One of the fundamental limitations of mobile radio communications is their time-varying fading channel. This thesis addresses the efficient use of channel state information to improve the communication systems, with a particular emphasis on practical issues such as compatibility with the existing wireless systems and low complexity implementation. The closed-loop transmit diversity technique is used to improve the performance of the downlink channel in MIMO communication systems. For example, the WCDMA standard endorsed by 3GPP adopts a mode of downlink closed-loop scheme based on partial channel state information known as mode 1 of 3GPP. Channel state information is fed back from the mobile unit to the base station through a low-rate uncoded feedback bit stream. In these closed-loop systems, feedback error and feedback delay, as well as the sub-optimum reconstruction of the quantized feedback data, are the usual sources of deficiency. In this thesis, we address the efficient reconstruction of the beamforming weights in the presence of the feedback imperfections, by exploiting the residual redundancies in the feedback stream. We propose a number of algorithms for reconstruction of beamforming weights at the base-station, with the constraint of a constant transmit power. The issue of the decoding at the receiver is also addressed. In one of the proposed algorithms, channel fading prediction is utilized to combat the feedback delay. We introduce the concept of Blind Antenna Verification which can substitute the conventional Antenna Weight Verification process without the need for any training data. The closed-loop mode 1 of 3GPP is used as a benchmark, and the performance is examined within a WCDMA simulation framework. It is demonstrated that the proposed algorithms have substantial gain over the conventional method at all mobile speeds, and are suitable for the implementation in practice. The proposed approach is applicable to other closed-loop schemes as well. The problem of (long-range) prediction of the fading channel is also considered, which is a key element for many fading-compensation techniques. A linear approach, usually used to model the time evolution of the fading process, does not perform well for long-range prediction applications. We propose an adaptive algorithm using a state-space approach for the fading process based on the sum-sinusoidal model. Also to enhance the widely-used linear approach, we propose a tracking method for a multi-step linear predictor. Comparing the two methods in our simulations shows that the proposed algorithm significantly outperforms the linear method, for both stationary and non-stationary fading processes, especially for long-range predictions. The robust structure, as well as the reasonable computational complexity, makes the proposed algorithm appealing for practical applications.

Biological signal processing can help us gain knowledge about biological complexity, as well as using this knowledge to engineer better systems. Three areas are identified as critical to understanding biology: 1) understanding DNA, 2) examining the overall biological function and 3) evaluating these systems in environmental (ie: turbulent) conditions. DNA is investigated for coding structure and redundancy, and a new tandem repeat region, an indicator of a neurodegenerative disease, is discovered. The linear algebraic framework can be used for further analysis and techniques. The work illustrates how signal processing is a tool to reverse engineer biological systems, and how our better understanding of biology can improve engineering designs. Then, the way a single-cell mobilizes in response to a chemical gradient, known as chemotaxis, is examined. Inspiration from receptor clustering in chemotaxis combined with a Hebbian learning method is shown to improve a gradient-source (chemical/thermal) localization algorithm. The algorithm is implemented, and its performance is evaluated in diffusive and turbulent environments. We then show that sensor cross-correlation can be used in solving chemical localization in difficult turbulent scenarios. This leads into future techniques which can be designed for gradient source tracking. These techniques pave the way for use of biologically-inspired sensor networks in chemical localization.

