Recently there has been a significant amount of interest in the area of wideband and ultra-wideband (UWB) signaling for use in indoor wireless systems. This interest is in part motivated by the notion that the use of large bandwidth signals makes systems less sensitive to the degrading effects of multipath propagation. By reducing the sensitivity to multipath, more robust and higher capacity systems can be realized. However, as signal bandwidth is increased, the complexity of a Rake receiver (or other receiver structure) required to capture the available power also increases. In addition, accurate channel estimation is required to realize this performance, which becomes increasingly difficult as energy is dispersed among more multipath components. In this thesis we quantify the channel response for six signal bandwidths ranging from continuous wave (CW) to 1 GHz transmission bandwidths. We present large scale and small scale fading statistics for both LOS and NLOS indoor channels based on an indoor measurement campaign conducted in Durham Hall at Virginia Tech. Using newly developed antenna positioning equipment we also quantify the spatial correlation of these signals. It is shown that the incremental performance gains due to reduced fading of large bandwidths level off as signals approach UWB bandwidths. Furthermore, we analyze the performance of Rake receivers for the different signal bandwidths and compare their performance for binary phase modulation (BPSK). It is shown that the receiver structure and performance is critical in realizing the reduced fading benefit of large signal bandwidths. We show practical channel estimation degrades performance more for larger bandwidths. We also demonstrate for a fixed finger Rake receiver there is an optimal signal bandwidth beyond which increased signal bandwidth produces degrading results. / Master of Science

In 2001, the Federal Communications Commission made available a large block of spectrum known as the 60 GHz band. The 60 GHz band is attractive because it provides the opportunity of multi-Gbps data rates with unlicensed commercial use. One of the main challenges facing the use of this band is poor propagation characteristics including high path loss and strong attenuation due to oxygen absorption. Antenna arrays have been proposed as a means of combating these effects. This thesis provides an analysis of array processing for communication systems operating in the 60 GHz band. Based on measurement campaigns at 60 GHz, deterministic modeling of the channel through ray tracing is proposed. We conduct a site-specific study using ray tracing to model an outdoor and an indoor environment on the Virginia Tech campus. Because arrays are required for antenna gain and adaptability, we explore the use of arrays as a form of equalization in the presence of channel-induced intersymbol interference. The first contribution of this thesis is to establish the expected performance achieved by arrays in the outdoor environment. The second contribution is to analyze the performance of adaptive algorithms applied to array processing in mobile indoor and outdoor environments. / Master of Science

The detrimental effects rapid power fluctuation has on wireless narrowband communication channels has long been a concern of the mobile radio community as appropriate channel models seek to gauge link quality. Furthermore, advances in signal processing capabilities and the desire for spectrally efficient and low power radio systems have rekindled the interest for adaptive transmission schemes, hence some method of quickly probing the link quality and/or predicting channel conditions is required. Mathematical distributions for modeling the channel profile seek to estimate fading parameters from a finite number of discrete time samples of signal amplitude. While the statistical inference of such estimators has proven to be robust to rapidly shifting channel conditions, the benefits are quickly realized at the expense of processing complexity. Furthermore, computations of the best-known estimation techniques are often iterative, tedious, and complex. This thesis takes a renewed look at estimating fading parameters for the Nakagami-m, Rice-K, and Weibull distributions, specifically by showing that the need to solve transcendental equations in the estimators can be circumvented through use of polynomial approximation in the least-squared error sense or via asymptotic series expansion which often lead to closed-form and simplified expressions. These new estimators are compared to existing ones, the performances of which are comparable while preserving a lower computational complexity. In addition, the thesis also investigates the impact knowledge of the fading profile has on systems employing adaptive switching modulation schemes by characterizing performance in terms of average bit error rates (BER) and spectral efficiency. A channel undergoing Rice-$K$ fading on top of log-normal shadowing is simulated by correlating samples of received signal amplitude according to the user's doppler speed, carrier frequency, etc. The channel's throughput and BER performances are analyzed using the above estimation techniques and compared to non-estimation assumptions. Further discussion on narrowband fading parameter estimation and its applicability to wireless communication channels is provided. / Master of Science

