Secured Audio Signal Transmission in 5G Compatible mmWave Massive MIMO FBMC System with Implementation of Audio-to-image Transformation Aided Encryption Scheme

In this paper, we have made comprehensive study for the performance evaluation of mmWave massive MIMO FBMC wireless communication system. The 165F2;56 large MIMO antenna configured simulated system under investigation incorporates three modern channel coding (Turbo, LDPC and (3, 2) SPC, higher order digital modulation (256-QAM)) and various signal detection (Q-Less QR, Lattice Reduction(LR) based Zero-forcing(ZF), Lattice Reduction (LR) based ZF-SIC and Complex-valued LLL(CLLL) algorithm implemented ZF-SIC) schemes. An audio to image conversion aided chaos-based physical layer security scheme has also been implemented in such study. On considering transmission of encrypted audio signal in a hostile fading channel, it is noticeable from MATLAB based simulation study that the LDPC Channel encoded system is very much robust and effective in retrieving color image under utilization of Lattice Reduction(LR) based ZF-SIC signal detection and 16- QAM digital modulation techniques

グラフ変換と一般化確率伝播法に基づくMIMO信号検出法に関する研究

　複数の送受信アンテナを用いて並列伝送を行うことで，通信路容量を線形に増大可能な多入力多出力(MIMO : Multi-Input Multi-Output)伝送が注目を集めている．MIMO 伝送では，高い周波数効率を実現できる一方，受信機において多重化された信号から所望の信号を分離・検出する必要がある．　MIMO 復調法として最も優れた誤り率特性を示すのは最尤復調(MLD: Maximum Likelihood Detection)であるが，莫大な演算量を要するため現実的ではない．そこで，変数ノードと観測ノードからなるファクターグラフ上でのメッセージ交換により，効率的に周辺事後確率を計算可能な確率伝播(BP: Belief Propagation)法によるMIMO復調法が提案されている．しかしMIMO通信路行列により定義されるファクターグラフは，全ての変数ノードと関数ノードが互いに結合しているため，多数のショートループが存在し，その復調性能はMLDと比べると著しく劣化する．　そこで本研究ではファクターグラフにショートループが存在する場合にも正確に周辺事後確率を求める手法として，QR分解付き一般化確率伝播(QR-GBP: QR-decomposed Generalized Belief Proppagation)法を提案する．QR-GBP 法ではファクターグラフ上の幾つかのノードの集合から領域グラフ(region graph)と呼ばれるグラフを構成し，このグラフ上でのメッセージ交換によって，正確な周辺事後確率を計算することができる．　QR-GBP 法はMLDに漸近する高い復調性能を示すものの，QR-GBP 法におけるメッセージ伝播アルゴリズムであるparent-to-childアルゴリズムの演算量は，アンテナ数および変調多値数の増加に伴って指数的に増加してしまう．そこで併せて本研究ではQR-GBP法の演算量低減手法として，QR分解付き確率的一般化確率伝播（QR-SGBP: QR-Stochastic GBP）法，SMR (Smart Message Reduction）付きQR-GBP法，エッジ削減QR-GBP(ER-QR-GBP: Edge Reduced QR-GBP)法を提案する．そして演算量を低減しつつMLDに漸近する性能を達成することを示す．電気通信大学201

Récepteur itératif pour les systèmes MIMO-OFDM basé sur le décodage sphérique : convergence, performance et complexité

