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

Coherent and Non-coherent Techniques for Cooperative Communications

Future wireless network may consist of a cluster of low-complexity battery-powered nodes or mobile stations (MS). Information is propagated from one location in the network to another by cooperation and relaying. Due to the channel fading or node failure, one or more relaying links could become unreliable during multiple-hop relaying. Inspired by conventional multiple-input multiple-output (MIMO) techniques exploiting multiple co-located transmit antennas to introduce temporal and spatial diversity, the error performance and robustness against channel fading of a multiple-hop cooperative network could be significantly improved by creating a virtual antenna array (VAA) with various distributed MIMO techniques. In this thesis, we concentrate on the low-complexity distributed MIMO designed for both coherent and non-coherent diversity signal reception at the destination node. Further improvement on the network throughput as well as spectral efficiency could be achieved by extending the concept of unidirectional relaying to bidirectional cooperative communication. Physical-layer network coding (PLNC) assisted distributed space-time block coding (STBC) scheme as well as non-coherent PLNC aided distributed differential STBC system are proposed. It is confirmed by the theoretical analysis that both approaches have the potential for offering full spatial diversity gain. 　　 Furthermore, differential encoding and non-coherent detection techniques are generally associated with performance degradation due to the doubled noise variance. More importantly, conventional differential schemes suffer from the incapability of recovering the source information in time-varying channels owing to the assumption of static channel model used in the derivation of non-coherent detection algorithm. Several low-complexity solutions are proposed and studied in this thesis, which are able to compensate the performance loss caused by non-coherent detection and guarantee the reliable recovery of information in applications with high mobility. A substantial amount of iteration gain is achieved by combining the differential encoding with error-correction code and sufficient interleaving, which allows iterative possessing at the receiver

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”

Multiple-Input Multiple-Output Detection Algorithms for Generalized Frequency Division Multiplexing

Since its invention, cellular communication has dramatically transformed personal lifes and the evolution of mobile networks is still ongoing. Evergrowing demand for higher data rates has driven development of 3G and 4G systems, but foreseen 5G requirements also address diverse characteristics such as low latency or massive connectivity. It is speculated that the 4G plain cyclic prefix (CP)-orthogonal frequency division multiplexing (OFDM) cannot sufficiently fulfill all requirements and hence alternative waveforms have been in-vestigated, where generalized frequency division multiplexing (GFDM) is one popular option. An important aspect for any modern wireless communication system is the application of multi-antenna, i.e. MIMO techiques, as MIMO can deliver gains in terms of capacity, reliability and connectivity. Due to its channel-independent orthogonality, CP-OFDM straightforwardly supports broadband MIMO techniques, as the resulting inter-antenna interference (IAI) can readily be resolved. In this regard, CP-OFDM is unique among multicarrier waveforms. Other waveforms suffer from additional inter-carrier interference (ICI), inter-symbol interference (ISI) or both. This possibly 3-dimensional interference renders an optimal MIMO detection much more complex. In this thesis, weinvestigate how GFDM can support an efficient multiple-input multiple-output (MIMO) operation given its 3-dimensional interference structure. To this end, we first connect the mathematical theory of time-frequency analysis (TFA) with multicarrier waveforms in general, leading to theoretical insights into GFDM. Second, we show that the detection problem can be seen as a detection problem on a large, banded linear model under Gaussian noise. Basing on this observation, we propose methods for applying both space-time code (STC) and spatial multiplexing techniques to GFDM. Subsequently, we propose methods to decode the transmitted signals and numerically and theoretically analyze their performance in terms of complexiy and achieved frame error rate (FER). After showing that GFDM modulation and linear demodulation is a direct application of Gabor expansion and transform, we apply results from TFA to explain singularities of the modulation matrix and derive low-complexity expressions for receiver filters. We derive two linear detection algorithms for STC encoded GFDM signals and we show that their performance is equal to OFDM. In the case of spatial multiplexing, we derive both non-iterative and iterative detection algorithms which base on successive interference cancellation (SIC) and minimum mean squared error (MMSE)-parallel interference cancellation (PIC) detection, respectively. By analyzing the error propagation of the SIC algorithm, we explain its significantly inferior performance compared to OFDM. Using feedback information from the channel decoder, we can eventually show that near-optimal GFDM detection can outperform an optimal OFDM detector by up to 3dB for high SNR regions. We conclude that GFDM, given the obtained results, is not a general-purpose replacement for CP-OFDM, due to higher complexity and varying performance. Instead, we can propose GFDM for scenarios with strong frequency-selectivity and stringent spectral and FER requirements

