This dissertation addresses multi-layer optimization aspects of multiple input multiple output (MIMO) and deep learning-based communication systems. The initial focus is on the rate optimization for multi-user MIMO (MU-MIMO) configurations; specifically, multiple access channel (MAC) and interference channel (IC). First, the ergodic sum rates of MIMO MAC and IC configurations are determined by jointly integrating the error and overhead effects due to channel estimation (training) and feedback into the rate optimization. Then, we investigated methods that will increase the achievable rate for parallel Gaussian IC (PGIC) which is a special case of MIMO IC where there is no interference between multiple antenna elements. We derive a generalized iterative waterfilling algorithm for power allocation that maximizes the ergodic achievable rate. We verified the sum rate improvement with our proposed scheme through extensive simulation tests. Next, we introduce a novel physical layer scheme for single user MIMO spatial multiplexing systems based on unsupervised deep learning using an autoencoder. Both transmitter and receiver are designed as feedforward neural networks (FNN) and constellation diagrams are optimized to minimize the symbol error rate (SER) based on the channel characteristics. We first evaluate the SER in the presence of a constant Rayleigh-fading channel as a performance upper bound. Then, we quantize the Gaussian distribution and train the autoencoder with multiple quantized channel matrices. The channel is provided as an input to both the transmitter and the receiver. The performance exceeds that of conventional communication systems both when the autoencoder is trained and tested with single and multiple channels and the performance gain is sustained after accounting for the channel estimation error. Moreover, we evaluate the performance with increasing number of quantization points and when there is a difference between training and test channels. We show that the performance loss is minimal when training is performed with sufficiently large number of quantization points and number of channels. Finally, we develop a distributed and decentralized MU-MIMO link selection and activation protocol that enables MU-MIMO operation in wireless networks. We verified the performance gains with the proposed protocol in terms of average network throughput. / Doctor of Philosophy / Multiple Input Multiple Output (MIMO) wireless systems include multiple antennas both at the transmitter and receiver and they are widely used today in cellular and wireless local area network systems to increase robustness, reliability and data rate. Multi-user MIMO (MU-MIMO) configurations include multiple access channel (MAC) where multiple transmitters communicate simultaneously with a single receiver; interference channel (IC) where multiple transmitters communicate simultaneously with their intended receivers; and broadcast channel (BC) where a single transmitter communicates simultaneously with multiple receivers. Channel state information (CSI) is required at the transmitter to precode the signal and mitigate interference effects. This requires CSI to be estimated at the receiver and transmitted back to the transmitter in a feedback loop. Errors occur during both channel estimation and feedback processes. We initially analyze the achievable rate of MAC and IC configurations when both channel estimation and feedback errors are taken into account in the capacity formulations. We treat the errors associated with channel estimation and feedback as additional noise. Next, we develop methods to maximize the achievable rate for IC by using interference cancellation techniques at the receivers when the interference is very strong. We consider parallel Gaussian IC (PGIC) which is a special case of MIMO IC where there is no interference between multiple antenna elements. We develop a power allocation scheme which maximizes the ergodic achievable rate of the communication systems. We verify the performance improvement with our proposed scheme through simulation tests. Standard optimization techniques are used to determine the fundamental limits of MIMO communications systems. However, there is still a gap between current operational systems and these limits due to complexity of these solutions and limitations in their assumptions. Next, we introduce a novel physical layer scheme for MIMO systems based on machine learning; specifically, unsupervised deep learning using an autoencoder. An autoencoder consists of an encoder and a decoder that compresses and decompresses data, respectively. We designed both the encoder and the decoder as feedforward neural networks (FNNs). In our case, encoder performs transmitter functionalities such as modulation and error correction coding and decoder performs receiver functionalities such as demodulation and decoding as part of the communication system. Channel is included as an additional layer between the encoder and decoder. By incorporating the channel effects in the design process of the autoencoder and jointly optimizing the transmitter and receiver, we demonstrate the performance gains over conventional MIMO communication schemes. Finally, we develop a distributed and decentralized MU-MIMO link selection and activation protocol that enables MU-MIMO operation in wireless networks. We verified the performance gains with the proposed protocol in terms of average network throughput.

To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed. The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows. It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form. A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm.

To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed. The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows. It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form. A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm.

