Κανάλια με αραιή κρουστική απόκριση εμφανίζονται πάρα πολύ συχνά σε εφαρμογές ασύρματων κυρίως τηλεπικοινωνιακών συστημάτων. Παραδείγματα τέτοιων εφαρμογών είναι η εκπομπή HDTV (HighDefinitionΤelevision) ή εκπομπή μέσω υποθαλλάσιων ακουστικών καναλιών. Σε όλες αυτές τις εφαρμογές η μορφή του καναλιού διαμορφώνεται από το φαινόμενο της πολυδιόδευσης. Συνεπώς ο δέκτης λαμβάνει έναν περιορισμένο αριθμό από διαφορετικές εκδοχές του εκπεμπόμενου σήματος καθεμία με διαφορετική εξασθένιση και καθυστέρηση. Ως εκ τούτου η συνάρτηση της κρουστικής απόκρισης ενός τέτοιου καναλιού αποτελείται από ελάχιστα μη μηδενικά στοιχεία σε συγκριση με το μήκος της, καθένα από τα οποία αντιστοιχεί σε ένα από τα μονοπάτια πολυδιόδευσης. Για την ισοστάθμιση αυτών των καναλιών έχουν προταθεί διάφορες τεχνικές, πολλές από τις οποίες εκμεταλλεύονται την ιδιαίτερη αυτή μορφή της κρουστικής απόκρισης. Πολλοί από τους προτεινόμενους ισοσταθμιστές καναλιών απαιτούν την παρεμβολή ακολουθίων εκμάθησης ανάμεσα στην ακολουθία δεδομένων, οι οποίες είναι εκ των προτέρων γνωστές στον δέκτη. Χρησιμοποιούνται δε προκειμένου ο αλγόριθμος εκτίμησης του καναλιού να συγκλίνει όσο το δυνατόν ταχύτερα στην επιθυμητή τιμή. Μειονέκτημα αυτών των μεθόδων είναι η επιβάρυνση του ωφέλιμου εύρους ζώνης που συνεπάγεται. Ωστόσο η εκ των προτέρων γνώση της αραιής μορφής της κρουστικής απόκρισης εχει δώσει αφορμή για την σχεδίαση ισοσταθμιστών με περιορισμένο μήκος αλλά εξίσου καλή απόδοση. Οι συμβατικές τεχνικές εκτίμησης καναλιών, όπως η Least Square μέθοδος, δεν εκμεταλλεύονται αυτή την γνώση. Οι πρόσφατες δε εξελίξεις στην ανακατασκευή αραιών σημάτων μέσω τεχνικών συμπιεσμένης καταγραφής (compressed sensing) έχουν οδηγήσει στην μελέτη της εφαρμογής τέτοιων τεχνικών στο πρόβλημα της εκτίμησης καναλιού. Η μέθοδος της συμπιεσμένης καταγραφής στηρίζεται στη δυνατότητα ανακατασκευής αραιών σημάτων από πλήθος δειγμάτων αισθητά κατώτερο από αυτό που προβλέπει το θεωρητικό όριο του Nyquist. Έχει αποδειχθεί ότι η ανακατασκευή αυτή είναι δυνατή όταν το σήμα ή έστω κάποιος μετασχηματισμός του περιέχει λίγα μη μηδενικά στοιχεία σε σχέση με το μήκος του. Οι εφαρμογές αυτών των τεχνικών εκτείνονται και σε άλλα πεδία όπως η επεξεργασία εικόνας, η μαγνητική τομογραφία, η ανάλυση γεωφυσικών δεδομένων, η επεξεργασία εικόνας radar, η αστρονομία κ.α. Στα πλαίσια αυτής της εργασίας παρουσιάζονταιοι βασικές αρχές που διέπουν την ανακατασκευή αραιών σημάτων μέσω της επίλυσης υποορισμένων συστημάτων γραμμικών εξισώσεων. Παράλληλα παρουσιάζονται οι κυριότεροι αλγόριθμοι που έχουν προταθεί για την υλοποίηση της και εξετάζονται ως προς την απόδοση και την υπολογιστική πολυπλοκότητα τους. Εν συνεχεία εξετάζεται η εφαρμογή αυτών των αλγορίθμων στο πρόβλημα της εκτίμησης αραιών καναλιών. Προτείνονται δε ισοσταθμιστές αραιών καναλιών βασισμένοι σε εκτιμητές απόκρισης που χρησιμοποιούν τεχνικές συμπιεσμένης καταγραφής. / Channels with sparse impulse response are very common in wireless telecommunications systems applications. Example of such channel is HDTV channel where multipath distribution of the transmitted signal results in a sparse form of the channel impulse response. Several different versions of the same signal are received, each one with its own gain and delay. As a result, channel impulse response has a few non zero taps compared to its length, its one corresponding to a different distribution path. Several techniques for estimating and equalizing such channels have been proposed, most of them taking advantage of this sparse form of the impulse response. The transmission of a training sequence known to the receiver is required for this purpose. It is used so that the channel estimation algorithm at the receiver converges faster. The disadvantage of the use of a training sequence is the fact that the useful bandwidth is reduced. However the a priori knowledge of the sparse form of the training sequence has led to the design of equalizers that require short training sequences but have satisfactory performance. Channel estimation techniques based on least square method do not take advantage of this idea. On the other hand recent progress on sparse signal reconstruction using compressed sensing techniques has led scientists to research the potential use of such algorithms in channel estimation. Compressed sensing is based on the idea of reconstructing a sparse signal using less samples that those predicted by Nyquist theorem. It has been proved that such a reconstruction is feasible if the reconstructed signal is sparse enough. In this dissertation several sparse signal reconstruction algorithms are presented and their performance and complexity are evaluated. Then the application of these algorithms on channel estimation equalization problem is analyzed.