Waveforms and channel coding for 5G

Abstract. The fifth generation (5G) communication systems are required to perform significantly better than the existing fourth generation (4G) systems in data rate, capacity, coverage, latency, energy consumption and cost. Hence, 5G needs to achieve considerable enhancements in the areas of bandwidth, spectral, energy, and signaling efficiencies and cost per bit. The new radio access technology (RAT) of 5G physical layer needs to utilize an efficient waveform to meet the demands of 5G. Orthogonal frequency division multiplexing (OFDM) is considered as a baseline for up to 30 GHz. However, a major drawback of OFDM systems is their large peak to average power ratio (PAPR). Here in this thesis, a simple selective-mapping (SLM) technique using scrambling is proposed to reduce the PAPR of OFDM signals. This technique selects symbol sequences with high PAPR and scrambles them until a PAPR sequence below a specific threshold is generated. The computational complexity of the proposed scheme is considerably lower than that of the traditional SLM. Also, performance of the system is investigated through simulations and more than 4.5 dB PAPR reduction is achieved. In addition, performance of single carrier waveforms is analyzed in multiple-input multiple-output (MIMO) systems as an alternative to OFDM. Performance of a single carrier massive MIMO system is presented for both uplink and downlink with single user and multiple user cases and the effect of pre-coding on the PAPR is studied. A variety of channel configurations were investigated such as correlated channels, practical channels and the channels with errors in channel estimate. Furthermore, the candidate coding schemes are investigated for the new RAT in the 5G standard corresponding the activities in the third generation partnership project (3GPP). The schemes are evaluated in terms of block error rate (BLER), bit error rate (BER), computational complexity, and flexibility. These parameters comprise a suitable set to assess the performance of different services and applications. Turbo, low density parity check (LDPC), and polar codes are considered as the candidate schemes. These are investigated in terms of obtaining suitable rates, block lengths by proper design for a fair comparison. The simulations have been carried out in order to obtain BLER / BER performance for various code rates and block lengths, in additive white Gaussian noise (AWGN) channel. Although polar codes perform well at short block lengths, LDPC has a relatively good performance at all the block lengths and code rates. In addition, complexity of the LDPC codes is relatively low. Furthermore, BLER/BER performances of the coding schemes in Rayleigh fading channels are investigated and found that the fading channel performance follows a similar trend as the performance in the AWGN channel

Capacity Approaching Coding Strategies for Machine-to-Machine Communication in IoT Networks