Recently, iterative processing has been widely considered to achieve near-capacity performance and reliable high data rate transmission, for future wireless communication systems. However, such an iterative processing poses significant challenges for efficient receiver design. In this thesis, iterative receiver combining multiple-input multiple-output (MIMO) detection with channel decoding is investigated for high data rate transmission. The convergence, the performance and the computational complexity of the iterative receiver for MIMO-OFDM system are considered. First, we review the most relevant hard-output and soft-output MIMO detection algorithms based on sphere decoding, K-Best decoding, and interference cancellation. Consequently, a low-complexity K-best (LCK- Best) based decoder is proposed in order to substantially reduce the computational complexity without significant performance degradation. We then analyze the convergence behaviors of combining these detection algorithms with various forward error correction codes, namely LTE turbo decoder and LDPC decoder with the help of Extrinsic Information Transfer (EXIT) charts. Based on this analysis, a new scheduling order of the required inner and outer iterations is suggested. The performance of the proposed receiver is evaluated in various LTE channel environments, and compared with other MIMO detection schemes. Secondly, the computational complexity of the iterative receiver with different channel coding techniques is evaluated and compared for different modulation orders and coding rates. Simulation results show that our proposed approaches achieve near optimal performance but more importantly it can substantially reduce the computational complexity of the system. From a practical point of view, fixed-point representation is usually used in order to reduce the hardware costs in terms of area, power consumption and execution time. Therefore, we present efficient fixed point arithmetic of the proposed iterative receiver based on LC-KBest decoder. Additionally, the impact of the channel estimation on the system performance is studied. The proposed iterative receiver is tested in a real-time environment using the MIMO WARP platform.Pour permettre l’accroissement de débit et de robustesse dans les futurs systèmes de communication sans fil, les processus itératifs sont de plus considérés dans les récepteurs. Cependant, l’adoption d’un traitement itératif pose des défis importants dans la conception du récepteur. Dans cette thèse, un récepteur itératif combinant les techniques de détection multi-antennes avec le décodage de canal est étudié. Trois aspects sont considérés dans un contexte MIMOOFDM: la convergence, la performance et la complexité du récepteur. Dans un premier temps, nous étudions les différents algorithmes de détection MIMO à décision dure et souple basés sur l’égalisation, le décodage sphérique, le décodage K-Best et l’annulation d’interférence. Un décodeur K-best de faible complexité (LC-K-Best) est proposé pour réduire la complexité sans dégradation significative des performances. Nous analysons ensuite la convergence de la combinaison de ces algorithmes de détection avec différentes techniques de codage de canal, notamment le décodeur turbo et le décodeur LDPC en utilisant le diagramme EXIT. En se basant sur cette analyse, un nouvel ordonnancement des itérations internes et externes nécessaires est proposé. Les performances du récepteur ainsi proposé sont évaluées dans différents modèles de canal LTE, et comparées avec différentes techniques de détection MIMO. Ensuite, la complexité des récepteurs itératifs avec différentes techniques de codage de canal est étudiée et comparée pour différents modulations et rendement de code. Les résultats de simulation montrent que les approches proposées offrent un bon compromis entre performance et complexité. D’un point de vue implémentation, la représentation en virgule fixe est généralement utilisée afin de réduire les coûts en termes de surface, de consommation d’énergie et de temps d’exécution. Nous présentons ainsi une représentation en virgule fixe du récepteur itératif proposé basé sur le décodeur LC K-Best. En outre, nous étudions l’impact de l’estimation de canal sur la performance du système. Finalement, le récepteur MIMOOFDM itératif est testé sur la plateforme matérielle WARP, validant le schéma proposé