Approved for public release, distribution is unlimited / This thesis investigates the simulation performance of multiple-input multiple-output (MIMO) systems utilizing Alamoutibased space-time block coding (STBC) technique. The MIMO communication systems using STBC technique employing both single- carrier modulation and orthogonal frequency division multiplexing (OFDM) are simulated in Matlab. The physical layer part of the IEEE 802.16a standard is used in constructing the simulated OFDM schemes. Stanford University Interim (SUI) channel models are selected for the wireless channel in the simulation process. The performance results of the simulated MIMO systems are compared to those of conventional single antenna systems. / Lieutenant Junior Grade, Turkish Navy

La demande de transmissions à débits de plus en plus élevés s’accentue davantage avec l’essor de nouveaux services dans les réseaux de communication sans fils. Pour répondre à cette demande, une solution consiste à augmenter la capacité de transmission du canal radiofréquence entre la station de base et le terminal portatif. Ceci peut être réalisé en augmentant le nombre d’éléments rayonnant impliqués à l’émission et à la réception de cette liaison radiofréquence : on parle alors de technique MIMO (Multiple Input, Multiple Output). Cette thèse porte principalement sur la conception, l’optimisation et la caractérisation de systèmes multi-antennaires pour techniques de diversité et MIMO en bandes LTE (Long Term Evolution). Trois prototypes multi-bandes sont proposés dont deux systèmes planaires et un système d’antennes IFAs compactes. De nouvelles solutions multi-bandes et l’influence de la position de l’antenne sur le plan de masse sont étudiées pour réaliser de la diversité spatiale, de polarisation et de diagramme de rayonnement avec une faible corrélation entre les signaux reçus sur chaque antenne mais surtout une bonne efficacité totale. Une ligne de neutralisation est utilisée pour isoler les antennes et un fonctionnement multi-bande est réalisé. L’impédance d’entrée des antennes est étudiée avec la méthode de Youla & Carlin afin d’améliorer la bande passante de la structure compacte de type IFA. Les performances en diversité et en MIMO de ces systèmes sont évaluées dans différents environnements de propagation. Elles montrent que ces systèmes peuvent être utilisés efficacement pour des applications en diversité et MIMO. / The transmission demand for increasing data rate becomes more and more important with the development of new services in radio communication networks. To answer to this demand, one solution consists in increasing the transmission capacity of the radio channel between the base station and the handset terminal. This can be realized by increasing the number of radiating elements involved in the transmission and the reception of this radio link: we talk about MIMO (Multiple Input Multiple Output) technique. The work realized in this thesis concerns mainly design, optimization and characterization of multi-antenna systems for MIMO and diversity techniques in LTE (Long Term Evolution) bands. Three multi bands prototypes are proposed whose two planar systems and one compact IFAs antennas system. News multiband solution and antenna position influence on the PCB were studied to realize spatial, polarization and pattern diversity with low correlation between received signals on each antenna and a good efficiency. The neutralization line was used for antennas isolation and its application in multiband was realized. The antenna load impedance has been studied with Youla & Carlin method in order to improve the frequency bandwidth of the compact IFA structure. Diversity and MIMO performances of these systems were evaluated in different propagation environments. They show that these systems can be effectively used for diversity and MIMO application.

Depuis l'apparition des premiers réseaux radiomobiles cellulaires analogiques au d´ebut des années 70, nous avons assisté à une explosion de la demande en systèmes de communication sans fil. Les services concernés par les télécommunications sans fil se sont depuis étendus à la transmission de données et aux applications multimédia. Devant la nécessité d'avoir des débits élevés tout en garantissant une certaine qualit´e de service, les techniques MIMO (Multiple Input - Multiple Output) apparaissent comme très prometteuses. En utilisant plusieurs antennes simultanément en émission et en réception, ces systèmes exploitent la dimension spatiale pour la transmission de l'information. Ainsi la mise en oeuvre de ces techniques permet d'aboutir à une amélioration substantielle des débits et/ou des performances des liaisons numériques. Comme dans toutes les études systèmes, une analyse approfondie du canal de transmission et des mécanismes de propagation associés s'avère indispensable. Si dans un contexte classique, la caractérisation et la modélisation du canal peuvent se limiter au domaine temporel, il est n´ecessaire pour les systèmes MIMO de considérer la dimension spatiale au même niveau que la dimension temporelle. Une modélisation précise et réaliste du canal dans le domaine spatial est d'autant plus importante dans un contexte MIMO, puisque le gain par rapport à un système classique en terme de débit et/ou de performance est largement tributaire des propriétés spatiales du canal. C'est dans ce contexte que s'inscrit le travail présenté dans ce document. Cette thèse a pour objectif la conception d'un système de mesure performant permettant d'approfondir la connaissance du canal de propagation MIMO pour la bande UMTS.