Passive coherent location (PCL) radars employ illuminators of opportunity to detect and track targets. This silent operating mode provides many advantages such as low cost and interception immunity. Many radiation sources have been exploited as illumination sources such as broadcasting and telecommunication transmitters. The classical architecture of the bistatic PCL radars involves two receiving channels: a reference channel and a surveillance channel. The reference channel captures the direct-path signal from the transmitter, and the surveillancesignal collects the possible target echoes. The two major challenges for the PCL radars are the reference signal noise and the surveillance signal static clutter. A noisy reference signal degrades the detection probability by increasing the noise-floor level of the detection filter output. And the static clutter presence in the surveillance signal reduces the detector dynamic range and buries low magnitude echoes.In this thesis, we consider a PCL radar based on the digital video broadcasting-terrestrial (DVB-T) signals, and we propose a set of improved methods to deal with the reference signal noise and the static clutter in the surveillance signal. The DVB-T signals constitute an excellentcandidate as an illumination source for PCL radars; they are characterized by a wide bandwidth and a high radiated power. In addition, they provide the possibility of reconstructing the reference signal to enhance its quality, and they allow a straightforward static clutter suppressionin the frequency domain. This thesis proposes an optimum method for the reference signal reconstruction and an improved method for the static clutter suppression.The optimum reference signal reconstruction minimizes the mean square error between the reconstructed signal and the exact one. And the improved static clutter suppression method exploits the possibility of estimating the propagation channel. These two methods extend thefeasibility of a single receiver PCL radar, where the reference signal is extracted from the direct-path signal present in the surveillance signal. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

abstract: Both two-way relays (TWR) and full-duplex (FD) radios are spectrally efficient, and their integration shows great potential to further improve the spectral efficiency, which offers a solution to the fifth generation wireless systems. High quality channel state information (CSI) are the key components for the implementation and the performance of the FD TWR system, making channel estimation in FD TWRs crucial. The impact of channel estimation on spectral efficiency in half-duplex multiple-input-multiple-output (MIMO) TWR systems is investigated. The trade-off between training and data energy is proposed. In the case that two sources are symmetric in power and number of antennas, a closed-form for the optimal ratio of data energy to total energy is derived. It can be shown that the achievable rate is a monotonically increasing function of the data length. The asymmetric case is discussed as well. Efficient and accurate training schemes for FD TWRs are essential for profiting from the inherent spectrally efficient structures of both FD and TWRs. A novel one-block training scheme with a maximum likelihood (ML) estimator is proposed to estimate the channels between the nodes and the residual self-interference (RSI) channel simultaneously. Baseline training schemes are also considered to compare with the one-block scheme. The Cramer-Rao bounds (CRBs) of the training schemes are derived and analyzed by using the asymptotic properties of Toeplitz matrices. The benefit of estimating the RSI channel is shown analytically in terms of Fisher information. To obtain fundamental and analytic results of how the