Signal Detection for OFDM-Based Virtual MIMO Systems under Unknown Doubly Selective Channels, Multiple Interferences and Phase Noises

In this paper, the challenging problem of signal detection under severe communication environment that plagued by unknown doubly selective channels (DSCs), multiple narrowband interferences (NBIs) and phase noises (PNs) is investigated for orthogonal frequency division multiplexing based virtual multiple-input multiple-output (OFDM-V-MIMO) systems. Based on the Variational Bayesian Inference framework, a novel iterative algorithm for joint signal detection, DSC, NBI and PN estimations is proposed. Simulation results demonstrate quick convergence of the proposed algorithm, and after convergence, the bit-error-rate performance of the proposed signal detection algorithm is very close to that of the ideal case which assumes perfect channel state information, no PN, and known positions and powers of NBIs plus additive white Gaussian noise. Furthermore, simulation results show that the proposed signal detection algorithm outperforms other state-of-the-art methods.published_or_final_versio

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

A New Method For Increasing the Accuracy of EM-based Channel Estimation

It was recently shown that the detection performance can be significantly improved if the statistics of channel estimation errors are available and properly used at the receiver. Although in pilot-only channel estimation it is usually straightforward to characterize the statistics of channel estimation errors, this is not the case for the class of data-aided (semi-blind) channel estimation techniques. In this paper, we focus on the widely-used data-aided channel estimation techniques based on the expectation-maximization (EM) algorithm. This is achieved by a modified formulation of the EM algorithm which provides the receiver with the statistics of the estimation errors and properly using this additional information. Simulation results show that the proposed data-aided estimator outperform its classical counterparts in terms of accuracy, without requiring additional complexity at the receiver