Radio access technologies for mobile communications are characterized by multiple access (MA) strategies. Orthogonal MA techniques were a reasonable choice for achieving good performance with single user detection. With the tremendous growth in the number of mobile users and the new internet of things (IoT) shifting paradigm, it is expected that the monthly mobile data traffic worldwide will exceed 24.3 exabytes by 2019, over 100 billion IoT connections by 2025, and the financial impact of IoT on the global economy varies in the range of 3.9 to 11.1 trillion dollars by 2025. In light of the envisaged exponential growth and new trends, one promising solution to further enhance data rates without increasing the bandwidth is by increasing the spectral efficiency of the channel. Non-orthogonal MA techniques are potential candidates for future wireless communications. The two corner points on the boundary region of the MA channel are known to be achievable by single user decoding followed by successive decoding (SD). Other points can also be achieved using time sharing or rate splitting. On the other hand, machine-to-machine (M2M) communication which is an enabling technology for the IoT, enables massive multipurpose networked devices to exchange information among themselves with minor or no human intervention. This thesis consists of three main parts. In the first part, we propose new practical encoding and joint belief propagation (BP) decoding techniques for 2-user MA erasure channel (MAEC) that achieve any rate pair close to the boundary of the capacity region without using time sharing nor rate splitting. While at the encoders, the corresponding parity check matrices are randomly built from a half-rate LDPC matrix, the joint BP decoder employs the associated Tanner graphs of the parity check matrices to iteratively recover the erasures in the received combined codewords. Specifically, the joint decoder performs two steps in each decoding iteration: 1) simultaneously and independently runs the BP decoding process at each constituent sub-graph to recover some of the common erasures, 2) update the other sub-graph with newly recovered erasures and vice versa. When the number of erasures in the received combined codewords is less than or equal to the number of parity check constraints, the decoder may successfully decode both codewords, otherwise the decoder declares decoding failure. Furthermore, we calculate the probability of decoding failure and the outage capacity. Additionally, we show how the erasure probability evolves with the number of decoding iterations and the maximum tolerable loss. Simulations show that any rate pair close to the capacity boundary is achievable without using time sharing. In the second part, we propose a new cooperative joint network and rateless coding strategy for machine-type communication (MTC) devices in the multicast settings where three or more MTC devices dynamically form a cluster to disseminate messages between themselves. Specifically, in the basic cluster, three MTC devices transmit their respective messages simultaneously to the relay in the first phase. The relay broadcasts back the combined messages to all MTC devices within the basic cluster in the second phase. Given the fact that each MTC device can remove its own message, the received signal in the second phase is reduced to the combined messages coming from the other two MTC devices. Hence, this results in exploiting the interference caused by one message on the other and therefore improving the bandwidth efficiency. Furthermore, each group of three MTC devices in vicinity can form a basic cluster for exchanging messages, and the basic scheme extends to N MTC devices. Furthermore, we propose an efficient algorithm to disseminate messages among a large number of MTC devices. Moreover, we implement the proposed scheme employing practical Raptor codes with the use of two relaying schemes, namely amplify and forward (AF) and de-noise and forward (DNF). We show that with very little processing at the relay using DNF relaying scheme, performance can be further enhanced. We also show that the proposed scheme achieves a near optimal sum rate performance. In the third part, we present a comparative study of joint channel estimation and decoding of factor graph-based codes over flat fading channels and propose a simple channel approximation scheme that performs close to the optimal technique. Specifically, when channel state information (CSI) is not available at the receiver, a simpler approach is to estimate the channel state of a group of received symbols, then use the approximated value of the channel with the received signal to compute the log likelihood ratio. Simulation results show that the proposed scheme exhibits about 0.4 dB loss compared to the optimal solution when perfect CSI is available at the receiver

Non-coherent successive relaying and cooperation: principles, designs, and applications

Cooperative communication is capable of forming a virtual antenna array for each node (user) in a network by allowing the nodes (users) to relay the messages of others to the destination. Such a relay aided network may be viewed as a distributed multiple-input multiple-output (MIMO) system relying on the spatially distributed single antennas of the cooperating mobiles, which avoids the correlation of the antenna elements routinely encountered in conventional MIMO systems and hence attains the maximum achievable diversity gain. Therefore, the family of cooperative communication techniques may be regarded as a potential solution for future wireless networks. However, constrained by the half-duplex transmit/receive mode of most practical transceivers, the cooperative networks may impose a severe 50% throughput loss. As a remedy, successive relaying can be employed, which is capable of mimicking a full-duplex relay and thereby recovering much of the 50% throughput loss. Furthermore, for the sake of bypassing power-hungry and potentially excessive-complexity channel estimation, noncoherent detection techniques may be employed for multiple-antenna aided systems, because estimating all the associated channels may become unrealistic. Explicitly, the mobile-stations acting as relays cannot be realistically expected to estimate the source-to-relay channels. In order to motivate further research on noncoherent successive relaying aided systems, a comprehensive review of its basic concepts, fundamental principles, practical transceiver designs and open challenges is provide