Physical-Layer Cooperation in Coded OFDM Relaying Systems

Mobile communication systems nowadays require ever-increasing data rate and coverage of wide areas. One promising approach to achieve this goal is the application of cooperative communications enabled by introducing intermediate nodes known as relays to support the transmission between terminals. By processing and forwarding the receive message at the relays, the path-loss effect between the source and the destination is mitigated. One major limit factor for relay assisted communications is that a relay cannot transmit and receive using the same physical resources. Therefore, a half-duplex constraint is commonly assumed resulting in halved spectral efficiency. To combat this drawback, two-way relaying is introduced, where two sources exchange information with each. On the other hand, due to the physical limitation of the relays, e.g., wireless sensor nodes, it's not possible to implement multiple antennas at one relay, which prohibits the application of multiple-input multiple-output (MIMO) techniques. However, when treating multiple relays as a cluster, a virtual antenna array is formed to perform MIMO techniques in a distributed manner. %This thesis aims at designing efficient one-way and two-way relaying schemes. Specifically, existing schemes from the literature are improved and new schemes are developed with the emphasis on coded orthogonal frequency division multiplexing (OFDM) transmissions. Of special interest is the application of physical-layer network coding (PLNC) for two-phase two-way relaying. In this case, a network coded message is estimated from the superimposed receive signal at the relay using PLNC schemes. The schemes are investigated based on a mutual information analysis and their performance are improved by a newly proposed phase control strategy. Furthermore, performance degradation due to system asynchrony is mitigated depending on different PLNC schemes. When multiple relays are available, novel cooperation schemes allowing information exchange within the relay cluster are proposed that facilitate distributed MIMO reception and transmission. Additionally, smart signaling approaches are presented to enable the cooperation at different levels with the cooperation overhead taken into account adequately in system performance evaluation