Αντικείμενο αυτής της μεταπτυχιακής εργασίας είναι ο εντοπισμός της ύπαρξης μιας κυρίαρχης ευρείας ζώνης ισοτροπικής πηγής κι η εκτίμηση των συντεταγμένων θέσης αυτής, όταν αυτή βρίσκεται σ’ έναν τρισδιάστατο ή δισδιάστατο χώρο, ο οποίος εποπτεύεται και παρακολουθείται από ένα δίκτυο τυχαία διασκορπισμένων αισθητήρων. Οι κόμβοι του δικτύου μπορούν να περιέχουν ακουστικά, παλμικά κι άλλου είδους μικροηλεκτρομηχανολογικά στοιχεία αίσθησης του περιβάλλοντος. Κατά την αίσθηση ενός γεγονότος ενδιαφέροντος μπορούν να αυτοοργανωθούν σ’ ένα συγχρονισμένο ασύρματο ραδιοδίκτυο χρησιμοποιώντας χαμηλής κατανάλωσης πομποδέκτες spread spectrum, ώστε να επικοινωνούν μεταξύ τους και με τους κεντρικούς επεξεργαστές. Ο εντοπισμός της ύπαρξης μιας κυρίαρχης πηγής σ’ ένα δίκτυο αισθητήρων, με τα παραπάνω χαρακτηριστικά, επιτεύχθηκε με τη χρήση μιας τυφλής μεθόδου μορφοποίησης λοβού, γνωστή ως μέθοδος συλλογής της μέγιστης ισχύος. Η μέθοδος αυτή, η οποία υλοποιήθηκε στα πλαίσια αυτής της εργασίας, παρέχει τις εκτιμήσεις των σχετικών χρόνων καθυστέρησης άφιξης του σήματος της κυρίαρχης πηγής στους αισθητήρες του δικτύου ως προς έναν αισθητήρα αναφοράς. Κύριο αντικείμενο μελέτης αυτής της εργασίας είναι ο υπολογισμός του κυρίαρχου ιδιοδιανύσματος του δειγματοληπτημένου πίνακα αυτοσυσχέτισης. Αυτό επιτυγχάνεται στη βιβλιογραφία που μελετήθηκε είτε με χρήση της δυναμικής μεθόδου είτε με χρήση της μεθόδου ιδιοανάλυσης. Ανά στιγμιότυπο δειγμάτων απαιτείται η ανανέωση του πίνακα αυτοσυσχέτισης κι ο υπολογισμός του κυρίαρχου ιδιοδιανύσματος. Όμως, οι δύο παραπάνω μέθοδοι για τον υπολογισμό αυτό χρειάζονται αυξημένη πολυπλοκότητα μιας κι η διάσταση του πίνακα είναι αρκετά μεγάλη. Η συνεισφορά της εργασίας αυτής έγκειται στη μείωση αυτής της πολυπλοκότητας με τη χρήση μιας προσαρμοστικής μεθόδου υπολογισμού του κυρίαρχου ιδιοδιανύσματος. Τέλος, αντικείμενο της εργασίας αυτής είναι και το πρόβλημα εντοπισμού και παρακολούθησης των συντεταγμένων θέσης της κυρίαρχης πηγής από τις εκτιμήσεις των σχετικών χρόνων καθυστέρησης άφιξης. / Object of this postgraduate work are the detection of presence of an isotropic wideband dominant source and the estimate of its coordinates of placement (localization), when the source is found in a three or two dimensional space, which is supervised and watched by a randomly distributed sensor network. The nodes of the network may contain acoustical, vibrational and other MEM-sensing (Micro-Electro-Mechanical) elements. Upon sensing an event of interest, they can self-organize into a synchronized wireless radio network using low-power spread-spectrum transceivers to communicate among themselves and central processors. The detection of presence of a dominant source in a sensor network, with the above characteristics, was achieved with the use of a blind beamforming method, known as the maximum power collection method. This method, which was implemented in the context of this work, provides estimates of the relative time delays of arrival (relative TDEs - Time Delay Estimations) of the dominant source’s signal to the sensors of the network referenced to a reference sensor. The main object of study of the work is the calculation of the dominant eigenvector of the sampled correlation matrix. This is achieved, in the bibliography that was studied, either by using the power method or with use of the SVD method (Singular Value Decomposition). Per snapshot of samples it is required to update the autocorrelation matrix and to calculate the dominant eigenvector. However, the above two methods for this calculation have an increased complexity because the dimension of the matrix is high enough. The contribution of this work lies in the reduction of that complexity by using an adaptive method for the dominant eigenvector calculation. Finally, this work also focuses on the problem of localization and tracking of the coordinates of placement of the dominant source from the estimates of the relative time delays of arrival.

