channel obtained by ideal analog predistortion of the nonlinear device under a peak-power constraint. In the presence of nonlinear distortion, a significant improvement in the symbol error rate is shown to be achievable by optimal detection with respect to ideal predistortion. The numerical results are based on both analytic and simulation methods.