Selected mapping (SLM) schemes are commonly employed to reduce the peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. It has been shown that the computational complexity of the traditional SLM scheme can be substantially reduced by adopting conversion vectors obtained by using the inverse fast Fourier transform (IFFT) of the phase rotation vectors in place of the conventional IFFT operations. To ensure that the elements of these phase rotation vectors have an equal magnitude, conversion vectors should have the form of a perfect sequence. This study firstly presents three novel classes of perfect sequence, each of which comprises certain base vectors and their cyclically shifted versions. Three novel low-complexity SLM schemes are then proposed based upon the unique structures of these perfect sequences. It is shown that while the PAPR reduction performances of the proposed schemes are marginally poorer than that of the traditional SLM scheme, the three schemes achieve a substantially lower computational complexity. Since the three proposed PAPR reduction schemes cannot be utilized in the orthogonal frequency division multiple access (OFDMA) system. A low-complexity scheme for PAPR reduction in OFDMA uplink systems using either an interleaved or a sub-band sub-carrier assignment strategy is also proposed in the second part of this study. The proposed scheme requires just one IFFT operation. The PAPR reduction performance of the proposed scheme is only marginally poorer than that of the traditional SLM scheme. However, the proposed schemes have significantly lower computational complexities. Besides, multiple-input multiple-output (MIMO) OFDM systems with space-frequency block coding (SFBC) are well-known for their robust performance in time selective fading channels. However, SFBC MIMO-OFDM systems have a high computational complexity since the number of IFFTs required scales in direct proportion to the number of antennas at the transmitter. Furthermore, SFBC MIMO-OFDM systems have a high PAPR. Accordingly, a low-complexity PAPR reduction scheme for SFBC MIMO OFDM systems with the Alamouti encoding scheme is proposed in this study. Extending this scheme obtains two low-complexity transmitter architectures for SFBC MIMO-OFDM systems with a general encoding matrix and an arbitrary number of transmitter antennas. The proposed schemes achieve a significant reduction in computational complexity by fully exploiting the time-domain signal properties of the transmitted signal. In addition, a PAPR reduction scheme is presented based on the proposed transmitter schemes. It is shown that the PAPR reduction performance of the proposed scheme is almost as good as that of the traditional SLM scheme, but is achieved with a substantially lower computational complexity.

Τα ασύρματα συστήματα τέταρτης γενιάς (4G) στοχεύουν σε πολύ υψηλές ταχύτητες μετάδοσης δεδομένων, 100 Mbps (Mega bits per second) για ταχέως κινούμενους πομποδέκτες και έως 1 Gbps για ακίνητους. Αυτός ο στόχος μπορεί να επιτευχθεί με τα συστήματα Πολλαπλής Εισόδου-Πολλαπλής Εξόδου (Multiple Input-Multiple Output, MIMO) τα οποία χρησιμοποιούν πολλές κεραίες στον πομπό και στο δέκτη. Ο στόχος της παρούσας διδακτορικής διατριβής (ΔΔ) εστιάζεται στην ανάλυση και βελτιστοποίηση αυτών των συστημάτων, υπό το πρίσμα των φαινομένων της σκέδασης και των διαλείψεων μικρής κλίμακας. Το αντικείμενο μελέτης συνοψίζεται στις ακόλουθες θεματικές ενότητες: α) μοντελοποίηση των ασυρμάτων καναλιών με διαλείψεις, β) απόδοση ακριβών και εύχρηστων μαθηματικών εκφράσεων της εργοδικής (μέσου όρου) χωρητικότητας των ασύρματων συστημάτων που χρησιμοποιούν πολλές κεραίες στο δέκτη, γ) αύξηση της εργοδικής χωρητικότητας του συστήματος ΜΙΜΟ χρησιμοποιώντας πληροφορία από το μέσο διάδοσης. Αρχικά περιγράφεται η γενική μοντελοποίηση του ασύρματου καναλιού που είναι αναγκαία για την κατανόηση βασικών εννοιών για την ανάλυση που θα ακολουθήσει. Αυτό έχει ως στόχο μία σύντομη περιγραφή των βασικών χαρακτηριστικών ενός οποιουδήποτε