RSI affects the spectral efficiency, one-way FD relay systems are studied. Optimal training design and ML channel estimation are proposed to estimate the RSI channel. The CRBs are derived and analyzed in closed-form so that the optimal training sequence can be found via minimizing the CRB. Extensions of the training scheme to frequency-selective channels and multiple relays are also presented. Simultaneously sensing and transmission in an FD cognitive radio system with MIMO is considered. The trade-off between the transmission rate and the detection accuracy is characterized by the sum-rate of the primary and the secondary users. Different beamforming and combining schemes are proposed and compared. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2018

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / In this work, the performance of MIMO systems based on space-time coding is investigated through multilinear algebra, more specifically, by means of tensor decompositions, pulling away a bit from commonly used matrix models. We assume a system composed of P transmit and M receive antennas, consisting of a combination of a space-time block code (STBC) with a formatting filter. This filter is formed by a precoding matrix and a matrix that maps the precoded signal onto the transmit antennas. For the considered system, two contributions are presented to solve the problem of channel estimation. First, we propose a tensor-based channel estimation method for orthogonal STBCs in MIMO systems, by focusing on the specific case of the Alamouti scheme. We resort to a third order PARATUCK2 tensor model for the received signal, the third dimension of which is related to the presence of the formatting filter. By capitalizing on this tensor model, a channel estimation method based on the alternating least squares (ALS) algorithm is proposed. As a second contribution, a generalization of this method to an arbitrary nonorthogonal STBC is made, where a generalized structure is proposed for the formatting filter, introducing a fourth dimension into the tensor signal model. In this case, we make use of the PARATUCK(2-4) model followed by its reduction to a structured PARAFAC model, from which a closed-form solution to the channel estimation problem is established. The performance metrics considered for evaluating the proposed channel estimation method are: (I) the quality of the estimation in terms of NMSE and (II) the system reliability in terms of Bit Error Rate. / Neste trabalho, o desempenho de sistemas MIMO baseados em codificaÃÃo espaÃo temporal à investigado via Ãlgebra multilinear, mais especificamente, por meio de decomposiÃÃes tensoriais, afastando-se um pouco dos modelos matriciais comumente adotados. Assume-se um sistema composto de P antenas transmissoras e M receptoras, consistindo de uma combinaÃÃo de um cÃdigo espaÃo-temporal em bloco com um filtro formatador. Esse filtro à formado por uma matriz de prÃ-codificaÃÃo e uma matriz que mapeia os sinais prÃ-codificados nas antenas transmissoras. Para o sistema considerado, duas contribuiÃÃes sÃo apresentadas para solucionar o problema de estimaÃÃo de canal. Primeiro, à proposto um mÃtodo tensorial de estimaÃÃo de canal para STBCs ortogonais em sistemas MIMO, tomando-se como exemplo o esquema de Alamouti. Tal mÃtodo faz uso de um modelo tensorial PARATUCK2 de terceira ordem para o sinal recebido, cuja terceira dimensÃo està associada à presenÃa do filtro formatador. Aproveitando-se desse modelo tensorial, um mÃtodo de estimaÃÃo de canal baseado no algoritmo dos mÃnimos quadrados alternados à proposto. Como uma segunda contribuiÃÃo, uma generalizaÃÃo desse modelo para um STBC nÃo ortogonal arbitrÃrio à feita, em que uma estrutura generalizada à proposta para o filtro formatador, introduzindo uma quarta dimensÃo no modelo tensorial de sinal. Neste caso, faz-se uso do modelo PARATUCK(2-4) seguido pela sua reduÃÃo a um modelo PARAFAC estruturado, a partir do qual uma soluÃÃo em forma fechada para o problema de estimaÃÃo de canal à estabelecida. As mÃtricas de desempenho consideradas para avaliaÃÃo dos mÃtodos de estimaÃÃo de canal propostos sÃo: (I) A qualidade da estimaÃÃo do canal em termos de NMSE e (II) a confiabilidade do sistema em termos de Taxa de Erro de Bit.