Neural-Kalman Schemes for Non-Stationary Channel Tracking and Learning

This Thesis focuses on channel tracking in Orthogonal Frequency-Division Multiplexing (OFDM), a widely-used method of data transmission in wireless communications, when abrupt changes occur in the channel. In highly mobile applications, new dynamics appear that might make channel tracking non-stationary, e.g. channels might vary with location, and location rapidly varies with time. Simple examples might be the di erent channel dynamics a train receiver faces when it is close to a station vs. crossing a bridge vs. entering a tunnel, or a car receiver in a route that grows more tra c-dense. Some of these dynamics can be modelled as channel taps dying or being reborn, and so tap birth-death detection is of the essence. In order to improve the quality of communications, we delved into mathematical methods to detect such abrupt changes in the channel, such as the mathematical areas of Sequential Analysis/ Abrupt Change Detection and Random Set Theory (RST), as well as the engineering advances in Neural Network schemes. This knowledge helped us nd a solution to the problem of abrupt change detection by informing and inspiring the creation of low-complexity implementations for real-world channel tracking. In particular, two such novel trackers were created: the Simpli- ed Maximum A Posteriori (SMAP) and the Neural-Network-switched Kalman Filtering (NNKF) schemes. The SMAP is a computationally inexpensive, threshold-based abrupt-change detector. It applies the three following heuristics for tap birth-death detection: a) detect death if the tap gain jumps into approximately zero (memoryless detection); b) detect death if the tap gain has slowly converged into approximately zero (memory detection); c) detect birth if the tap gain is far from zero. The precise parameters for these three simple rules can be approximated with simple theoretical derivations and then ne-tuned through extensive simulations. The status detector for each tap using only these three computationally inexpensive threshold comparisons achieves an error reduction matching that of a close-to-perfect path death/birth detection, as shown in simulations. This estimator was shown to greatly reduce channel tracking error in the target Signal-to-Noise Ratio (SNR) range at a very small computational cost, thus outperforming previously known systems. The underlying RST framework for the SMAP was then extended to combined death/birth and SNR detection when SNR is dynamical and may drift. We analyzed how di erent quasi-ideal SNR detectors a ect the SMAP-enhanced Kalman tracker's performance. Simulations showed SMAP is robust to SNR drift in simulations, although it was also shown to bene t from an accurate SNR detection. The core idea behind the second novel tracker, NNKFs, is similar to the SMAP, but now the tap birth/death detection will be performed via an arti cial neuronal network (NN). Simulations show that the proposed NNKF estimator provides extremely good performance, practically identical to a detector with 100% accuracy. These proposed Neural-Kalman schemes can work as novel trackers for multipath channels, since they are robust to wide variations in the probabilities of tap birth and death. Such robustness suggests a single, low-complexity NNKF could be reusable over di erent tap indices and communication environments. Furthermore, a di erent kind of abrupt change was proposed and analyzed: energy shifts from one channel tap to adjacent taps (partial tap lateral hops). This Thesis also discusses how to model, detect and track such changes, providing a geometric justi cation for this and additional non-stationary dynamics in vehicular situations, such as road scenarios where re ections on trucks and vans are involved, or the visual appearance/disappearance of drone swarms. An extensive literature review of empirically-backed abrupt-change dynamics in channel modelling/measuring campaigns is included. For this generalized framework of abrupt channel changes that includes partial tap lateral hopping, a neural detector for lateral hops with large energy transfers is introduced. Simulation results suggest the proposed NN architecture might be a feasible lateral hop detector, suitable for integration in NNKF schemes. Finally, the newly found understanding of abrupt changes and the interactions between Kalman lters and neural networks is leveraged to analyze the neural consequences of abrupt changes and brie y sketch a novel, abrupt-change-derived stochastic model for neural intelligence, extract some neuro nancial consequences of unstereotyped abrupt dynamics, and propose a new portfolio-building mechanism in nance: Highly Leveraged Abrupt Bets Against Failing Experts (HLABAFEOs). Some communication-engineering-relevant topics, such as a Bayesian stochastic stereotyper for hopping Linear Gauss-Markov (LGM) models, are discussed in the process. The forecasting problem in the presence of expert disagreements is illustrated with a hopping LGM model and a novel structure for a Bayesian stereotyper is introduced that might eventually solve such problems through bio-inspired, neuroscienti cally-backed mechanisms, like dreaming and surprise (biological Neural-Kalman). A generalized framework for abrupt changes and expert disagreements was introduced with the novel concept of Neural-Kalman Phenomena. This Thesis suggests mathematical (Neural-Kalman Problem Category Conjecture), neuro-evolutionary and social reasons why Neural-Kalman Phenomena might exist and found signi cant evidence for their existence in the areas of neuroscience and nance. Apart from providing speci c examples, practical guidelines and historical (out)performance for some HLABAFEO investing portfolios, this multidisciplinary research suggests that a Neural- Kalman architecture for ever granular stereotyping providing a practical solution for continual learning in the presence of unstereotyped abrupt dynamics would be extremely useful in communications and other continual learning tasks.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretaria: Ana García Armada.- Vocal: José Antonio Portilla Figuera