Mehrdimensionale Kanalschätzung für MIMO-OFDM

DIGITAL wireless communication started in the 1990s with the wide-spread deployment of GSM. Since then, wireless systems evolved dramatically. Current wireless standards approach the goal of an omnipresent communication system, which fulfils the wish to communicate with anyone, anywhere at anytime. Nowadays, the acceptance of smartphones and/or tablets is huge and the mobile internet is the core application. Given the current growth, the estimated data traffic in wireless networks in 2020 might be 1000 times higher than that of 2010, exceeding 127 exabyte. Unfortunately, the available radio spectrum is scarce and hence, needs to be utilized efficiently. Key technologies, such as multiple-input multiple-output (MIMO), orthogonal frequency-division multiplexing (OFDM) as well as various MIMO precoding techniques increase the theoretically achievable channel capacity considerably and are used in the majority of wireless standards. On the one hand, MIMO-OFDM promises substantial diversity and/or capacity gains. On the other hand, the complexity of optimum maximum-likelihood detection grows exponentially and is thus, not sustainable. Additionally, the required signaling overhead increases with the number of antennas and thereby reduces the bandwidth efficiency. Iterative receivers which jointly carry out channel estimation and data detection are a potential enabler to reduce the pilot overhead and approach optimum capacity at often reduced complexity. In this thesis, a graph-based receiver is developed, which iteratively performs joint data detection and channel estimation. The proposed multi-dimensional factor graph introduces transfer nodes that exploit correlation of adjacent channel coefficients in an arbitrary number of dimensions (e.g. time, frequency, and space). This establishes a simple and flexible receiver structure that facilitates soft channel estimation and data detection in multi-dimensional dispersive channels, and supports arbitrary modulation and channel coding schemes. However, the factor graph exhibits suboptimal cycles. In order to reach the maximum performance, the message exchange schedule, the process of combining messages, and the initialization are adapted. Unlike conventional approaches, which merge nodes of the factor graph to avoid cycles, the proposed message combining methods mitigate the impairing effects of short cycles and retain a low computational complexity. Furthermore, a novel detection algorithm is presented, which combines tree-based MIMO detection with a Gaussian detector. The resulting detector, termed Gaussian tree search detection, integrates well within the factor graph framework and reduces further the overall complexity of the receiver. Additionally, particle swarm optimization (PSO) is investigated for the purpose of initial channel estimation. The bio-inspired algorithm is particularly interesting because of its fast convergence to a reasonable MSE and its versatile adaptation to a variety of optimization problems. It is especially suited for initialization since no a priori information is required. A cooperative approach to PSO is proposed for large-scale antenna implementations as well as a multi-objective PSO for time-varying frequency-selective channels. The performance of the multi-dimensional graph-based soft iterative receiver is evaluated by means of Monte Carlo simulations. The achieved results are compared to the performance of an iterative state-of-the-art receiver. It is shown that a similar or better performance is achieved at a lower complexity. An appealing feature of iterative semi-blind channel estimation is that the supported pilot spacings may exceed the limits given the by Nyquist-Shannon sampling theorem. In this thesis, a relation between pilot spacing and channel code is formulated. Depending on the chosen channel code and code rate, the maximum spacing approaches the proposed “coded sampling bound”.Die digitale drahtlose Kommunikation begann in den 1990er Jahren mit der zunehmenden Verbreitung von GSM. Seitdem haben sich Mobilfunksysteme drastisch weiterentwickelt. Aktuelle Mobilfunkstandards nähern sich dem Ziel eines omnipräsenten Kommunikationssystems an und erfüllen damit den Wunsch mit jedem Menschen zu jeder Zeit an jedem Ort kommunizieren zu können. Heutzutage ist die Akzeptanz von Smartphones und Tablets immens und das mobile Internet ist die zentrale Anwendung. Ausgehend von dem momentanen Wachstum wird das Datenaufkommen in Mobilfunk-Netzwerken im Jahr 2020, im Vergleich zum Jahr 2010, um den Faktor 1000 gestiegen sein und 100 Exabyte überschreiten. Unglücklicherweise ist die verfügbare Bandbreite beschränkt und muss daher effizient genutzt werden. Schlüsseltechnologien, wie z.B. Mehrantennensysteme (multiple-input multiple-output, MIMO), orthogonale Frequenzmultiplexverfahren (orthogonal frequency-division multiplexing, OFDM) sowie weitere MIMO Codierverfahren, vergrößern die theoretisch erreichbare Kanalkapazität und kommen bereits in der Mehrheit der Mobil-funkstandards zum Einsatz. Auf der einen Seite verspricht MIMO-OFDM erhebliche Diversitäts- und/oder Kapazitätsgewinne. Auf der anderen Seite steigt die Komplexität der optimalen Maximum-Likelihood Detektion exponientiell und ist infolgedessen nicht haltbar. Zusätzlich wächst der benötigte Mehraufwand für die Kanalschätzung mit der Anzahl der verwendeten Antennen und reduziert dadurch die Bandbreiteneffizienz. Iterative Empfänger, die Datendetektion und Kanalschätzung im Verbund ausführen, sind potentielle Wegbereiter um den Mehraufwand des Trainings zu reduzieren und sich gleichzeitig der maximalen Kapazität mit geringerem Aufwand anzunähern. Im Rahmen dieser Arbeit wird ein graphenbasierter Empfänger für iterative Datendetektion und Kanalschätzung entwickelt. Der vorgeschlagene multidimensionale Faktor Graph führt sogenannte Transferknoten ein, die die Korrelation benachbarter Kanalkoeffizienten in beliebigen Dimensionen, z.B. Zeit, Frequenz und Raum, ausnutzen. Hierdurch wird eine einfache und flexible Empfängerstruktur realisiert mit deren Hilfe weiche Kanalschätzung und Datendetektion in mehrdimensionalen, dispersiven Kanälen mit beliebiger Modulation und Codierung durchgeführt werden kann. Allerdings weist der Faktorgraph suboptimale Schleifen auf. Um die maximale Performance zu erreichen, wurde neben dem Ablauf des Nachrichtenaustausches und des Vorgangs zur Kombination von Nachrichten auch die Initialisierung speziell angepasst. Im Gegensatz zu herkömmlichen Methoden, bei denen mehrere Knoten zur Vermeidung von Schleifen zusammengefasst werden, verringern die vorgeschlagenen Methoden die leistungsmindernde Effekte von Schleifen, erhalten aber zugleich die geringe Komplexität des Empfängers. Zusätzlich wird ein neuartiger Detektionsalgorithmus vorgestellt, der baumbasierte Detektionsalgorithmen mit dem sogenannten Gauss-Detektor verknüpft. Der resultierende baumbasierte Gauss-Detektor (Gaussian tree search detector) lässt sich ideal in das graphenbasierte Framework einbinden und verringert weiter die Gesamtkomplexität des Empfängers. Zusätzlich wird Particle Swarm Optimization (PSO) zum Zweck der initialen Kanalschätzung untersucht. Der biologisch inspirierte Algorithmus ist insbesonders wegen seiner schnellen Konvergenz zu einem akzeptablen MSE und seiner vielseitigen Abstimmungsmöglichkeiten auf eine Vielzahl von Optimierungsproblemen interessant. Da PSO keine a priori Informationen benötigt, ist er speziell für die Initialisierung geeignet. Sowohl ein kooperativer Ansatz für PSO für Antennensysteme mit extrem vielen Antennen als auch ein multi-objective PSO für Kanäle, die in Zeit und Frequenz dispersiv sind, werden evaluiert. Die Leistungsfähigkeit des multidimensionalen graphenbasierten iterativen Empfängers wird mit Hilfe von Monte Carlo Simulationen untersucht. Die Simulationsergebnisse werden mit denen eines dem Stand der Technik entsprechenden Empfängers verglichen. Es wird gezeigt, dass ähnliche oder bessere Ergebnisse mit geringerem Aufwand erreicht werden. Eine weitere ansprechende Eigenschaft von iterativen semi-blinden Kanalschätzern ist, dass der mögliche Abstand von Trainingssymbolen die Grenzen des Nyquist-Shannon Abtasttheorem überschreiten kann. Im Rahmen dieser Arbeit wird eine Beziehung zwischen dem Trainingsabstand und dem Kanalcode formuliert. In Abhängigkeit des gewählten Kanalcodes und der Coderate folgt der maximale Trainingsabstand der vorgeschlagenen “coded sampling bound”