Since the 1950s, hidden Markov models (HMMS) have seen widespread use in electrical engineering. Foremost has been their use in speech processing, pattern recognition, artificial intelligence, queuing theory, and communications theory. However, recent years have witnessed a renaissance in the application of HMMs to the analysis and simulation of digital communication systems. Typical applications have included signal estimation, frequency tracking, equalization, burst error characterization, and transmit power control. Of special significance to this thesis, however, has been the use of HMMs to model fading channels typical of wireless communications. This variegated use of HMMs is fueled by their ability to model time-varying systems with memory, their ability to yield closed form solutions to otherwise intractable analytic problems, and their ability to help facilitate simple hardware and/or software based implementations of simulation test-beds. The aim of this thesis is to employ and exploit hidden Markov fading models within an iterative (turbo) decoding framework. Of particular importance is the problem of channel estimation, which is vital for realizing the large coding gains inherent in turbo coded schemes. This thesis shows that a Markov fading channel (MFC) can be conceptualized as a trellis, and that the transmission of a sequence over a MFC can be viewed as a trellis encoding process much like convolutional encoding. The thesis demonstrates that either maximum likelihood sequence estimation (MLSE) algorithms or maximum <I> a posteriori</I> (MAP) algorithms operating over the trellis defined by the MFC can be used for channel estimation. Furthermore, the thesis illustrates sequential and decision-directed techniques for using the aforementioned trellis based channel estimators <I>en masse</I> with an iterative decoder. / Master of Science

No description available.

Relay-based cooperation promises significant gains in a wireless network as it provides an inde-pendent path between a source and a destination. Using simple single antenna nodes, it exploits the spatial diversity provided by the geographically separated nodes in a network to improve the robustness of the communication system against fading. Among the cooperative commu¬nication schemes, the amplify-and-forward (AF) relaying scheme is considered to be easy to implement since the relay does not need to decode its received signal. Instead, it just forwards to the destination the signal it receives from the source. We analyze the performance of fixed-gain AF relaying with imperfect channel knowledge that is acquired through an AF relay-specific training protocol. The analysis is challenging because the received signal at the destination contains the product (or cascade) of source-relay (SR) and relay-destination (RD) complex baseband channel gains, and additional products terms that arise due to imperfect estimation related errors. We focus on the time-efficient cascaded channel estimation (CCE) protocol to acquire the channel estimates at the destination. Using it, the destination can only estimate the product of SR and RD complex baseband channel gains, but not the two separately. Our analysis encompasses a single AF relay system and an opportunistic system with mul¬tiple AF relays, among which one is selected to forward its received signal to the destination, based on its SR and RD complex baseband channel gains. For a single relay system, we first de¬velop a novel SEP expression and a tight SEP upper bound. We then analyze the opportunistic multi-relay system, in which both selection and coherent demodulation use imperfect channel estimates. A distinctive aspect of our approach is the use of as few simplifying approximations as possible. It results in a new analysis that is accurate at signal-to-noise-ratios as low as 1 dB for single and multi-relay systems. Further, the training protocol is an integral part of the model and analysis. Using an insightful asymptotic analysis, we then present a simple, closed-form, nearly-optimal solution for allocation of energy between pilot and data symbols at the source and relay(s). Further, the optimal energy allocation between a source and a relay is characterized when both together operate under a sum energy constraint, as has often been assumed in the literature. In summary, the sum total of the results in this work provides a rigorous and accurate performance characterization and optimization of cascaded channel estimation for AF relaying.