Abstract This thesis focuses on transceiver optimization for heterogeneous multi-user multiple-input multiple-output (MIMO) wireless communications systems. The aim is to design decentralized beamforming methods with low signaling overhead for improved spatial spectrum utilization. A wide range of transceiver optimization techniques are covered, with particular consideration of decentralized optimization, fast convergence, computational complexity and signaling limitations. The proposed methods are shown to provide improved rate of convergence, when compared to the conventional weighted minimum MSE (WMMSE) approach. This makes them suitable for time-correlated channel conditions, in which the ability to follow the changing channel conditions is essential. Coordinated beamforming under quality of service (QoS) constraints is considered for interfering broadcast channel. Decomposition based decentralized processing approaches are shown to enable the weighted sum rate maximization (WSRMax) in time-correlated channel conditions. Pilot-aided decentralized WSRMax beamformer estimation is studied for coordinated multi-point (CoMP) joint processing (JP). In stream specific estimation (SSE), all effective channels are individually estimated. The beamformers are then constructed from the locally estimated channels. On the other hand, with direct estimation (DE) of the beamformers, only the intended signal needs to be separately estimated and the covariance matrices are implicitly estimated from the received pilot training matrices. This makes the pilot design more robust to pilot contamination. These methods show that CoMP JP is feasible even in relatively fading channel conditions and with limited backhaul capacity by employing decentralized beamformer processing. In the final part of the thesis, a relay-assisted cellular system with decentralized processing is considered, in which users are served either directly by the base stations or via relays for WSRMax or sum power minimization subject to rate constraints. Zero-forcing and coordinated beamforming provide a trade-off between complexity, in-band signaling and spectrum utilization. Relays are shown to be beneficial in many scenarios when the in-band signaling is accounted for. This thesis shows that decentralized downlink MIMO transceiver design with a reasonable computational complexity is feasible in various system architectures even when signaling resources are limited and channel conditions are moderately fast fading. / Tiivistelmä Tämä väitöskirja keskittyy lähetin- ja vastaanotinoptimointiin heterogeenisissä monikäyttäjä- ja moniantennijärjestelmissä. Tavoitteena on parantaa tilatason suorituskykyä tutkimalla hajautettuja keilanmuodostusmenetelmiä, joissa ohjaussignaloinnin tarve on alhainen. Erityisesti keskitytään hajautetun keilanmuodostuksen optimointiin, nopeaan konvergenssiin, laskennalliseen kompleksisuuteen sekä signaloinnin rajoitteisiin. Esitettyjen menetelmien osoitetaan parantavan konvergenssinopeutta ja vähentävän signaloinnin tarvetta, verrattaessa tunnettuun WMMSE-menetelmään. Nämä mahdollistavat lähetyksen aikajatkuvissa kanavissa, joissa kanavan muutosten seuraaminen on erityisen tärkeää. Näiden menetelmien osoitetaan mahdollistavan hajautetun ja priorisoidun tiedonsiirtonopeuden maksimoinnin monisolujärjestelmissä sekä aikajatkuvissa kanavissa käyttäjäkohtaisilla siirtonopeustakuilla. Pilottiavusteisten lähetys- ja vastaanotinkeilojen estimointia tutkitaan yhteislähetysjärjestelmissä. Yksittäisten lähetyskanavien estimoinnissa effektiiviset kanavat estimoidaan yksitellen, ja lähetys- ja vastaanotinkovarianssimatriisit muodostetaan summaamalla paikalliset kanavaestimaatit. Suoraestimoinnissa ainoastaan oman käyttäjän effektiivinen kanava estimoimaan erikseen. Tällöin kovarianssimatriisit saadaan suoraan vastaanotetuista pilottisignaaleista. Tämä tekee estimaateista vähemmän herkkiä häiriölle. Hajautetun yhteislähetyksen osoitetaan olevan mahdollista, jopa verrattain nopeasti muuttuvissa kanavissa sekä rajallisella verkkoyhteydellä lähettimien välillä. Viimeisessä osassa tutkitaan välittäjä-avusteisia järjestelmiä, joissa käyttäjiä palvellaan joko suoraan tukiasemasta tai välittäjä-aseman kautta. Optimointikriteereinä käytetään siirtonopeuden maksimointia sekä lähetystehon minimointia siirtonopeustakuilla. Nollaanpakottava sekä koordinoitu keilanmuodostus tarjoavat valinna laskennallisen kompleksisuuden, ohjaussignaloinnin sekä suorituskyvyn välillä. Välittäjä-avusteisen lähetyksen osoitetaan olevan hyödyllisiä useissa tilanteissa, kun radiorajanpinnan yli tapahtuvan signaloinnin tarve otetaan huomioon keilanmuodostuksessa. Tässä väitöskirjassa osoitetaan hajautetun keilanmuodostuksen olevan mahdollista verrattaen vähäisillä laskennallisilla resursseilla heterogeenisissä moniantennijärjestelmissä. Esitetyt menetelmät tarjoavat ratkaisuja järjestelmiin, joissa ohjaussignalointiresurssit ovat rajallisia ja radiokanava on jatkuvasti muuttuva.

