No description available.

No description available.

No description available.

<h2 id="x-x-x-bp_categories-h"> </h2> / <p>The discrete-time Poisson (DTP) channel models a wide range of optical communication channels. The channel capacity and capacity-achieving distributions are generally unknown. This thesis addresses system design of DTP channels and presents novel contributions to the capacity of DTP channel, properties and closed-form expression of the capacity-achieving distribution under peak and average constraints, signalling design, and sum-capacity-achieving distributions of DTP multiple access channel (MAC) with peak amplitude constraints.</p> <p>Two algorithms are developed to compute the channel capacity of DTP channel as well as the capacity-achieving distribution with average and peak amplitude constraints. Tight lower bounds based on input distributions with simple forms are presented. Non-uniform signalling algorithms to achieve the channel capacity are also demonstrated. Fundamental properties of capacity-achieving distributions for DTP channels are established. Furthermore, necessary and sufficient conditions on the optimality of binary distributions are presented. Analytical expressions for the capacity-achieving distributions of the DTP channel are derived when there is no dark current and when the dark current is large enough. A two-user DTP multiple access channel model is proposed and it is shown that the sum-capacity-achieving distributions under peak amplitude constraints are discrete with a finite number of mass points.</p> / Doctor of Philosophy (PhD)

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

Il est attendu que les radios flexibles constituent un tournant technologique majeur dans le domaine des communications sans fil. Le point de vue adopté en radios flexibles est de considérer les canaux de communication comme un ensemble de ressources qui peuvent être accédées sur demande par un réseau primaire sous licence ou de manière opportuniste par un réseau secondaire à plus faible priorité. Du point de vue de la couche physique, le réseau primaire n’a aucune information sur l’existence de réseaux secondaires, de sorte que ces derniers doivent explorer l’environnement aérien de manière autonome à la recherche d’opportunités spectrales et exploiter ces ressources de manière optimale. Les phases d’exploration et d’exploitation, qui impliquent la gestion de nombreux agents, doivent être très fiables, rapides et efficaces. L’objectif de cette thèse est de modéliser, d’analyser et de proposer des solutions efficaces et quasi optimales pour ces dernières opérations.En ce qui concerne la phase d’exploration, nous calculons le test optimal de Neyman-Pearson de détection de plusieurs sources primaires via un réseau de capteurs. Cette procédure permet à un réseau secondaire d’établir la présence de ressources spectrales disponibles. La complexité calculatoire de l’approche optimale appelle cependant la mise en place de méthodes moins onéreuses, que nous rappelons et discutons. Nous étendons alors le test de détection en l’estimation aveugle de la position de sources multiples, qui permet l’acquisition d’informations détaillées sur les ressources spectrales disponibles.Le second volet de cette thèse est consacré à la phase d’exploitation optimale des ressources au niveau du réseau secondaire. Pour ce faire, nous obtenons une approximation fine du débit ergodique d’un canal multi-antennes à accès multiples et proposons des solutions peu coûteuses en termes de feedback afin que les réseaux secondaires s’adaptent rapidement aux évolutions rapides du réseau primaire. / Future cognitive radio networks are expected to come as a disruptive technological advance in the currently saturated field of wireless communications. The idea behind cognitive radios is to think of the wireless channels as a pool of communication resources, which can be accessed on-demand by a primary licensed network or opportunistically preempted (or overlaid) by a secondary network with lower access priority. From a physical layer point of view, the primary network is ideally oblivious of the existence of a co-localized secondary networks. The latter are therefore required to autonomously explore the air in search for resource left-overs, and then to optimally exploit the available resource. The exploration and exploitation procedures, which involve multiple interacting agents, are requested to be highly reliable, fast and efficient. The objective of the thesis is to model, analyse and propose computationally efficient and close-to-optimal solutions to the above operations.Regarding the exploration phase, we first resort to the maximum entropy principle to derive communication models with many unknowns, from which we derive the optimal multi-source multi-sensor Neyman-Pearson signal sensing procedure. The latter allows for a secondary network to detect the presence of spectral left-overs. The computational complexity of the optimal approach however calls for simpler techniques, which are recollected and discussed. We then proceed to the extension of the signal sensing approach to the more advanced blind user localization, which provides further valuable information to overlay occupied spectral resources.The second part of the thesis is dedicaded to the exploitation phase, that is, the optimal sharing of available resources. To this end, we derive an (asymptotically accurate) approximated expression for the uplink ergodic sum rate of a multi-antenna multiple-access channel and propose solutions for cognitive radios to adapt rapidly to the evolution of the primary network at a minimum feedback cost for the secondary networks.