Dans les systèmes de radiocommunications à hauts débits, les canaux de propagationsont dispersifs dans le temps à cause de la présence de la propagation par trajetsmultiples (multipath). Cette dispersion temporelle engendre de l’interférence entresymboles (IES) qui dégrade les performances du système en réception. Pour luttercontre l’IES, un égaliseur doit être utilisé en réception. Afin de retrouver les symbolesémis à partir des échantillons reçus, l’égaliseur doit disposer d’une bonne estimationdu canal. Cette estimation est en général effectuée en utilisant une séquence d’apprentissageconnue par le récepteur. Augmenter la longueur de la séquence d’apprentissageaméliore la qualité de l’estimation du canal mais diminue le débit utile de transmission.Une question qui se pose concerne la longueur de la séquence d’apprentissage àchoisir afin d’avoir une bonne estimation du canal sans diminuer significativement ledébit utile de transmission. Une solution basée sur la maximisation d’uneborne inférieure de la capacité du canal a été proposée pour une transmission sur uncanal mono-antenne (SISO : Single-Input Single-Output) sélectif en fréquence [56]et une transmission sur un canal multi-antennes (MIMO : Multiple-Input Multiple-Output) non sélectif en fréquence. Dans [10], Buzzi et al. considèrent une transmissionsur un canal MIMO non sélectif en fréquence et proposent de détecter les données etd’estimer le canal de manière itérative. Leur solution consiste à optimiser la longueurde la séquence d’apprentissage en minimisant le rapport de l’Erreur QuadratiqueMoyenne (EQM) de l’estimation du canal sur le débit utile. Toutes ces études nespécifient pas le récepteur utilisé.Dans cette thèse, nous nous intéressons à la détection selon le critère Maximum APosteriori (MAP) et nous proposons d’optimiser la séquence d’apprentissage selondes critères que nous définirons. Nous considérons le cas d’une transmission sur uncanal SISO sélectif en fréquence et le cas d’une transmission sur un canal MIMOnon sélectif en fréquence. Notons que dans le cas de la transmission sur un canalMIMO non sélectif en fréquence, une égalisation spatiale est nécessaire pour séparerles trains de données émis par les différentes antennes émettrices. Nous commençonspar considérer le cas où un récepteur non itératif composé d’un estimateur du canalet d’un détecteur MAP est utilisé. L’estimation du canal est réalisée selon le critèredes moindres carrés en utilisant uniquement une séquence d’apprentissage. Puis, nousconsidérons le cas où le récepteur est itératif et est composé d’un détecteur MAP etd’un décodeur MAP. Afin d’améliorer les performances du récepteur, le détecteur etle décodeur échangent des informations extrinsèques à chaque itération. Ces informationsseront utilisées comme des informations a priori à l’itération suivante. Dans cecas, l’estimation du canal est améliorée itérativement en utilisant les décisions duressur les bits codés à la sortie du décodeur. Cette technique est appelée estimationbootstrap du canal. Nous calculons analytiquement un Rapport Signal à Bruit (RSB)utile de transmission qui tient compte de la perte en termes de débit utile due à l’utilisationde la séquence d’apprentissage. Nous considérons le problème de l’optimisationde la séquence d’apprentissage afin de maximiser ce RSB utile pour les deux cas detransmission considérés (SISO et MIMO) et pour les deux types de récepteur (nonitératif et itératif).Les interférences entre symboles sont généralement traitées par des techniques d’égalisation.Cependant, l’égaliseur est d’autant plus complexe que la longueur du canalsélectif en fréquence est grande. Afin de contourner ce problème de complexité del’égalisation, une modulation OFDM (Orthogonal Frequency Division Multiplexing)peut être utilisée. Quand l’émetteur peut estimer le canal OFDM, comme c’est le casdu mode de transmission TDD (Time Division Duplexing), une modulation adaptativeet une allocation de la puissance peuvent être utilisées en émission, ce qui amélioresignificativement les performances du système. Cependant, en mode FDD (FrequencyDivision Duplexing), les canaux sur la voie montante et sur la voie descendante sontindépendants. Le récepteur peut dans ce cas estimer le canal et retourner cette estimationvers l’émetteur sur un canal de retour à faible débit . Le récepteur peutégalement effectuer l’allocation des puissances et/ou de la modulation et la renvoyerà l’émetteur. La voie de retour nécessite alors une réservation de ressources.Quand ces ressources sont importantes, les informations retournées à l’émetteur sontfiables. Mais, ceci entraîne une perte en termes de débit utile. Un compromis doit alorsêtre trouvé entre la voie de retour et la qualité de l’allocation des puissances et/ou dela modulation. Nous étudions le problème de l’optimisation conjointe de l’allocationdes puissances et de la voie de retour. Nous proposons aussi des algorithmes d’adaptationde la modulation à faibles complexités basés sur la méthode On-Off . Nousdiscutons la manière dont le récepteur informe l’émetteur sur les modulations allouées. / X

