Synchronization Techniques for Burst-Mode Continuous Phase Modulation

Synchronization is a critical operation in digital communication systems, which establishes and maintains an operational link between transmitter and the receiver. As the advancement of digital modulation and coding schemes continues, the synchronization task becomes more and more challenging since the new standards require high-throughput functionality at low signal-to-noise ratios (SNRs). In this work, we address feedforward synchronization of continuous phase modulations (CPMs) using data-aided (DA) methods, which are best suited for burst-mode communications. In our transmission model, a known training sequence is appended to the beginning of each burst, which is then affected by additive white Gaussian noise (AWGN), and unknown frequency, phase, and timing offsets. Based on our transmission model, we derive the Cramer-Rao bound (CRB) for DA joint estimation of synchronization parameters. Using the CRB expressions, the optimum training sequence for CPM signals is proposed. It is shown that the proposed sequence minimizes the CRB for all three synchronization parameters asymptotically, and can be applied to the entire CPM family. We take advantage of the simple structure of the optimized training sequence in order to design a practical synchronization algorithm based on the maximum likelihood (ML) principles. The proposed DA algorithm jointly estimates frequency offset, carrier phase and symbol timing in a feedforward manner. The frequency offset estimate is first found by means of maximizing a one dimensional function. It is then followed by symbol timing and carrier phase estimation, which are carried out using simple closed-form expressions. We show that the proposed algorithm attains the theoretical CRBs for all synchronization parameters for moderate training sequence lengths and all SNR regions. Moreover, a frame synchronization algorithm is developed, which detects the training sequence boundaries in burst-mode CPM signals. The proposed training sequence and synchronization algorithm are extended to shaped-offset quadrature phase-shift keying (SOQPSK) modulation, which is considered for next generation aeronautical telemetry systems. Here, it is shown that the optimized training sequence outperforms the one that is defined in the draft telemetry standard as long as estimation error variances are considered. The overall bit error rate (BER) plots suggest that the optimized preamble with a shorter length can be utilized such that the performance loss is less than 0.5 dB of an ideal synchronization scenario

Implementação de códigos LDPC em OFDM e SC-FDE

Os desenvolvimentos dos sistemas de comunicação sem fios apontam para transmissões de alta velocidade e alta qualidade de serviço com um uso eficiente de energia. Eficiência espectral pode ser obtida por modulações multinível, enquanto que melhorias na eficiência de potência podem ser proporcionadas pelo uso de códigos corretores de erros. Os códigos Low-Density Parity-Check (LDPC), devido ao seu desempenho próximo do limite de Shannon e baixa complexidade na implementação e descodificação são apropriados para futuros sistemas de comunicações sem fios. Por outro lado, o uso de modulações multinível acarreta limitações na amplificação. Contudo, uma amplificação eficiente pode ser assegurada por estruturas de transmissão onde as modulações multinível são decompostas em submodulações com envolvente constante que podem ser amplificadas por amplificadores não lineares a operar na zona de saturação. Neste tipo de estruturas surgem desvios de fase e ganho, produzindo distorções na constelação resultante da soma de todos os sinais amplificados. O trabalho foca-se no uso dos códigos LDPC em esquemas multiportadora e monoportadora, com especial ênfase na performance de uma equalização iterativa implementada no domínio da frequência por um Iterative Block-Decision Feedback Equalizer (IB-DFE). São analisados aspectos como o impacto do número de iterações no processo de descodificação dentro das iterações do processo de equalização. Os códigos LDPC também serão utilizados para compensar os desvios de fase em recetores iterativos para sistemas baseados em transmissores com vários ramos de amplificação. É feito um estudo sobre o modo como estes códigos podem aumentar a tolerância a erros de fase que incluí uma análise da complexidade e um algoritmo para estimação dos desequilíbrios de fase

Massive MIMO transmission techniques

Next generation of mobile communication systems must support astounding data traffic increases, higher data rates and lower latency, among other requirements. These requirements should be met while assuring energy efficiency for mobile devices and base stations. Several technologies are being proposed for 5G, but a consensus begins to emerge. Most likely, the future core 5G technologies will include massive MIMO (Multiple Input Multiple Output) and beamforming schemes operating in the millimeter wave spectrum. As soon as the millimeter wave propagation difficulties are overcome, the full potential of massive MIMO structures can be tapped. The present work proposes a new transmission system with bi-dimensional antenna arrays working at millimeter wave frequencies, where the multiple antenna configurations can be used to obtain very high gain and directive transmission in point to point communications. A combination of beamforming with a constellation shaping scheme is proposed, that enables good user isolation and protection against eavesdropping, while simultaneously assuring power efficient amplification of multi-level constellations