ασύρματου καναλιού και να γίνουν κατανοητές κάποιες σημαντικές έννοιες που προκύπτουν και χρησιμοποιούνται κατά κόρον στις ασύρματες επικοινωνίες. Πιο συγκεκριμένα, παρατίθενται βασικές θεωρητικές γνώσεις όπου περιγράφονται τα διάφορα προβλήματα διάδοσης, δίνοντας μια σύντομη περιγραφή των φυσικών φαινομένων που εμπλέκονται, χωρίς να εμβαθύνουμε σε πολύπλοκες μαθηματικές σχέσεις. Στη συνέχεια, γίνεται προσπάθεια ακριβέστερης μοντελοποίησης, με χρήση στοχαστικών διαδικασιών, των ασύρματων μη επιλεκτικών στη συχνότητα καναλιών με διαλείψεις (frequency non-selective fading channels) σε περιβάλλον τρισδιάστατης ανισοτροπικής σκέδασης καναλιού Rice. Με τον όρο ανισοτροπική εννοείται ότι η λήψη των διαφόρων συνιστωσών για το αζιμούθιο επίπεδο γίνεται από κάποιους τομείς γωνιών και όχι από όλες τις κατευθύνσεις, ενώ στο επίπεδο της ανύψωσης θεωρούμε την ύπαρξη ενός τομέα άφιξης των συνιστωσών στον οποίο η ισχύς δεν κατανέμεται ομοιόμορφα αλλά βάσει μιας κατανομής. Επιπλέον λόγω της θεώρησης καναλιού Rice, συμπεριλαμβάνεται η ύπαρξη μιας δεσπόζουσας συνιστώσας με σταθερό πλάτος η οποία συνήθως προέρχεται από οπτική επαφή του πομπού με το δέκτη. Θεωρώντας συγκεκριμένες κατανομές για την άφιξη των συνιστωσών σε αυτούς τους τομείς από τη διεθνή βιβλιογραφία, εξάγεται αναλυτικά η συνάρτηση της αυτοσυσχέτισης και το φάσμα της ολίσθησης των συχνοτήτων σε αναλυτική μορφή και υπολογίζονται σημαντικά μεγέθη που εκφράζουν την ταχύτητα αυξομείωσης του σήματος και τη διάρκεια των διαλείψεων. Επιπλέον με αυτό τον τρόπο είναι δυνατόν να καθοριστεί η απόσταση μεταξύ των κεραιών που πρέπει να τηρείται ώστε να εξασφαλίζονται οι υψηλές επιδόσεις. Σε αστικό περιβάλλον, αποδεικνύεται ότι η ελάχιστη απόσταση μεταξύ των κεραιών ενός πομποδέκτη θα πρέπει να είναι μεγαλύτερη από ότι σε ένα υπαίθριο περιβάλλον. Στην επόμενη ενότητα επιτυγχάνεται η απόδοση ακριβών και εύχρηστων μαθηματικών εκφράσεων της εργοδικής (μέσου όρου) χωρητικότητας των ασύρματων συστημάτων που χρησιμοποιούν πολλές κεραίες στο δέκτη σε περιβάλλον Nakagami (που θεωρείται από τα πιο αντιπροσωπευτικά για την περιγραφή της ασύρματης διάδοσης σε κλειστούς χώρους) με όσο το δυνατό απλούστερες μαθηματικές συναρτήσεις. Με αυτό τον τρόπο, η ταχύτητα μετάδοσης δεδομένων εκφράζεται συναρτήσει των φυσικών παραμέτρων του συστήματος, δηλαδή το κανάλι, τον αριθμό των κεραιών κτλ. Ήδη έχουν γίνει πολλές δημοσιεύεις σε αυτό τον τομέα για διάφορες περιπτώσεις μοντελοποίησης των καναλιών (Rayleigh, Rice κτλ) και για διάφορες τεχνικές λήψης. Όμως υπάρχουν αρκετές περιπτώσεις όπου υπάρχουν κενά στη διεθνή βιβλιογραφία ή η έκφραση της χωρητικότητας δεν γίνεται με κλειστές μαθηματικές μορφές. Έτσι παρουσιάζονται αναλυτικές μαθηματικές εκφράσεις της εργοδικής χωρητικότητας των συστημάτων SIMO που δεν υπήρχαν έως τώρα στη διεθνή βιβλιογραφία, για διάφορες περιπτώσεις γνώσης του καναλιού. Αυτό γίνεται κάνοντας τον άμεσο παραλληλισμό των συστημάτων SIMO με τις διάφορες τεχνικές διαφορισμού. Εξετάζεται η εργοδική χωρητικότητα ενός συστήματος SIMO το οποίο λειτουργεί σε κανάλι διαλείψεων Nakagami-m στο οποίο όλες οι ζεύξεις είναι ανεξάρτητες αλλά δεν είναι κατά ανάγκη όμοιες. Συγκεκριμένα εξάγονται μαθηματικές εκφράσεις κλειστού τύπου για την εργοδική χωρητικότητα συστημάτων Equal Gain Combining και Selection Combining και Switch and Stay Combining δύο κλάδων. Επιπλέον, παρουσιάζεται για πρώτη φορά, η εργοδική χωρητικότητα ενός συστήματος SIMO στο οποίο δεν εφαρμόζεται καμία τεχνική διαφορικής λήψης και εξάγονται πολύ διδακτικά συμπεράσματα. Αυτό σημαίνει ότι ο δέκτης δεν έχει καμία πληροφορία για το κανάλι (no channel state information CSI) και απλά προσθέτει τα λαμβανόμενα σήματα από κάθε κλάδο-ζεύξη. Επιπλέον γίνεται προσπάθεια οι μαθηματικοί τύποι να είναι εύχρηστοι και υλοποιήσιμοι χωρίς την χρήση ιδιαίτερων μαθηματικών λογισμικών. Ουσιαστικά η μαθηματική έκφραση της χωρητικότητας των συστημάτων SIMO σε κανάλι διαλείψεων Nakagami-m, ανάγεται στην επίλυση ενός είδους ολοκληρώματος που περιέχει ταυτόχρονα τη λογαριθμική συνάρτηση, την εκθετική