The demand for sophisticated wireless applications capable of conveying information content represented in various forms such as voice, data, audio and video is ever increasing. In order to support such applications, either additional wireless spectrum is needed or advanced signal processing techniques must be employed by the next-generation wireless communication systems. An immediate observation that can be made regarding the first option is that radio frequency spectrum is a limited natural resource. Moreover, since existing spectrum allocation policies of several national regulatory agencies such as the Federal Communications Commission (FCC) restrict spectrum access to licensed entities only, it has been identified that most of the licensed spectrum across time and frequency is inefficiently utilized. To facilitate greater spectral efficiency, many national regulatory agencies are considering a paradigm shift towards spectrum allocation by allowing unlicensed users to temporarily borrow unused spectral resources. This concept is referred to a dynamic spectrum access (DSA). Although, several spectrum measurement campaigns have been reported in the published literature for quantitatively assessing the available vacant spectrum, there are certain aspects of spectrum utilization that need a deeper understanding. First, we examine two complementary approaches to the problem of characterizing the usage of licensed bands. In the first approach, a linear mixed-effects based regression model is proposed, where the variations in percentage spectrum occupancy and activity period of the licensed user are described as a function of certain independent regressor variables. The second approach is based on the creation of a geo-location database consisting of the licensed transmitters in a specific geographical region and identifying the coverage areas that affect the available secondary channels. Both of these approaches are based on the energy spectral density data-samples collected across numerous frequency bands in several locations in the United States. We then study the mutual interference effects in a coexistence scenario consisting of licensed and unclicensed users. We numerically evaluate the impact of interference as a function of certain receiver characteristics. Specifically, we consider the unlicensed user to utilize OFDM or NOFDM symbols since the appropriate subcarriers can be turned off to facilitate non- contiguous spectrum utilization. Finally, it has been demonstrated that multiple-input and multiple-output (MIMO) antennas yield significant throughput while requiring no increase in transmit power or required bandwidth. However, the separation of spectrally overlapping signals is a challenging task that involves the estimation of the channel. We provide results concerning channel and symbol estimation in the scenario described above. In particular, we focus on the MIMO-OFDM transmission scheme and derive capacity lower bounds due to imperfect channel estimation.

La détection radar passive met à profit des émetteurs non-coopératifs, déjà présents dans l’environnement, qui transmettent des signaux de télécommunications, de type DVB-T dans l’étude présentée.Elle utilise les réflexions de ces signaux sur de potentielles cibles et les exploite comme échos radar au niveau d’un récepteur aéroporté.Ces nouveaux systèmes de détection, par nature discrets et économes en énergie et en allocation de fréquences, étendent la surveillance à la basse altitude.Si les différentes étapes des traitements classiques utilisés en radar passif terrestre (estimation du signal de référence, réjection, filtrage adapté, détection)demandent d’être réorientées sérieusement pour répondre aux contraintes liées à la réception aéroportée,il en va de même du récepteur qui doit satisfaire les exigences matérielles de la plateforme aérienne.Dans ce but, un système expérimental embarqué sur motoplanneur a été développé permettant d’acquérir des signaux réels indispensablesà la compréhension de l’impact de la propagation des signaux DVB-T.La méthode d’estimation du signal de référence proposée permet d’une part, de lutter contre les fluctuations du canal de propagation induites par les multi-trajetsen exploitant la diversité d’antenne et d’autre part, de prendre en compte les variations temporelles en s’appuyant sur la méthode BEM (Basis Expension Model).Ensuite, une analyse théorique sur la répartition du fouillis de sol est apportée.L’exploitation des signaux expérimentaux permet de la valider par une analyse dans le plan distance-Doppler et angle-Doppler.Une projection cartésienne permet de mettre en évidence des échos forts confrontés avec la vérité terrain.L’estimation du signal de référence et la connaissance de l’étalement du fouillis de sol sont les piliers fondamentaux de la détection car ces composantes représentent deux contributions à rejeter.Pour le signal de référence, une méthode classique de réjection où les coefficients du filtre sont estimés au sens des moindres carrés est mise en oeuvre.Un filtrage spatial orthogonal à la direction d’arrivée du signal de référence est ajouté afin de diminuer l’impact du bruit émis.Le large étalement en Doppler et en distance nous a conduit à rejeter le fouillis sur des périodes de corrélation plus courtes.Les travaux présentés apportent une compréhension fine de l’impact de la propagation sur les traitements de détection en radar passif aéroporté et offrent des perspectives engageantesquant à la détection de cibles de moyennes à grandes Surfaces Equivalentes Radar. / Passive radar detection benefits from non-cooperative telecommunication broadcasters, already existing in the environment, such as DVB-T broadcasters.It uses signal reflections over potential targets. An airborne receiver takes advantage of it as radar echoes.This new kind a detection system is discrete, has low energy consumption, uses already allocated frequencies and broaden radar detection to low altitudes.Due to airborne constraints, the standard signal processing steps, as the receiving system need to be adjusted.Indeed a dedicated radar has been developped in order to get experimental signal, and therefore help deepen the understanding of propagation phenomenon.The proposed reference signal estimation allows to face channel multipath induced fluctuations on the one hand, and to take into account channel time variationsthanks to Basis Expansion Model (BEM) modeling. A theoretical analysis of the clutter spread is then drawn.Experimental results confirm the expectation in the range-Doppler and angle-Doppler domain.Besides a clutter cartesian projection highlights the major reflectors, that may be confronted to the terrain truth.Reference signal estimation and clutter spread constitute two radar detection pilars, as these components have to be cancelled.So as to reject direct path, space filtering orthogonal to the direct direction is also performed to suppress the impact of the transmitted noise.Then reference signal is cancelled via a standard rejection method based on least-square filter coefficients estimation.The large Doppler and range clutter spread, lead us to reject the reference signal over shorter correlation periods.The present work gives an accurate comprehension of propagation mechanisms impact on airborne passive radar signal processing andprovides a promising perspective regarding intermediate radar cross section target detection.