Signal Processing for Compressed Sensing Multiuser Detection

The era of human based communication was longly believed to be the main driver for the development of communication systems. Already nowadays we observe that other types of communication impact the discussions of how future communication system will look like. One emerging technology in this direction is machine to machine (M2M) communication. M2M addresses the communication between autonomous entities without human interaction in mind. A very challenging aspect is the fact that M2M strongly differ from what communication system were designed for. Compared to human based communication, M2M is often characterized by small and sporadic uplink transmissions with limited data-rate constraints. While current communication systems can cope with several 100 transmissions, M2M envisions a massive number of devices that simultaneously communicate to a central base-station. Therefore, future communication systems need to be equipped with novel technologies facilitating the aggregation of massive M2M. The key design challenge lies in the efficient design of medium access technologies that allows for efficient communication with small data packets. Further, novel physical layer aspects have to be considered in order to reliable detect the massive uplink communication. Within this thesis physical layer concepts are introduced for a novel medium access technology tailored to the demands of sporadic M2M. This concept combines advances from the field of sporadic signal processing and communications. The main idea is to exploit the sporadic structure of the M2M traffic to design physical layer algorithms utilizing this side information. This concept considers that the base-station has to jointly detect the activity and the data of the M2M nodes. The whole framework of joint activity and data detection in sporadic M2M is known as Compressed Sensing Multiuser Detection (CS-MUD). This thesis introduces new physical layer concepts for CS-MUD. One important aspect is the question of how the activity detection impacts the data detection. It is shown that activity errors have a fundamentally different impact on the underlying communication system than data errors have. To address this impact, this thesis introduces new algorithms that aim at controlling or even avoiding the activity errors in a system. It is shown that a separate activity and data detection is a possible approach to control activity errors in M2M. This becomes possible by considering the activity detection task in a Bayesian framework based on soft activity information. This concept allows maintaining a constant and predictable activity error rate in a system. Beyond separate activity and data detection, the joint activity and data detection problem is addressed. Here a novel detector based on message passing is introduced. The main driver for this concept is the extrinsic information exchange between different entities being part of a graphical representation of the whole estimation problem. It can be shown that this detector is superior to state-of-the-art concepts for CS-MUD. Besides analyzing the concepts introduced simulatively, this thesis also shows an implementation of CS-MUD on a hardware demonstrator platform using the algorithms developed within this thesis. This implementation validates that the advantages of CS-MUD via over-the-air transmissions and measurements under practical constraints

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