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

A Linear Subspace Approach to Burst Communication Signal Processing

This dissertation focuses on the topic of burst signal communications in a high interference environment. It derives new signal processing algorithms from a mathematical linear subspace approach instead of the common stationary or cyclostationary approach. The research developed new algorithms that have well-known optimality criteria associated with them. The investigation demonstrated a unique class of multisensor filters having a lower mean square error than all other known filters, a maximum likelihood time difference of arrival estimator that outperformed previously optimal estimators, and a signal presence detector having a selectivity unparalleled in burst interference environments. It was further shown that these improvements resulted in a greater ability to communicate, to locate electronic transmitters, and to mitigate the effects of a growing interference environment

Trellis coding with Continuous Phase Modulation (CPM) for satellite-based land-mobile communications

This volume of the final report summarizes the results of our studies on the satellite-based mobile communications project. It includes: a detailed analysis, design, and simulations of trellis coded, full/partial response CPM signals with/without interleaving over various Rician fading channels; analysis and simulation of computational cutoff rates for coherent, noncoherent, and differential detection of CPM signals; optimization of the complete transmission system; analysis and simulation of power spectrum of the CPM signals; design and development of a class of Doppler frequency shift estimators; design and development of a symbol timing recovery circuit; and breadboard implementation of the transmission system. Studies prove the suitability of the CPM system for mobile communications

Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last 5 Years

Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010–2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Classification of linear and nonlinear modulations using Bayesian methods

La reconnaissance de modulations numériques consiste à identifier, au niveau du récepteur d'une chaîne de transmission, l'alphabet auquel appartiennent les symboles du message transmis. Cette reconnaissance est nécessaire dans de nombreux scénarios de communication, afin, par exemple, de sécuriser les transmissions pour détecter d'éventuels utilisateurs non autorisés ou bien encore de déterminer quel terminal brouille les autres. Le signal observé en réception est généralement affecté d'un certain nombre d'imperfections, dues à une synchronisation imparfaite de l'émetteur et du récepteur, une démodulation imparfaite, une égalisation imparfaite du canal de transmission. Nous proposons plusieurs méthodes de classification qui permettent d'annuler les effets liés aux imperfections de la chaîne de transmission. Les symboles reçus sont alors corrigés puis comparés à ceux du dictionnaire des symboles transmis. Plus précisément, nous étudions trois techniques permettant d'estimer la loi a posteriori d'une modulation au niveau du récepteur. La première technique estime les paramètres inconnus associés aux diverses imperfections affectant le récepteur à l'aide d'une approche Bayésienne couplée avec une méthode de simulation MCMC (Markov Chain Monte Carlo). Une deuxième technique utilise l'algorithme de Baum Welch qui permet d'estimer de manière récursive la loi a posteriori du signal reçu et de déterminer la modulation la plus probable parmi un catalogue donné. La dernière méthode étudiée dans cette thèse consiste à corriger les erreurs de synchronisation de phase et de fréquence avec une boucle de phase. Les algorithmes considérés dans cette thèse ont permis de reconnaître un certain nombre de modulations linéaires de types QAM (Quadrature Amplitude Modulation) et PSK (Phase Shift Keying) mais aussi des modulations non linéaires de type GMSK (Gaussian Minimum Shift Keying). ABSTRACT : This thesis studies classification of digital linear and nonlinear modulations using Bayesian methods. Modulation recognition consists of identifying, at the receiver, the type of modulation signals used by the transmitter. It is important in many communication scenarios, for example, to secure transmissions by detecting unauthorized users, or to determine which transmitter interferes the others. The received signal is generally affected by a number of impairments. We propose several classification methods that can mitigate the effects related to imperfections in transmission channels. More specifically, we study three techniques to estimate the posterior probabilities of the received signals conditionally to each modulation. The first technique estimates the unknown parameters associated with various imperfections using a Bayesian approach coupled with Markov Chain Monte Carlo (MCMC) methods. A second technique uses the Baum Welch (BW) algorithm to estimate recursively the posterior probabilities and determine the most likely modulation type from a catalogue. The last method studied in this thesis corrects synchronization errors (phase and frequency offsets) with a phase-locked loop (PLL). The classification algorithms considered in this thesis can recognize a number of linear modulations such as Quadrature Amplitude Modulation (QAM), Phase Shift Keying (PSK), and nonlinear modulations such as Gaussian Minimum Shift Keying (GMSK