Abstract This thesis focuses on the timely problem of energy-efficient transmission for wireless multiantenna cellular systems. The emphasis is on transmit beamforming (BF) and active antenna set optimization to maximize the network-wide energy efficiency (EE) metric, i.e., the number of transmitted bits per energy unit. The fundamental novelty of EE optimization is that it incorporates the transceivers' processing power in addition to the actual transmit power in the BF design. The key features of the thesis are that it focuses on sophisticated power consumption models (PCMs), giving useful insights into the EE of current cellular systems in particular, and provides mathematical tools for EE optimization in future wireless networks generally. The BF problem is first studied in a multiuser multiple-input single-output system by using a PCM scaling with transmit power and the number of active radio frequency (RF) chains. To find the best performance, a globally optimal solution based on a branch-reduce-and-bound (BRB) method is proposed, and two efficient designs based on zero-forcing and successive convex approximation (SCA) are derived for practical applications. Next, joint BF and antenna selection (JBAS) is studied, which can switch off some RF chains for further EE improvements. An optimal BRB method and efficient SCA-based algorithms exploiting continuous relaxation (CR) or sparse BF are proposed to solve the resulting mixed-Boolean nonconvex problem (MBNP). In a multi-cell system, energy-efficient coordinated BF is explored under two optimization targets: 1) the network EE maximization and 2) the weighted sum EEmax (WsumEEmax). A more sophisticated PCM scaling also with the data rate and the associated computational complexity is assumed. The SCA-based methods are derived to solve these problems in a centralized manner, and distributed algorithms relying only on the local channel state information and limited backhaul signaling are then proposed. The WsumEEmax problem is solved using SCA combined with an alternating direction method of multipliers, and iterative closed-form algorithms having easily derivable computational complexity are developed to solve both problems. The work is subsequently extended to a multi-cell multigroup multicasting system, where user groups request multicasting data. For the MBNP, a modeling method to improve the performance of the SCA for solving the CR is proposed, aiming at encouraging the relaxed Boolean variables to converge at the binary values. A second approach based on sparse BF, which introduces no Boolean variables, is also derived. The methods are then modified to solve the EE and sum rate trade-off problem. Finally, the BF design with multiantenna receivers is considered, where the users can receive both unicasting and multicasting data simultaneously. The performances of the developed algorithms are assessed via thorough computer simulations. The results show that the proposed algorithms provide 30-300% EE improvements over various conventional methods in the BF optimization, and that JBAS techniques can offer further gains of more than 100%. / Tiivistelmä Tämä väitöskirja