Over the past few years there has been an extensive growth in data traffic consumption devices. Billions of mobile data devices are connected to the global wireless network. Customers demand revived services and up-to-date developed applications, like real-time video and games. These applications require reliable and high data rate wireless communication with high throughput network. One way to meet these requirements is by increasing the number of transmit and/or receive antennas of the wireless communication systems. Massive multiple-input multiple-output (MIMO) has emerged as a promising candidate technology for the next generation (5G) wireless communication. Massive MIMO increases the spatial multiplexing gain and the data rate by adding an excessive number of antennas to the base station (BS) terminals of wireless communication systems. However, building efficient algorithms able to decode a coherently or non-coherently large flow of transmitted signal with low complexity is a big challenge in massive MIMO. In this dissertation, we propose novel approaches to achieve optimal performance for joint channel estimation and signal detection for massive MIMO systems. The dissertation consists of three parts depending on the number of users at the receiver side. In the first part, we introduce a probabilistic approach to solve the problem of coherent signal detection using the optimized Markov Chain Monte Carlo (MCMC) technique. Two factors contribute to the speed of finding the optimal solution by the MCMC detector: The probability of encountering the optimal solution when the Markov chain converges to the stationary distribution, and the mixing time of the MCMC detector. First, we compute the optimal value of the “temperature'' parameter such that the MC encounters the optimal solution in a polynomially small probability. Second, we study the mixing time of the underlying Markov chain of the proposed MCMC detector. We assume the channel state information is known in the first part of the dissertation; in the second part we consider non-coherent signal detection. We develop and design an optimal joint channel estimation and signal detection algorithms for massive (single-input multiple-output) SIMO wireless systems. We propose exact non-coherent data detection algorithms in the sense of generalized likelihood ratio test (GLRT). In addition to their optimality, these proposed tree based algorithms perform low expected complexity and for general modulus constellations. More specifically, despite the large number of the unknown channel coefficients for massive SIMO systems, we show that the expected computational complexity of these algorithms is linear in the number of receive antennas (N) and polynomial in channel coherence time (T). We prove that as $N \rightarrow \infty$, the number of tested hypotheses for each coherent block equals $T$ times the cardinality of the modulus constellation. Simulation results show that the optimal non-coherent data detection algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity. In the part three, we consider massive MIMO uplink wireless systems with time-division duplex (TDD) operation. We propose an optimal algorithm in terms of GLRT to solve the problem of joint channel estimation and data detection for massive MIMO systems. We show that the expected complexity of our algorithm grows polynomially in the channel coherence time (T). The proposed algorithm is novel in two terms: First, the transmitted signal can be chosen from any modulus constellation, constant and non-constant. Second, the algorithm decodes the received noisy signal, which is transmitted a from multiple-antenna array, offering exact solution with polynomial complexity in the coherent block interval. Simulation results demonstrate significant performance gains of our approach compared with suboptimal non-coherent detection schemes. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.