[pt] Sistemas OFDM têm conseguido atenção dos órgãos internacionais de padronização na última década. Vários trabalhos na literatura tratam sobre como otimizar a transmissão desses sistemas na situação de enlace aberto, ou seja, onde não há comunicação reversa entre transmissor e receptor. Este trabalho foca a utilização de enlace fechado para sistemas de transmissão OFDM, um assunto pouco explorado até agora. Aqui focamos principalmente no método recentemente proposto na literatura de alocação dinâmica de símbolos-pilotos. Foi mostrado recentemente que, para sistemas OFDM coerentes e de enlace fechado, essa opção é a que traz mais ganhos. Os símbolos-piloto são usados para obter amostras do comportamento do canal a fim de detectar os símbolos de dado corretamente. Esses pilotos, porém, não precisam estar dispostos para a melhor estimação possível do canal. Almeja-se apenas uma estimação boa o suficiente. Nesse trabalho propõe-se uma estratégia de alocação de pilotos com o objetivo de minimizar a probabilidade de erro de bits do sistema. Sugere-se também um algoritmo de menor complexidade que mantém grande parte do desempenho da solução ótima. Resultados experimentais mostram ganhos bastante significativos. / [en] OFDM systems have gathered quite some attention from international standardization bodies for the past decade. Various works in literature aim at optimizing transmission for these systems in the open-loop scenario, i.e. when there is no feedback communication between transmitter and receiver. This work focuses on the utilization of a closed-loop for OFDM systems, a subject which has not been thoroughly explored so far. Here we primarily focus on the recently proposed method of dynamic pilot-symbol allocation. It was shown in a recent paper that for coherent closed-loop OFDM systems, this option is capable of delivering substantial increases in performance. The pilot-symbols are used to sample channel behavior so that data symbols can be decoded correctly. However, these pilot do not need to be located so as to estimate the channel in the best possible way. The system requires an estimation which is only good enough. In this work, we propose a pilotsymbol allocation strategy with the goal of minimizing the bit error rate. We also suggest a lower complexity algorithm, which attains most of the performance of the optimal solution. Experimental results show significant gains.

Abstract Iterative receiver techniques, multiple-input – multiple-output (MIMO) processing, and orthogonal frequency division multiplexing (OFDM) are amongst the key physical layer technologies when aiming at higher spectral efficiency for a wireless communication system. Special focus is put on iterative detection, decoding, and channel estimation for a MIMO-OFDM system. After designing separately efficient algorithms for the detection, channel decoding, and channel estimation, the objective is to optimize them to work together through optimizing the activation schedules for soft-in soft-out (SfISfO) components. A list parallel interference cancellation (PIC) detector is derived to approximate an a posteriori probability (APP) algorithm with reduced complexity and minimal loss of performance. It is shown that the list PIC detector with good initialization outperforms the K-best list sphere detector (LSD) in the case of small list sizes, whereas the complexities of the algorithms are of the same order. The convergence of the iterative detection and decoding is improved by using a priori information to also recalculate the candidate list, aside from the log-likelihood ratios (LLRs) of the coded bits. Unlike in pilot based channel estimation, the least-squares (LS) channel estimator based on symbol decisions requires a matrix inversion in MIMO-OFDM. The frequency domain (FD) space-alternating generalized expectation-maximization (SAGE) channel estimator calculates the LS estimate iteratively, avoiding the matrix inversion with constant envelope modulation. The performance and computational complexity of the FD-SAGE channel estimator are compared to those of pilot based LS channel estimation with minimum mean square error (MMSE) post-processing exploiting the time correlation of the channel. A time domain (TD) SAGE channel estimator is derived to avoid the matrix inversion in channel estimation based on symbol decisions for MIMO-OFDM systems also with non-constant envelope modulation. An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. It is proposed to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and to find out the optimal activation schedule for them. A semi-analytical expression of the EXIT function is derived for the LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of the channel estimate’s mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the channel decoder. Trellis search algorithms are shown to find the convergence with the lowest possible complexity using the EXIT functions. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided.