Modem design for digital satellite communications

The thesis is concerned with the design of a phase-shift keying system for a digital modem, operating over a satellite link. Computer simulation tests and theoretical analyses are used to assess the proposed design. The optimum design of both transmitter and receiver filters for the system to be used in the modem are discussed. Sinusoidal roll-off spectrum with different roll-off factor and optimum truncation lengths of the sample impulse response are designed for the proposed scheme to approximate to the theoretical ideal. It has used an EF bandpass filter to band limit the modulated signal, which forms part of the satellite channel modelling. The high power amplifier (HPA) at the earth station has been used in the satellite channel modelling due to its effect in introducing nonlinear AMAM and AM-PM conversion effects and distortion on the transmitted signal from the earth station. The satellite transponder is assumed to be operating in a linear mode. Different phase-shift keying signals such as differentially encoded quaternary phase-shift keying (DEQPSK), offset quaternary phase-shift keying (OQPSK) and convolutionally encoded 8PSK (CE8PSK) signals are analysed and discussed in the thesis, when the high power amplifier (HPA) at the earth station is operating in a nonlinear mode. Convolutional encoding is discussed when applied to the system used in the modem, and a Viterbi -algorithm decoder at the receiver has been used, for CE8PSK signals for a nonlinear satellite channel. A method of feed-forward synchronisation scheme is designed for carrier recovery in CE8PSK receiver. The thesis describes a method of baseband linearizing the baseband signal in order to reduce the nonlinear effects caused by the HPA at the earth station. The scheme which compensates for the nonlinear effects of the HPA by predistorting the baseband signal prior to modulation as opposed to correcting the distortion after modulation, thus reducing the effects of nonlinear distortion introduced by the HPA. The results of the improvement are presented. The advanced technology of digital signal processors (DSPs) has been used in the implementation of the demodulation and digital filtering parts of the modem replacing large parts of conventional circuits. The Viterbi-algorithm decoder for CE8PSK signals has been implemented using a digital signal processor chip, giving excellent performance and is a cost effective and easy way for future developments and any modifications, The results showed that, by using the various studied techniques, as well as the implementation of digital signal processor chip in parts of the modem, a potentially more cost effective modem can be obtained

Joint 1D and 2D Neural Networks for Automatic Modulation Recognition

The digital communication and radar community has recently manifested more interest in using data-driven approaches for tasks such as modulation recognition, channel estimation and distortion correction. In this research we seek to apply an object detector for parameter estimation to perform waveform separation in the time and frequency domain prior to classification. This enables the full automation of detecting and classifying simultaneously occurring waveforms. We leverage a lD ResNet implemented by O\u27Shea et al. in [1] and the YOLO v3 object detector designed by Redmon et al. in [2]. We conducted an in depth study of the performance of these architectures and integrated the models to perform joint detection and classification. To our knowledge, the present research is the first to study and successfully combine a lD ResNet classifier and Yolo v3 object detector to fully automate the process of AMR for parameter estimation, pulse extraction and waveform classification for non-cooperative scenarios. The overall performance of the joint detector/ classifier is 90 at 10 dB signal to noise ratio for 24 digital and analog modulations

Design of a Hybrid RF Fingerprint Extraction and Device Classification Scheme

Radio frequency (RF) fingerprint is the inherent hardware characteristics and has been employed to classify and identify wireless devices in many Internet of Things (IoT) applications. This paper extracts novel RF fingerprint features, designs a hybrid and adaptive classification scheme adjusting to the environment conditions, and carries out extensive experiments to evaluate the performance. In particular, four modulation features, namely differential constellation trace figure (DCTF), carrier frequency offset, modulation offset and I/Q offset extracted from constellation trace figure (CTF), are employed. The feature weights under different channel conditions are calculated at the training stage. These features are combined smartly with the weights selected according to the estimated signal to noise ratio (SNR) at the classification stage. We construct a testbed using universal software radio peripheral (USRP) platform as the receiver and 54 ZigBee nodes as the candidate devices to be classified, which are the most ZigBee devices ever tested. Extensive experiments are carried out to evaluate the classification performance under different channel conditions, namely line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. We then validate the robustness by carrying out the classification process 18 months after the training, which is the longest time gap. We also use a different receiver platform for classification for the first time. The classification error rate is as low as 0.048 in LOS scenario, and 0.1105 even when a different receiver is used for classification 18 months after the training. Our hybrid classification scheme has thus been demonstrated effective in classifying a large amount of ZigBee devices

A robust modulation classification method using convolutional neural networks

Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized

MIMO signal processing in offset-QAM based filter bank multicarrier systems

Next-generation communication systems have to comply with very strict requirements for increased flexibility in heterogeneous environments, high spectral efficiency, and agility of carrier aggregation. This fact motivates research in advanced multicarrier modulation (MCM) schemes, such as filter bank-based multicarrier (FBMC) modulation. This paper focuses on the offset quadrature amplitude modulation (OQAM)-based FBMC variant, known as FBMC/OQAM, which presents outstanding spectral efficiency and confinement in a number of channels and applications. Its special nature, however, generates a number of new signal processing challenges that are not present in other MCM schemes, notably, in orthogonal-frequency-division multiplexing (OFDM). In multiple-input multiple-output (MIMO) architectures, which are expected to play a primary role in future communication systems, these challenges are intensified, creating new interesting research problems and calling for new ideas and methods that are adapted to the particularities of the MIMO-FBMC/OQAM system. The goal of this paper is to focus on these signal processing problems and provide a concise yet comprehensive overview of the recent advances in this area. Open problems and associated directions for future research are also discussed.Peer ReviewedPostprint (author's final draft