Orthogonal Time Frequency Space (OTFS) Modulation for Wireless Communications

The orthogonal time frequency space (OTFS) modulation is a recently proposed multi-carrier transmission scheme, which innovatively multiplexes the information symbols in the delay-Doppler (DD) domain instead of the conventional time-frequency (TF) domain. The DD domain symbol multiplexing gives rise to a direct interaction between the DD domain information symbols and DD domain channel responses, which are usually quasi-static, compact, separable, and potentially sparse. Therefore, OTFS modulation enjoys appealing advantages over the conventional orthogonal frequency-division multiplexing (OFDM) modulation for wireless communications. In this thesis, we investigate the related subjects of OTFS modulation for wireless communications, specifically focusing on its signal detection, performance analysis, and applications. In specific, we first offer a literature review on the OTFS modulation in Chapter~1. Furthermore, a summary of wireless channels is given in Chapter 2. In particular, we discuss the characteristics of wireless channels in different domains and compare their properties. In Chapter 3, we present a detailed derivation of the OTFS concept based on the theory of Zak transform (ZT) and discrete Zak transform (DZT). We unveil the connections between OTFS modulation and DZT, where the DD domain interpretations of key components for modulation, such as pulse shaping, and matched-filtering, are highlighted. The main research contributions of this thesis appear in Chapter 4 to Chapter 7. In Chapter 4, we introduce the hybrid maximum a posteriori (MAP) and parallel interference cancellation (PIC) detection. This detection approach exploits the power discrepancy among different resolvable paths and can obtain near-optimal error performance with a reduced complexity. In Chapter 5, we propose the cross domain iterative detection for OTFS modulation by leveraging the unitary transformations among different domains. After presenting the key concepts of the cross domain iterative detection, we study its performance via state evolution. We show that the cross domain iterative detection can approach the optimal error performance theoretically. Our numerical results agree with our theoretical analysis and demonstrate a significant performance improvement compared to conventional OTFS detection methods. In Chapter 6, we investigate the error performance for coded OTFS systems based on the pairwise-error probability (PEP) analysis. We show that there exists a fundamental trade-off between the coding gain and the diversity gain for coded OTFS systems. According to this trade-off, we further provide some rule-of-thumb guidelines for code design in OTFS systems. In Chapter 7, we study the potential of OTFS modulation in integrated sensing and communication (ISAC) transmissions. We propose the concept of spatial-spreading to facilitate the ISAC design, which is able to discretize the angular domain, resulting in simple and insightful input-output relationships for both radar sensing and communication. Based on spatial-spreading, we verify the effectiveness of OTFS modulation in ISAC transmissions and demonstrate the performance improvements in comparison to the OFDM counterpart. A summary of this thesis is presented in Chapter 8, where we also discuss some potential research directions on OTFS modulation. The concept of OTFS modulation and the elegant theory of DD domain communication may have opened a new gate for the development of wireless communications, which is worthy to be further explored

Transmission strategies for broadband wireless systems with MMSE turbo equalization