Abstract Future wireless systems will require high data rate with low transmit and processing power consumption. A combination of multiple-input multiple-output (MIMO) transmission with orthogonal frequency division multiplexing (OFDM) is a promising approach for offering better performance in terms of the capacity and quality of service (QoS). The detector in the wireless receiver is one of the highest power consuming parts. In order to minimize the power consumption, it is desirable for the detector to be able to change the detection algorithm to suit the channel conditions. In this thesis work, we study the suitability of different MIMO detection algorithms for adaptive operation. The selective spanning with fast enumeration (SSFE), K-best list sphere detector (LSD), linear minimum mean square error (LMMSE), and successive interference cancellation (SIC) detectors are compared to each other in terms of communications performance in the 4 × 4 and 8 × 8 MIMO–OFDM systems. The impact of least squares (LS) and minimum mean square error (MMSE) channel estimation methods, mobile speed, and transmit precoding at the base station on detector algorithm selection is also considered. The SIC detector is shown to suffer from error propagation in poor channel conditions. The SSFE detector is unable to outperform the K-best LSD and is occasionally outperformed by the LMMSE detector. The LMMSE detector is able to outperform the K-best LSD on the low signal-to-noise (SNR) regime when the mobile speed is high and the spatial channel correlation is low or moderate; it is also found to be more robust against channel estimation errors. Because a realistic adaptive detector is expected to support only two detection algorithms, the K-best LSD and LMMSE are selected based on the performance results for application specific integrated circuit (ASIC) architecture design and further comparison. The chosen algorithms are evaluated by considering the performance and implementation results. The K-best LSD provides good performance under challenging channel conditions with the cost of high complexity and power consumption. The LMMSE detector is energy efficient but performs poorly in correlated channels. However, exceptions exist, and detailed results on when to use a simple detector and when to use a complex detector are provided. / Tiivistelmä Tulevaisuuden langattomat tietoliikennejärjestelmät edellyttävät suurta datanopeutta ja vähäistä tehonkulutusta datan lähetyksessä ja käsittelyssä. Monitulo-monilähtötekniikan (MIMO) ja monikantoaaltomoduloinnin (OFDM) yhdistelmä (MIMO–OFDM) on lupaava lähestymistapa hyvän suorituskyvyn saavuttamiseksi, sekä kapasiteetin että luotettavuuden kannalta. Yksi langattoman vastaanottimen eniten tehoa kuluttavista osista on ilmaisin. Tehonkulutuksen minimoimiseksi tulisi ilmaisimen pystyä vaihtamaan ilmaisinalgoritmia radiokanavan olosuhteisiin sopivaksi. Tässä väitöskirjatyössä tarkastellaan erilaisten MIMO-ilmaisinalgoritmien sopivuutta mukautuvaan ilmaisuun. Listapalloilmaisimen (list sphere detector, LSD), valikoivan laajennuksen listailmaisimen (selective spanning with fast enumeration, SSFE), lineaarisen pienimmän keskineliövirheen ilmaisimen (linear minimum mean square error, LMMSE) ja peräkkäisen häiriönpoistoilmaisimen (successive interference cancellation, SIC) suorituskykyjä verrataan toisiinsa sekä 4 × 4 että 8 × 8 MIMO–OFDM järjestelmissä. Pienimmän neliösumman (LS) ja pienimmän keskineliövirheen (MMSE) kanavaestimointialgoritmien, vastaanottimen nopeuden ja lähetyksen esikoodauksen vaikutus ilmaisinalgoritmin valintaan otetaan huomioon vertailussa. Haastavissa kanavaolosuhteissa SIC-ilmaisin kärsii virheen etenemisestä. SSFE-ilmaisimen suorituskyky on huonompi kuin K-best LSD-ilmaisimen, ja joissakin tilanteissa huonompi kuin LMMSE-ilmaisimen. LMMSE-ilmaisin pystyy parempaan suorituskykyyn kuin K-best LSD-ilmaisin kun signaali-kohinasuhde (SNR) on pieni, vastaanottimen nopeus on suuri ja radiokanavan korrelaatio on matala tai kohtalainen. LMMSE-ilmaisin myös kestää epätarkat kanavaestimaatit paremmin kuin LSD-ilmaisin. Realistisessa vastaanottimessa mukautuva ilmaisin tukee vain kahta ilmaisinalgoritmia, ja