keskittyy ajankohtaiseen energiatehokkaaseen lähetinsuunnitteluun langattomissa solukkoverkoissa, joissa suorituskykymittarina käytetään energiatehokkuuden (energy efficiency (EE)) maksimointia, eli kuinka monta bittiä pystytään lähettämään yhtä energiayksikköä kohti. Työn painopiste on lähettimien keilanmuodostuksen (beamforming (BF)) ja aktiivisten lähetinantennien optimoinnissa. EE-optimoinnin uutuusarvo on ottaa lähettimien prosessoinnin tehonkulutus huomioon keilanmuodostuksen suunnittelussa, varsinaisen lähetystehon lisäksi. Työ antaa hyvän käsityksen erityisesti tämänhetkisten solukkoverkkojen energiatehokkuudesta, ja luo työkaluja EE-optimointiin tulevaisuuden järjestelmissä. Ensin suunnitellaan keilanmuodostus yksisolumallissa, jossa tehonkulutus kasvaa lähetystehon ja aktiivisten radiotaajuusketjujen lukumäärän mukana. Ongelmaan johdetaan optimaalinen ratkaisu, ja kaksi käytännöllistä menetelmää perustuen nollaanpakotukseen tai peräkkäinen konveksi approksimaatio (successive convex approximation (SCA)) -ideaan. Seuraavaksi keskitytään keilanmuodostuksen ja antenninvalinnan yhteisoptimointiin (joint beamforming and antenna selection (JBAS)), jossa radiotaajuusketjuja voidaan sulkea EE:n parantamiseksi. Tähän ehdotetaan optimaalinen menetelmä ja kaksi käytännöllistä SCA-menetelmää perustuen binääristen ja jatkuvien muuttujien yhteisoptimointiongelman relaksaatioon, tai harvan vektorin optimointiin. Monisoluverkon EE-optimoinnissa käytetään yksityiskohtaisempaa tehonkulutusmallia, joka skaalautuu myös datanopeuden ja prosessoinnin monimutkaisuuden mukaan. Työssä käytetään kahta suorituskyvyn mittaria: 1) koko verkon energiatehokkuuden, ja 2) painotettujen energiatehokkuuksien summien maksimointia (weighted sum EEmax (WsumEEmax)). Ensin johdetaan keskitetyt ratkaisut SCA-ideaa käyttäen. Tämän jälkeen keskitytään hajautettuun optimointiin, joka pystytään toteuttamaan paikallisen kanavatiedon avulla, kun matalanopeuksinen skalaariarvojen jako on käytettävissä tukiasemien välillä. Ensin WsumEEmax-ongelma ratkaistaan yhdistämällä SCA ja kerrointen vaihtelevan suunnan menetelmä, ja lisäksi ehdotetaan iteratiivinen suljetun muodon ratkaisu molempiin ongelmiin, joka mahdollistaa tarkan laskennallisen monimutkaisuuden määrityksen. Lopussa työ laajennetaan monisoluverkkoon, jossa tukiasemat palvelevat käyttäjäryhmiä ryhmälähetyksenä. Keskittymällä JBAS-ongelmaan, ensin ehdotetaan lähestymistapa parantaa SCA-menetelmän suorituskykyä yhteisoptimointiongelman relaksaation ratkaisemisessa. Toinen yksinkertaisempi lähestymistapa perustuu harvan vektorin optimointiin, joka ei vaadi binäärisiä muuttujia. Lisäksi menetelmiä muunnellaan myös energiatehokkuuden ja summadatanopeuden kompromissin optimointiin. Lopussa työ ottaa huomioon vielä moniantennivastaanottimet, joka mahdollistaa sekä täsmälähetyksen että ryhmälähetyksen samanaikaisesti. Menetelmien suorituskykyä arvioidaan laajamittaisilla tietokonesimulaatioilla. Tulokset näyttävät väitöskirjan menetelmien lisäävän energiatehokkuutta 30-300% verrattuna lukuisiin perinteisiin menetelmiin BF-optimoinnissa, ja JBAS-menetelmät antavat vielä yli 100% lisää suorituskykyä.