συνάρτηση και πολυώνυμα νιοστής δύναμης. Αυτός ο τύπος ολοκληρωμάτων είναι δυσεπίλυτος και προκύπτει συχνά στις ασύρματες επικοινωνίες. Στην τελευταία ενότητα, γίνεται προσπάθεια αύξησης του μέσου όρου της χωρητικότητας του συστήματος ΜΙΜΟ χρησιμοποιώντας πληροφορία από το μέσο διάδοσης. Πιο συγκεκριμένα μελετάται η πολιτική εκπομπής, αν ο πομπός γνωρίζει τις παραμέτρους του καναλιού οι οποίες είναι δυνατό να γνωστοποιηθούν στον πομπό σε ρεαλιστικό επίπεδο. Ως παράμετροι του καναλιού οι οποίες είναι απαραίτητο να είναι γνωστές, θεωρούνται ο μέσος όρος και η διασπορά του καναλιού που είναι δυνατό να μετρηθούν στην πράξη ιδιαίτερα για κανάλια που δε μεταβάλλονται πάρα πολύ γρήγορα στο χρόνο. Το πρόβλημα της μεγιστοποίησης της εργοδικής χωρητικότητας, στην γενική του μορφή έως τώρα αντιμετωπίζεται μόνο με χρονοβόρες υπολογιστικές μεθόδους που απαιτούν αρκετή υπολογιστική ισχύ, καθιστώντας τη λύση μη εφαρμόσιμη σε πραγματικό χρόνο και επομένως μη ρεαλιστική. Το πρόβλημα είναι δυσεπίλυτο και οι μόνες αναλυτικές λύσεις που υπάρχουν αναφέρονται σε ιδιαίτερες περιπτώσεις. Η παρούσα ΔΔ ασχολείται με τη μεγιστοποίηση της εργοδικής χωρητικότητας του συστήματος MISO (Multiple Input-Single Output) το οποίο χρησιμοποιεί την τεχνική beamforming στην εκπομπή. Το πρόβλημα επιλύεται και η λύση του ανάγεται στη λύση ενός συστήματος δύο εξισώσεων το οποίο λύνεται αριθμητικά. Έτσι είναι δυνατή η μεγιστοποίηση της χωρητικότητας σε πραγματικό χρόνο χωρίς ιδιαίτερη υπολογιστική ισχύ. Έως τώρα η προσέγγιση αυτού του προβλήματος γίνεται αποκλειστικά με αλγορίθμους μεγιστοποίησης μη γραμμικού προγραμματισμού. Επιπλέον εξετάζοντας τη λύση του απλού συστήματος , εξάγονται καθολικά συμπεράσματα που εκφράζουν το γενικό πρόβλημα. Για τη μεγιστοποίηση του συστήματος MISO beamforming, απαιτήθηκε η διανυσματική ανάλυση του μέσου όρου του καναλιού και του διανύσματος beamforming του πομπού σε μία κατάλληλη ορθοκανονική βάση. Έτσι το πρόβλημα ανάγεται στην εύρεση των γωνιών που σχηματίζει το διάνυσμα beamforming με την ορθοκανονική βάση ώστε να μεγιστοποιείται η χωρητικότητα για δεδομένες παραμέτρους του καναλιού. Με αυτή τη μέθοδο το πρόβλημα επιλύεται πολύ εύκολα με αριθμητικές μεθόδους. Αυτό δίνει, πέρα από την ίδια τη λύση, τη δυνατότητα να γίνει σύγκριση και με υπάρχουσες μεθόδους που προσέγγιζαν τη λύση, όπως η μεγιστοποίηση του σηματοθορυβικού λόγου (Signal to Noise Ratio, SNR). Επίσης αποδεικνύεται ότι το λαμβανόμενο SNR στο δέκτη επηρεάζει το διάνυσμα beamforming που μεγιστοποιεί την χωρητικότητα. Λαμβάνοντας υπόψη όλα αυτά, προτείνεται ένας κανόνας για την πολιτική εκπομπής του πομπού. Η μεθοδολογία που αναπτύχθηκε μπορεί να βοηθήσει σημαντικά στην επίλυση του γενικότερου προβλήματος της μεγιστοποίησης της χωρητικότητας σε συστήματα ΜΙΜΟ. / 4G Wireless Communication Systems aim at high data rates, 100 Mbps (Mega bits per second) for high speed transceivers and up to 1 Gbps for stationary transceivers. This target can be accomplished with Multiple Input Multiple Output (MIMO) Systems which use multiple antennas at both the transmitter and the receiver. The subject of this Philosophy Diploma (PhD) dissertation focuses on analysis and optimization of these systems, taking into account the effects of small scale fading and scattering which occur in a wireless channel. The subject of this study is summarized in the following thematic units: a) Fading channel modelling b) Closed-form mathematical expressions for the ergodic capacity of wireless systems which use multiple antennas at the receiver c) increase MISO ergodic capacity through channel state information. Initially, the general wireless fading channel model is described which is necessary for the better understanding of the analysis used in this dissertation. This aims at a brief description of the basic characteristics of the wireless channel. Specifically, general theoretical knowledge of propagation channel is presented, giving a description of the phenomena occurring in the channel without presenting complex mathematical