sen takia K-best LSD and LMMSE-ilmaisimet valittiin suorituskykytulosten perusteella toteutettaviksi ASIC-teknologialla. Valittuja ilmaisinalgoritmeja arvioidaan sekä suorituskyvyn että toteutustulosten perusteella. K-best LSD-ilmaisimella on hyvä suorituskyky haastavissa kanavaolosuhteissa, mutta toteutus on monimutkainen ja tehonkulutus korkea. LMMSE-ilmaisin on energiatehokas, mutta suorituskyky on huono korreloivissa kanavissa. Poikkeuksia näihin tilanteisiin kuitenkin esiintyy, ja työssä esitetään suositus milloin yksinkertaista ilmaisinta voidaan käyttää tehonkulutuksen minimoimiseksi ja milloin taas monimutkainen ilmaisin on välttämätön luotettavan tiedonsiirron takaamiseksi.

Abstract The aim of the thesis is to develop algorithms and architectures to meet the high data rate, low complexity requirements of the future mobile communication systems. Algorithms, architectures and implementations for detection, channel estimation and interference mitigation in the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) receivers are presented. The performance-complexity trade-offs in different receiver algorithms are studied and the results can be utilized in receiver design as well as in system design. Implementation of detectors for spatial multiplexing systems is considered first. The linear minimum mean squared error (LMMSE) and the K-best list sphere detector (LSD) are compared to the successive interference cancellation (SIC) detector. The SIC algorithm was found to perform worse than the K-best LSD when the MIMO channels are highly correlated. The performance difference diminishes when the correlation decreases. With feedback to the transmitter, the performance difference is even smaller, but the full rank transmissions still require a more complex detector. A reconfigurable receiver, using a simple or a more complex detector as the channel conditions change, would achieve the best performance while consuming the least amount of power in the receiver. The use of decision directed (DD) channel estimation is also studied. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark. The performance and complexity of the pilot symbol based least-squares (LS) channel estimator, the minimum mean square error (MMSE) filter and the DD space-alternating generalized expectation-maximization (SAGE) algorithm are studied. DD channel estimation and MMSE filtering improve the performance with high user velocities, where the pilot symbol density is not sufficient. With DD channel estimation, the pilot overhead can be reduced without any performance degradation by transmitting data instead of pilot symbols. Suppression of co-channel interference in the MIMO-OFDM receiver is finally considered. The interference and noise spatial covariance matrix is used in data detection and channel estimation. Interference mitigation is applied for linear and nonlinear detectors. An algorithm to adapt the accuracy of the matrix decomposition and the use of interference suppression is proposed. The adaptive algorithm performs well in all interference scenarios and the power consumption of the receiver can be reduced. / Tiivistelmä Tämän väitöskirjatyön tavoitteena on kehittää vastaanotinalgoritmeja ja -arkkitehtuureja, jotka toteuttavat tulevaisuuden langattomien tietoliikennejärjestelmien suuren datanopeuden ja pienen kompleksisuuden tavoitteet. Työssä esitellään algoritmeja, arkkitehtuureja ja toteutuksia ilmaisuun, kanavaestimointiin ja häiriönvaimennukseen monitulo-monilähtötekniikkaa (multiple-input multiple-output, MIMO) ja ortogonaalista taajuusjakokanavointia (orthogonal frequency division multiplexing, OFDM) yhdistäviin vastaanottimiin. Algoritmeista saatavaa suorituskykyhyötyä verrataan vaadittavaan toteutuksen monimutkaisuuteen. Työn tuloksia voidaan hyödyntää sekä vastaanotin- että järjestelmäsuunnittelussa. Lineaarista pienimmän keskineliövirheen (minimum mean square error, MMSE) ilmaisinta ja listapalloilmaisinta (list sphere detector, LSD) verrataan peräkkäiseen häiriönpoistoilmaisimeen (successive interference cancellation, SIC). SIC-ilmaisimella on huonompi suorituskyky kuin LSD-ilmaisimella radiokanavan ollessa korreloitunut. Korrelaation