Abstract This thesis focuses on the design of coordinated downlink beamforming techniques for wireless multi-cell multi-user multi-antenna systems. In particular, cellular and cognitive radio networks are considered. In general, coordinated beamforming schemes aim to improve system performance, especially at the cell-edge area, by controlling inter-cell interference. In this work, special emphasis is put on practical coordinated beamforming designs that can be implemented in a decentralized manner by relying on local channel state information (CSI) and low-rate backhaul signaling. The network design objective is the sum power minimization (SPMin) of base stations (BSs) while providing the guaranteed minimum rate for each user. Decentralized coordinated beamforming techniques are developed for cellular multi-user multiple-input single-output (MISO) systems. The proposed iterative algorithms are based on classical primal and dual decomposition methods. The SPMin problem is decomposed into two optimization levels, i.e., BS-specific subproblems for the beamforming design and a network-wide master problem for the inter-cell interference coordination. After the acquisition of local CSI, each BS can independently compute its transmit beamformers by solving the subproblem via standard convex optimization techniques. Interference coordination is managed by solving the master problem via a traditional subgradient method that requires scalar information exchange between the BSs. The algorithms make it possible to satisfy the user-specific rate constraints for any iteration. Hence, delay and signaling overhead can be reduced by limiting the number of performed iterations. In this respect, the proposed algorithms are applicable to practical implementations unlike most of the existing decentralized approaches. The numerical results demonstrate that the algorithms provide significant performance gains over zero-forcing beamforming strategies. Coordinated beamforming is also studied in cellular multi-user multiple-input multiple-output (MIMO) systems. The corresponding non-convex SPMin problem is divided into transmit and receive beamforming optimization steps that are alternately solved via successive convex approximation method and the linear minimum mean square error criterion, respectively, until the desired level of convergence is attained. In addition to centralized design, two decentralized primal decomposition-based algorithms are proposed wherein the transmit and receive beamforming designs are facilitated by a combination of pilot and backhaul signaling. The results show that the proposed MIMO algorithms notably outperform the MISO ones. Finally, cellular coordinated beamforming strategies are extended to multi-user MISO cognitive radio systems, where primary and secondary networks share the same spectrum. Here, network optimization is performed for the secondary system with additional interference constraints imposed for the primary users. Decentralized algorithms are proposed based on primal decomposition and an alternating direction method of multipliers. / Tiivistelmä Tämä väitöskirja keskittyy yhteistoiminnallisten keilanmuodostustekniikoiden suunnitteluun langattomissa monisolu- ja moniantennijärjestelmissä, erityisesti solukko- ja kognitiiviradioverkoissa. Yhteistoiminnalliset keilanmuodostustekniikat pyrkivät parantamaan verkkojen suorituskykyä kontrolloimalla monisoluhäiriötä, erityisesti tukiasemasolujen reuna-alueilla. Tässä työssä painotetaan erityisesti käytännöllisten yhteistoiminnallisten keilanmuodostustekniikoiden suunnittelua, joka voidaan toteuttaa hajautetusti perustuen paikalliseen kanavatietoon ja tukiasemien väliseen informaationvaihtoon. Verkon suunnittelutavoite on minimoida tukiasemien kokonaislähetysteho samalla, kun jokaiselle käyttäjälle taataan tietty vähimmäistiedonsiirtonopeus. Hajautettuja yhteistoiminnallisia keilanmuodostustekniikoita kehitetään moni-tulo yksi-lähtö -solukkoverkoille. Oletuksena on, että tukiasemat ovat varustettuja monilla lähetysantenneilla, kun taas päätelaitteissa on vain yksi vastaanotinantenni. Ehdotetut iteratiiviset algoritmit perustuvat klassisiin primaali- ja duaalihajotelmiin. Lähetystehon minimointiongelma hajotetaan kahteen optimointitasoon: tukiasemakohtaisiin aliongelmiin keilanmuodostusta varten ja verkkotason pääongelmaan monisoluhäiriön hallintaa varten. Paikallisen kanavatiedon hankkimisen jälkeen jokainen tukiasema laskee itsenäisesti lähetyskeilansa ratkaisemalla aliongelmansa käyttäen apunaan standardeja konveksioptimointitekniikoita. Monisoluhäiriötä kontrolloidaan ratkaisemalla pääongelma käyttäen perinteistä aligradienttimenetelmää. Tämä vaatii tukiasemien välistä informaationvaihtoa. Ehdotetut algoritmit takaavat käyttäjäkohtaiset tiedonsiirtonopeustavoitteet jokaisella iterointikierroksella. Tämä mahdollistaa viiveen pienentämisen ja tukiasemien