expressions. Next, using stochastic procedures, an accurate model of frequency non-selective Rician fading channel with 3 dimensional anisotropic scattering is presented. The term anisotropic means that the arrival of the multipath components comes from some specific sectors and not from any direction. In the elevation plane, we assume a sector for the arrival of the multipath components in which power does not arrive uniformly but follows a specific distribution. In addition, assuming a communication system operating in a Rice fading channel, a dominant component is included which usually represents the Line of Sight (LOS) component between the transmitter and the receiver. Taking into account international literature and assuming specific probability density functions for the angle of arrivals in these sectors, analytical mathematical expressions of the auto-correlation function and the power spectral density of the received signal are derived. Moreover important measures of the level crossing rate and the average duration of fades are calculated. By this analysis, the system designer is able to estimate the optimal distance between antennas in order to assure high performance of the communication system. It is proved that the distance between antennas should be greater in rural than in urban environments. In the next section, accurate closed-form mathematical expressions for the ergodic capacity of SIMO (Single Input Multiple Output) systems in Nakagami fading channel are derived with the help of known and easy to use mathematical functions (Nakagami fading is appropriate for indoor channel modelling). Thus channel capacity is expressed with respect to the physical system parameters such as: amount of fading, number of antennas etc. Many studies have been published for different cases of fading channel models (Rayleigh, Rice, etc) and diversity techniques. But for some cases there are no mathematical expressions for the ergodic capacity or it is expressed in a no closed form way. Thus in this study, new analytical mathematical expressions for the ergodic capacity of SIMO systems with different channel knowledge cases are derived. Also the relation between diversity techniques and SIMO systems is taken into account. We assume that the SIMO system operates in a Nakagami fading channel where each branch is statistically independent but not identically distributed. More precisely, new ergodic capacity formulas for dual Equal Gain Combining, Selection Combining and Switch and Stay Combining techniques are presented. In addition, a new mathematical formula for the ergodic capacity of a SIMO system with no channel knowledge is presented, resulting in useful conclusions. All these mathematical expressions are calculated with mathematical functions that are included in any mathematical software. Essentially, the calculation of the ergodic capacity of SIMO systems in Nakagami fading channels entails the calculation of an integral which contains the logarithmic function, the exponential function and n power polynomials. This type of integral is intractable and arises frequently in wireless communications. In the last section, the ergodic capacity of a MIMO channel using channel state information is studied. In particular, this dissertation studies the transmit strategy if the transmitter knows the statistical parameters of the channel which is feasible in a realistic scenario. The statistical parameters of the channel that have to be transferred to the transmitter are channel mean and covariance. These parameters can be measured in practice especially for low time variant channels. Transmitter optimization problem, in its general form, is tackled only with hard optimization methods which are not feasible for real time applications due to large processing time. The problem is intractable and the only analytical solutions in literature are referred to special cases. The current dissertation studies the ergodic capacity optimization problem of a MISO (Multiple Input-Single