pienentyessä myös ilmaisimien suorituskykyero pienenee. Erot suorituskyvyissä ovat vähäisiä silloinkin, jos järjestelmässä on takaisinkytkentäkanava lähettimelle. Tällöinkin korkean signaali-kohinasuhteen olosuhteissa LSD-ilmaisimet mahdollistavat tilakanavoidun, suuren datanopeuden tiedonsiirron. Radiokanavan muuttuessa uudelleenkonfiguroitava vastaanotin toisi virransäästömahdollisuuden vaihtelemalla kompleksisen ja yksinkertaisen ilmaisimen välillä. Kanavaestimointialgoritmeja ja niiden toteutuksia vertaillaan käyttämällä lähtökohtana nykyisen mobiilin tiedonsiirtostandardin viitesignaalimallia. Tutkittavat algoritmit perustuvat pienimmän neliösumman (least squares, LS) ja pienimmän keskineliövirheen menetelmään, sekä päätöstakaisinkytkettyyn (decision directed, DD) kanavaestimointialgoritmiin. DD-kanavaestimaattori ja MMSE-suodatin parantavat vastaanottimen suorituskykyä korkeissa käyttäjän nopeuksissa, joissa viitesignaaleiden tiheys ei ole riittävä. DD-kanavaestimoinnilla datanopeutta voidaan nostaa viitesignaaleiden määrää laskemalla vaikuttamatta suorituskykyyn. Työssä tarkastellaan myös saman kanavan häiriön vaimennusta. Häiriöstä ja kohinasta koostuvaa kovarianssimatriisia käytetään ilmaisuun ja kanavaestimointiin. Työssä esitetään adaptiivinen algoritmi matriisihajoitelman tarkkuuden ja häiriön vaimennuksen säätämiseen. Algoritmi mahdollistaa hyvän suorituskyvyn kaikissa häiriötilanteissa vähentäen samalla virrankulutusta.

To meet the urgent demand of high-speed data rate and to support large number of users, the massive multiple-input multiple-output (MIMO) technology is becoming one of the most promising candidates for the next generation wireless communications, namely the 5G. To realize the full potential of massive MIMO, it is necessary to have the channel state information (CSI) (partially) available at the transmitter. Hence, an efficient channel estimation is one of the key enablers and also critical challenges for 5G communications. Dealing with such problems, this dissertation investigates the design of efficient pilot-data transmission pattern and channel estimation in massive MIMO for both multipair relaying and peer-to-peer systems. Firstly, this dissertation proposes a pilot-data transmission overlay scheme for multipair MIMO relaying systems employing either half- or full-duplex (HD or FD) communications at the relay station (RS). In the proposed scheme, pilots are transmitted in partial overlap with data to decrease the channel estimation overhead. The RS can detect the source data by exploiting the asymptotic orthogonality of massive MIMO channels. Due to the transmission overlay, the effective data period is extended, hence improving system throughput. Both theoretical and simulation results verify that the proposed pilot-data overlay scheme outperforms the conventional separate pilot-data design in the limited coherence interval scenario. Moreover, a power allocation problem is formulated to properly adjust the transmission power of source data transmission and relay data forwarding which further improves the system performance. Additionally, this dissertation proposes and analyzes an efficient HD decode-and-forward (DF) scheme, named sum decode-and-forward (SDF), with the physical layer network coding (PNC) in the multipair massive MIMO two-way relaying system. As comparison, a joint decode-and-forward (JDF) scheme applied to the multipair massive MIMO relaying is also proposed and investigated. In the SDF scheme, a half number of pilots are saved compared to the JDF scheme which in turn increases the spectral efficiency of the system. Both the theoretical analyses and numerical results verifies such superiority of the SDF scheme. Further, the power efficiency of the proposed schemes is also investigated. Simulation results show that the signal transmission power can be rapidly reduced if the massive antenna arrays are equipped on the RS and the required data transmission power can further decrease if the training power is fixed. Finally, this dissertation investigates the general channel estimation problem in the massive MIMO system which employs the hybrid analog/digital precoding