välisen informaatiovaihdon kontrolloimisen. Tästä syystä ehdotetut algoritmit soveltuvat käytännön toteutuksiin toisin kuin useimmat aiemmin ehdotetut hajautetut algoritmit. Numeeriset tulokset osoittavat, että väitöskirjassa ehdotetut algoritmit tuovat merkittävää verkon suorituskyvyn parannusta verrattaessa aiempiin nollaanpakotus -menetelmiin. Yhteistoiminnallista keilanmuodostusta tutkitaan myös moni-tulo moni-lähtö -solukkoverkoissa, joissa tukiasemat sekä päätelaitteet ovat varustettuja monilla antenneilla. Tällaisessa verkossa lähetystehon minimointiongelma on ei-konveksi. Optimointiongelma jaetaan lähetys- ja vastaanottokeilanmuodostukseen, jotka toistetaan vuorotellen, kunnes algoritmi konvergoituu. Lähetyskeilanmuodostusongelma ratkaistaan peräkkäisillä konvekseilla approksimaatioilla. Vastaanottimen keilanmuodostus toteutetaan summaneliövirheen minimoinnin kautta. Keskitetyn algoritmin lisäksi tässä työssä kehitetään myös kaksi hajautettua algoritmia, jotka perustuvat primaalihajotelmaan. Hajautettua toteutusta helpotetaan pilottisignaloinnilla ja tukiasemien välisellä informaationvaihdolla. Numeeriset tulokset osoittavat, että moni-tulo moni-lähtö -tekniikoilla on merkittävästi parempi suorituskyky kuin moni-tulo yksi-lähtö -tekniikoilla. Lopuksi yhteistoiminnallista keilanmuodostusta tarkastellaan kognitiiviradioverkoissa, joissa primaari- ja sekundaarijärjestelmät jakavat saman taajuuskaistan. Lähetystehon optimointi suoritetaan sekundaariverkolle samalla minimoiden primaarikäyttäjille aiheuttamaa häiriötä. Väitöskirjassa kehitetään kaksi hajautettua algoritmia, joista toinen perustuu primaalihajotelmaan ja toinen kerrointen vaihtelevan suunnan menetelmään.

[pt] Processamento em arranjos de sensores é uma área com vasta aplicação, tanto civil quanto militar, por exemplo em sonar, radar, sismologia e comunicações sem fio. Por meio de processamento espacial e espácio-temporal é possível melhorar suas funcionalidades e explorar novas possibilidades. Esta área vem atraindo cada vez mais a atenção e os esfor¸cos da comunidade científica, especialmente agora, em que antenas phased-array se estabeleceram como uma tecnologia comercial e madura. Neste contexto, nós tratamos o problema de processamento com posto reduzido em processamento espacial (beamforming) e espácio-temporal de sinais radar e a nova área de radares com função dual de radar e comunicações (dualfunction radar-communications, DFRC), que pode ser resumida na incorporação de mensagens de comunicações nas transmissıes radar como uma tarefa secundária. Nesta tese, nós investigamos a aplicação de um novo esquema de reduções de posto baseado em interpolação e decimação em duas áreas distintas: processamento espacial e processamento espácio-temporal de sinais radar. Este algoritmo para redução de posto nunca havia sido testado nestes ambientes antes e apresentou resultados bastante expressivos. Nós também propomos simplificações para reduzir a complexidade computacional do algoritmo em bemforming. Quanto ao tópico de DFRC, nós propomos dois métodos originais para incorporar modulação de amplitude/fase aos lóbulos laterais do diagrama de irradiação do radar de forma robusta. Os métodos propostos são muito mais simples do que o estado-da-arte e apresentam desempenho superior em termos de robustez e aplicabilidade em operações de tempo-real. Nós ainda provemos várias outras análises, comparações e contribuições a esta nova área. / [en] Array processing is an area with many civilian and military applications, e.g. sonar, radar, seismology and wireless communications. By means of space and space-time processing it is possible to enhance their features and explore new possibilities. This area has been attracting increasingly more attention and gathering more efforts of the science community, especially now, that phased array antennas are established as a commercial and mature technology. Within this context, we address the problem of reduced rank processing in space and space-time radar signal processing and the new area of dual-function radar-communications (DFRC), which may be summarized as embedding communication messages into radar emissions as a secondary task for the radar. In this thesis, we investigate the application of a new joint interpolation and decimation rank reducing scheme in two different areas: beamforming and space-time radar processing. This rank reducing algorithm was never tested within these contexts before and shows impressive results. We also propose simplifications for decreasing the computational complexity of the algorithm in beamforming. In the topic of DFRC, we propose two original robust radar-embedded sidelobe phase/amplitude modulation methods which have simple closed form equations. The proposed methods are much simpler than the state of the art and have superior performance in terms of robustness and real-time applicability.