Output) system which uses beamforming as its transmit strategy. The problem is solved through a system of two equations which is solved numerically. Thus the problem is extremely simplified and beamforming capacity optimization is feasible even for real time applications. So far this problem was tackled with non linear programming optimization methods. Also examining the solution for the MISO system, it is provided intuition into the problem. Also general results are presented which express the general problem. Beamforming capacity optimization solution was achieved by following an analytical approach that projects the beamforming vector on an appropriate orthonormal basis defined by the eigenvectors of the channel covariance matrix. Thus the problem reduces to calculation of the angles between the beamforming vector and the orthonormal basis which maximize capacity for given channel parameters. Following this method, the problem is solved very easily through numerical root finding algorithms. Besides the solution itself, a comparison against existing approximate solutions is possible, e.g. SNR (Signal to Noise Ratio) maximization solution. It is proved that the optimal beamforming vector is dependent on the received SNR. Taking into account all the arising results, a rule of thumb for the transmit policy is proposed. In addition, the used method can help significantly towards the solution of the MIMO transmitter optimization problem.

This work concentrates on the application of diversity techniques and space time block coding for future high speed mobile wireless communications on multicarrier systems. At first, alternative multicarrier kernels robust for high speed doubly-selective fading channel are sought. They include the comparisons of discrete Fourier transform (DFT), fractional Fourier transform (FrFT) and wavelet transform (WT) multicarrier kernels. Different wavelet types, including the raised-cosine spectrum wavelets are implemented, evaluated and compared. From different wavelet families, orthogonal wavelets are isolated from detailed evaluations and comparisons as suitable for multicarrier applications. The three transforms are compared over a doubly-selective channel with the WT significantly outperforming all for high speed conditions up to 300 km/hr. Then, a new wavelet is constructed from an ideal filter approximation using established wavelet design algorithms to match any signal of interest; in this case under bandlimited criteria. The new wavelet showed better performance than other traditional orthogonal wavelets. To achieve MIMO communication, orthogonal space-time block coding, OSTBC, is evaluated next. First, the OSTBC is extended to assess the performance of the scheme over extended receiver diversity order. Again, with the extended diversity conditions, the OSTBC is implemented for a multicarrier system over a doubly-selective fading channel. The MIMO-OFDM systems (implemented using DFT and WT kernels) are evaluated for different operating frequencies, typical of LTE standard, with Doppler effects. It was found that, during high mobile speed, it is better to transmit OFDM signals using lower operating frequencies. The information theory for the 2-transmit antenna OSTBC does not support higher order implementation of multi-antenna systems, which is required for the future generation wireless communications systems. Instead of the OSTBC, the QO-STBC is usually deployed to support the design of higher order multi-antenna systems other than the 2-transmit antenna scheme. The performances of traditional QO-STBC methods are diminished by some off-diagonal (interference) terms such that the resulting system does not attain full diversity. Some methods for eliminating the interference terms have earlier been discussed. This work follows the construction of cyclic matrices with Hadamard matrix to derive QO-STBC codes construction which are N-times better than interference free QO-STBC, where N is the number of transmit antenna branches.