structure with limited radio-frequency (RF) chains. By properly designing RF combiners and performing multiple trainings, the performance of the proposed channel estimation can approach that of full-chain estimations depending on the degree of channel spatial correlation and the number of RF chains which is verified by simulation results in terms of both mean square error (MSE) and spectral efficiency. Moreover, a covariance matching method is proposed to obtain channel correlation in practice and the simulation verifies its effectiveness by evaluating the spectral efficiency performance in parametric channel models. / Graduate / 0537 / 0544 / leyuanpan@gmail.com

Faced with imminent spectrum scarcity largely due to inflexible licensed band arrangements, cognitive radio (CR) has been proposed to facilitate higher spectrum utilization by allowing cognitive users (CUs) to access the licensed bands without causing harmful interference to primary users (PUs). To achieve this without the aid of PUs, the CUs have to perform spectrum sensing reliably detecting the presence or absence of PU signals. Without reliable spectrum sensing, the discovery of spectrum opportunities will be inefficient, resulting in limited utilization enhancement. This dissertation examines three major techniques for spectrum sensing, which are matched filter, energy detection, and cyclostationary feature detection. After evaluating the advantages and disadvantages of these techniques, we narrow down our research to a focus on cyclostationary feature detection (CFD). Our first contribution is to boost performance of an existing and prevailing CFD method. This boost is achieved by our proposed optimal and sub-optimal schemes for identifying best hypothesis test points. The optimal scheme incorporates prior knowledge of the PU signals into test point selection, while the sub-optimal scheme circumvents the need for this knowledge. The results show that our proposed can significantly outperform other existing schemes. Secondly, in view of multi-antenna deployment in CR networks, we generalize the CFD method to include the multi-antenna case. This requires effort to justify the joint asymptotic normality of vector-valued statistics and show the consistency of covariance estimates. Meanwhile, to effectively integrate the received multi-antenna signals, a novel cyclostationary feature based channel estimation is devised to obtain channel side information. The simulation results demonstrate that the errors of channel estimates can diminish sharply by increasing the sample size or the average signal-to-noise ratio. In addition, no research has been found that analytically assessed CFD performance over fading channels. We make a contribution to such analysis by providing tight bounds on the average detection probability over Nakagami fading channels and tight approximations of diversity reception performance subject to independent and identically distributed Rayleigh fading. For successful coexistence with the primary system, interference management in cognitive radio networks plays a prominent part. Normally certain average or peak transmission power constraints have to be placed on the CR system. Depending on available channel side information and fading types (fast or slow fading) experienced by the PU receiver, we derive the corresponding constraints that should be imposed. These constraints indicate that the second moment of interference channel gain is an important parameter for CUs allocating transmission power. Hence, we develop a cooperative estimation procedure which provides robust estimate of this parameter based on geolocation information. With less aid from the primary system, the success of this procedure relies on statistically correlated channel measurements from cooperative CUs. The robustness of our proposed procedure to the uncertainty of geolocation information is analytically presented. Simulation results show that this procedure can lead to better mean-square error performance than other existing estimates, and the effects of using inaccurate geolocation information diminish steadily with the increasing number of cooperative cognitive users.