This monograph details efficient transmission strategies for single-carrier wireless broadband communication systems employing iterative (turbo) equalization. In particular, the first part focuses on the design and analysis of low complexity and robust MMSE-based turbo equalizers operating in the frequency domain. Accordingly, several novel receiver schemes are presented which improve the convergence properties and error performance over the existing turbo equalizers. The second part discusses concepts and algorithms that aim to increase the power and spectral efficiency of the communication system by efficiently exploiting the available resources at the transmitter side based upon the channel conditions. The challenging issue encountered in this context is how the transmission rate and power can be optimized, while a specific convergence constraint of the turbo equalizer is guaranteed.Die vorliegende Arbeit beschäftigt sich mit dem Entwurf und der Analyse von effizienten Übertragungs-konzepten für drahtlose, breitbandige Einträger-Kommunikationssysteme mit iterativer (Turbo-) Entzerrung und Kanaldekodierung. Dies beinhaltet einerseits die Entwicklung von empfängerseitigen Frequenzbereichs-entzerrern mit geringer Komplexität basierend auf dem Prinzip der Soft Interference Cancellation Minimum-Mean Squared-Error (SC-MMSE) Filterung und andererseits den Entwurf von senderseitigen Algorithmen, die durch Ausnutzung von Kanalzustandsinformationen die Bandbreiten- und Leistungseffizienz in Ein- und Mehrnutzersystemen mit Mehrfachantennen (sog. Multiple-Input Multiple-Output (MIMO)) verbessern. Im ersten Teil dieser Arbeit wird ein allgemeiner Ansatz für Verfahren zur Turbo-Entzerrung nach dem Prinzip der linearen MMSE-Schätzung, der nichtlinearen MMSE-Schätzung sowie der kombinierten MMSE- und Maximum-a-Posteriori (MAP)-Schätzung vorgestellt. In diesem Zusammenhang werden zwei neue Empfängerkonzepte, die eine Steigerung der Leistungsfähigkeit und Verbesserung der Konvergenz in Bezug auf existierende SC-MMSE Turbo-Entzerrer in verschiedenen Kanalumgebungen erzielen, eingeführt. Der erste Empfänger - PDA SC-MMSE - stellt eine Kombination aus dem Probabilistic-Data-Association (PDA) Ansatz und dem bekannten SC-MMSE Entzerrer dar. Im Gegensatz zum SC-MMSE nutzt der PDA SC-MMSE eine interne Entscheidungsrückführung, so dass zur Unterdrückung von Interferenzen neben den a priori Informationen der Kanaldekodierung auch weiche Entscheidungen der vorherigen Detektions-schritte berücksichtigt werden. Durch die zusätzlich interne Entscheidungsrückführung erzielt der PDA SC-MMSE einen wesentlichen Gewinn an Performance in räumlich unkorrelierten MIMO-Kanälen gegenüber dem SC-MMSE, ohne dabei die Komplexität des Entzerrers wesentlich zu erhöhen. Der zweite Empfänger - hybrid SC-MMSE - bildet eine Verknüpfung von gruppenbasierter SC-MMSE Frequenzbereichsfilterung und MAP-Detektion. Dieser Empfänger besitzt eine skalierbare Berechnungskomplexität und weist eine hohe Robustheit gegenüber räumlichen Korrelationen in MIMO-Kanälen auf. Die numerischen Ergebnisse von Simulationen basierend auf Messungen mit einem Channel-Sounder in Mehrnutzerkanälen mit starken räumlichen Korrelationen zeigen eindrucksvoll die Überlegenheit des hybriden SC-MMSE-Ansatzes gegenüber dem konventionellen SC-MMSE-basiertem Empfänger. Im zweiten Teil wird der Einfluss von System- und Kanalmodellparametern auf die Konvergenzeigenschaften der vorgestellten iterativen Empfänger mit Hilfe sogenannter Korrelationsdiagramme untersucht. Durch semi-analytische Berechnungen der Entzerrer- und Kanaldecoder-Korrelationsfunktionen wird eine einfache Berechnungsvorschrift zur Vorhersage der Bitfehlerwahrscheinlichkeit von SC-MMSE und PDA SC-MMSE Turbo Entzerrern für MIMO-Fadingkanäle entwickelt. Des Weiteren werden zwei Fehlerschranken für die Ausfallwahrscheinlichkeit der Empfänger vorgestellt. Die semi-analytische Methode und die abgeleiteten Fehlerschranken ermöglichen eine aufwandsgeringe Abschätzung sowie Optimierung der Leistungsfähigkeit des iterativen Systems. Im dritten und abschließenden Teil werden Strategien zur Raten- und Leistungszuweisung in Kommunikationssystemen mit konventionellen iterativen SC-MMSE Empfängern untersucht. Zunächst wird das Problem der Maximierung der instantanen Summendatenrate unter der Berücksichtigung der Konvergenz des iterativen Empfängers für einen Zweinutzerkanal mit fester Leistungsallokation betrachtet. Mit Hilfe des Flächentheorems von Extrinsic-Information-Transfer (EXIT)-Funktionen wird eine obere Schranke für die erreichbare Ratenregion hergeleitet. Auf Grundlage dieser Schranke wird ein einfacher Algorithmus entwickelt, der für jeden Nutzer aus einer Menge von vorgegebenen Kanalcodes mit verschiedenen Codierraten denjenigen auswählt, der den instantanen Datendurchsatz des Mehrnutzersystems verbessert. Neben der instantanen Ratenzuweisung wird auch ein ausfallbasierter Ansatz zur Ratenzuweisung entwickelt. Hierbei erfolgt die Auswahl der Kanalcodes für die Nutzer unter Berücksichtigung der Einhaltung einer bestimmten Ausfallwahrscheinlichkeit (outage probability) des iterativen Empfängers. Des Weiteren wird ein neues Entwurfskriterium für irreguläre Faltungscodes hergeleitet, das die Ausfallwahrscheinlichkeit von Turbo SC-MMSE Systemen verringert und somit die Zuverlässigkeit der Datenübertragung erhöht. Eine Reihe von Simulationsergebnissen von Kapazitäts- und Durchsatzberechnungen werden vorgestellt, die die Wirksamkeit der vorgeschlagenen Algorithmen und Optimierungsverfahren in Mehrnutzerkanälen belegen. Abschließend werden außerdem verschiedene Maßnahmen zur Minimierung der Sendeleistung in Einnutzersystemen mit senderseitiger Singular-Value-Decomposition (SVD)-basierter Vorcodierung untersucht. Es wird gezeigt, dass eine Methode, welche die Leistungspegel des Senders hinsichtlich der Bitfehlerrate des iterativen Empfängers optimiert, den konventionellen Verfahren zur Leistungszuweisung überlegen ist