We often have to face the fact that several signals are mixed together in unknown environment. The signals must be first extracted from the mixture in order to interpret them correctly. This problem is in signal processing society called blind source separation. This dissertation thesis deals with multi-channel separation of audio signals in real environment, when the source signals outnumber the sensors. An introduction to blind source separation is presented in the first part of the thesis. The present state of separation methods is then analyzed. Based on this knowledge, the separation systems implementing fuzzy time-frequency mask are introduced. However these methods are still introducing nonlinear changes in the signal spectra, which can yield in musical noise. In order to reduce musical noise, novel methods combining time-frequency binary masking and beamforming are introduced. The new separation system performs linear spatial filtering even if the source signals outnumber the sensors. Finally, the separation systems are evaluated by objective and subjective tests in the last part of the thesis.

Digital beamforming is a powerful signal processing technique used in many communication and radar sensing applications. However, despite its many advantages, its high cost makes it a less popular choice than other directional antenna options. The development of a low cost architecture for digital beamforming would make it a more feasible option, allowing it to be used for a number of new applications. Specifically, the Collaborative, Adaptive Sensing of the Atmosphere (CASA) project’s Distributed Collaborative Adaptive Sensing (DCAS) system, a low cost weather radar system, could benefit from the incorporation of digital beamforming into small, inexpensive but highly functional radars. Existing DBF architectures are implemented in complex systems which include a number of expensive processing modules and other associated hardware. This project shows a low-cost digital beamforming architecture that has been developed by utilizing today’s powerful and inexpensive FPGA devices along with recently available low-voltage-differential-signaling enabled multi-channel analog to digital conversion hardware. The utilization of commercially available devices rather than custom hardware allows this architecture to be manufactured at a fraction of the cost of most. This makes it a viable alternative to the classic dish antennas for the DCAS system, allowing a reduction in size and cost which will benefit deployment. The flexibility of an FPGA-based DBF system will result in a more robust radar system. With this in mind, an architecture has been developed, fabricated and evaluated.

Sound source separation is a popular and active research area, especially with modern machine learning techniques. In this thesis, the focus is on single-channel separation of two speakers into individual streams, and specifically considering the case where two speakers are also accompanied by background noise. There are different methods to separate speakers and in this thesis three different methods are evaluated: the Conv-TasNet, the DPTNet, and the FaSNetTAC.  The methods were used to train models to perform the sound source separation. These models were evaluated and validated through three experiments. Firstly, previous results for the chosen separation methods were reproduced. Secondly, appropriate models applicable for NFC's datasets and applications were created, to fulfill the aim of this thesis. Lastly, all models were evaluated on an independent dataset, similar to datasets from NFC. The results were evaluated using the metrics SI-SNRi and SDRi. This thesis provides recommended models and methods suitable for NFC applications, especially concluding that the Conv-TasNet and the DPTNet are reasonable choices.