Multi-antenna wireless systems have become very popular due to their theoretically predicted higher spectral efficiencies and improved performance compared to single-antenna systems. Large-scale multiple-input multiple-output (MIMO) systems refer to wireless systems where communication terminals employ tens to hundreds of antennas to achieve in-creased spectral efficiencies/sum rates, reliability, and power efficiency. Large-scale multi-antenna systems are attractive to meet the increasing wireless data rate requirements, without compromising on the bandwidth. This thesis addresses key signal processing issues in large-scale MIMO systems. Specifically, the thesis investigates efficient algorithms for signal detection and channel estimation in large-scale MIMO systems. It also investigates ‘spatial modulation,’ a multi-antenna modulation scheme that can reduce the number of transmit radio frequency (RF) chains, without compromising much on the spectral efficiency. The work reported in this thesis is comprised of the following two parts: 1 investigation of low-complexity receiver algorithms based on Markov chain Monte Carlo (MCMC) technique, tabu search, and belief propagation for large-scale uplink multiuser MIMO systems, and 2 investigation of achievable rates and signal detection in generalized spatial modulation. 1. Receiver algorithms for large-scale multiuser MIMO systems on the uplink In this part of the thesis, we propose low-complexity algorithms based on MCMC techniques, Gaussian sampling based lattice decoding (GSLD), reactive tabu search (RTS), and factor graph based belief propagation (BP) for signal detection on the uplink in large-scale multiuser MIMO systems. We also propose an efficient channel estimation scheme based on Gaussian sampling. Markov chain Monte Carlo (MCMC) sampling: We propose a novel MCMC based detection algorithm, which achieves near-optimal performance in large dimensions at low complexities by the joint use of a mixed Gibbs sampling (MGS) strategy and a multiple restart strategy with an efficient restart criterion. The proposed mixed Gibbs sampling distribution is a weighted mixture of the target distribution and uniform distribution. The presence of the uniform component in the sampling distribution allows the algorithm to exit from local traps quickly and alleviate the stalling problem encountered in conventional Gibbs sampling. We present an analysis for the optimum choice of the mixing ratio. The analysis approach is to define an absorbing Markov chain and use its property regarding the expected number of iterations needed to reach the global minima for the first time. We also propose an MCMC based algorithm which exploits the sparsity in uplink multiuser MIMO transmissions, where not all users are active simultaneously. Gaussian sampling based lattice decoding: Next, we investigate the problem of searching the closest lattice point in large dimensional lattices and its use in signal detection in large-scale MIMO systems. Specifically, we propose a Gaussian sampling based lattice decoding (GSLD) algorithm. The novelty of this algorithm is that, instead of sampling from a discrete distribution as in Gibbs sampling, the algorithm iteratively generates samples from a continuous Gaussian distribution, whose parameters are obtained analytically. This makes the complexity of the proposed algorithm to be independent of the size of the modulation alpha-bet. Also, the algorithm is able to achieve near-optimal performance for different antenna and modulation alphabet settings at low complexities. Random restart reactive tabu search (R3TS): Next, we study receiver algorithms based on reactive tabu search (RTS) technique in large-scale MIMO systems. We propose a multiple random restarts based reactive tabu search (R3TS) algorithm that achieves near-optimal performance in large-scale MIMO systems. A key feature of the proposed R3TS algorithm is its performance based restart criterion, which gives very good performance-complexity tradeoff in large-dimension systems. Lower bound on maximum likelihood (ML) bit error rate (BER) performance: We propose an approach to obtain lower bounds on the ML performance of large-scale MIMO systems using RTS simulation. In the proposed approach, we run the RTS algorithm using the transmitted vector as the initial vector, along with a suitable neighborhood definition, and find a lower bound on number of errors in ML solution. We demonstrate that the proposed bound is tight (within about 0.5 dB of the optimal performance in a 16×16MIMO system) at moderate to high SNRs. Factor graph using Gaussian approximation of interference (FG-GAI): Multiuser MIMO channels can be represented by graphical models that are fully/densely connected (loopy graphs), where conventional belief propagation yields suboptimal performance and requires high complexity. We propose a solution to this problem that uses a simple, yet effective, Gaussian approximation of interference (GAI) approach that carries out a linear per-symbol complexity message passing on a factor graph (FG) based graphical model. The proposed algorithm achieves near-optimal performance in large dimensions in frequency-flat as well as frequency-selective channels. Gaussian sampling based channel estimation: Next, we propose a Gaussian sampling based channel estimation technique for large-scale time-division duplex (TDD) MIMO systems. The proposed algorithm refines the initial estimate of the channel by iteratively detecting the data block and using that knowledge to improve the estimated channel knowledge using a Gaussian sampling based technique. We demonstrate that this algorithm achieves near-optimal performance both in terms of mean square error of the channel estimates and BER of detected data in both frequency-flat and frequency-selective channels. 2. Generalized spatial modulation In the second part of the thesis, we investigate generalized spatial modulation (GSM) in point-to point MIMO systems. GSM is attractive because of its ability to work with less number of transmit RF chains compared to traditional spatial multiplexing, without com-promising much on spectral efficiency. In this work, we show that, by using an optimum combination of number of transmit antennas and number of transmit RF chains, GSM can achieve better throughput and/or BER than spatial multiplexing. We compute tight bounds on the maximum achievable rate in a GSM system, and quantify the percentage savings in the number of transmit RF chains as well as the percentage increase in the rate achieved in GSM compared to spatial multiplexing. We also propose a Gibbs sampling based algorithm suited to detect GSM signals, which yields impressive BER performance and complexity results.