We consider the transmission of correlated sources over a multiple access channel(MAC). Multiple access channels are important building blocks in many practical communication systems, e.g., local area networks(LAN), cellular systems, wireless multi-hop networks. Thus this topic has been studied for last several decades. One recent motivation is estimating a random field via wireless sensor networks. Often the sensor nodes are densely deployed resulting in correlated observations. These sensor nodes need to transmit their correlated observations to a fusion center which uses this data to estimate the sensed random field. Sensor nodes have limited computational and storage capabilities and very limited energy. Since transmission is very energy intensive, it is important to minimize it. This motivates our problem of energy efficient transmission of correlated sources over a sensor network. Sensor networks are often arranged in a hierarchical fashion. Neighboring nodes can first transmit their data to a cluster head which can further compress information before transmission to the fusion center. The transmission of data from sensor nodes to their cluster-head is usually through a MAC. At the fusion center the underlying physical process is estimated. The main trade-off possible is between the rates at which the sensors send their observations and the distortion incurred in estimation at the fusion center. The availability of side information at the encoders and/or the decoder can reduce the rate of transmission. In this thesis, the above scenario is modeled as an information theoretic problem. Efficient joint source-channel codes are discussed under various assumptions on side information and distortion criteria. Sufficient conditions for transmission of discrete/continuous alphabet sources with a given distortion over a discrete/continuous alphabet MAC are given. We recover various previous results as special cases from our results. Furthermore, we study the practically important case of the Gaussian MAC(GMAC) in detail and propose new joint source-channel coding schemes for discrete and continuous sources. Optimal schemes are identified in different scenarios. The protocols like TDMA, FDMA and CDMA are widely used across systems and standards. When these protocols are used the MAC becomes a system of orthogonal channels. Our general conditions can be specialized to obtain sufficient conditions for lossy transmission over this system. Using this conditions, we identify an optimal scheme for transmission of Gaussian sources over orthogonal Gaussian channels and show that the Amplify and Forward(AF) scheme performs close to the optimal scheme even at high SNR. Next we investigate transmission of correlated sources over a fast fading MAC with perfect or partial channel state information available at both the encoders and the decoder. We provide sufficient conditions for transmission with given distortions. We also provide power allocation policies for efficient transmission. Next, we use MAC with side information as a building block of a hierarchical sensor network. For Gaussian sources over Gaussian MACs, we show that AF performs well in such sensor network scenarios where the battery power is at a premium. We then extend this result to the hierarchical network scenario and show that it can perform favourably to the Slepian-Wolf based source coding and independent channel coding scheme. In a hierarchical sensor network the cluster heads often need to send only a function of the sensor observations to the fusion center. In such a setup the sensor nodes can compress the data sent to the cluster head exploiting the correlation in the data and also the structure of the function to be computed at the cluster head. Depending upon the function, exploiting the structure of the function can substantially reduce the data rate for transmission. We provide efficient joint source-channel codes for transmitting a general class of functions of the sources over the MAC.

Abstract In the current LTE-Advanced system, decode-and-forward (DF) is leveraged for cooperative relaying, where the erroneously decoded sequences are discarded at the relay, resulting in a waste of resources. The reason lies in that the erroneously decoded sequence can provide a certain amount of useful information about the source at the destination. Therefore, we develop a new relaying scheme, called lossy DF (also known as lossy forward (LF)), where the relay always forwards the decoded sequence to the destination. Beneficial from the always-forward principle, it has been verified that LF relaying outperforms DF relaying in terms of outage probability, &#949;-outage achievable rate, frame error rate (FER), and communication coverage. Three exemplifying network scenarios are studied in this thesis: the one-way multiple-input multiple-output (MIMO) relay network, the multiple access relay channel (MARC), and the general multi-source multi-relay network. We derive the outage probability of the one-way MIMO relay networks under the assumption that the orthogonal space-time block code (OSTBC) is implemented at the transmitter side for each individual transmission. Interestingly, we find that the diversity order of the OSTBC based one-way MIMO relay network can be interpreted and formulated by the well-known max-flow min-cut theorem, which is widely utilized to calculate the network capacity. For the MARC, non-orthogonal transmission is introduced to further improve the network throughput compared to its orthogonal counterpart. The region for lossless recovery of both sources is formulated by the theorem of multiple access channel (MAC) with a helper, which combines the Slepian-Wolf rate region and the MAC capacity region. Since the region for lossless recovery is obtained via sufficient condition, the derived outage probability can be regarded as a theoretical upper bound. We also conduct the performance evaluation by exploiting different accumulator (ACC) aided turbo codes at the transmitter side, exclusive or (XOR) based multi-user complete decoding at the relay, and iterative joint decoding (JD) at the destination. For the general multi-source multi-relay network, we focus on the investigation the end-to-end outage probability. The performance improvement of LF over DF is verified through theoretical analyses and numerical results in terms of outage probability. / Tiivistelmä Tämän päivän LTE-A-tiedonsiirtojärjestelmissä hyödynnetään dekoodaa-ja-välitä (decode-and-forward, DF) menetelmää yhteistoiminnalliseen tiedon edelleenlähetykseen (relaying) siten, että virheellisesti vastaanotetut sekvenssit hylätään välittimessä (relay). Tämä on resurssien hukkaamista, sillä virheellisissäkin viesteissä on informaatiota, jota voidaan hyödyntää vastaanottimessa. Tässä väitöskirjassa tutkitaan uutta häviöllistä DF-menetelmää, johon viitataan nimellä häviöllinen välitys (lossy forward, LF). Menetelmässä välitin lähettää informaation aina eteenpäin olipa siinä virheitä tai ei. Sen etuna verrattuna perinteiseen DF-menetelmään, on parantunut luotettavuus metriikoilla jossa mitataan vastaanoton todennäköisyyttä ja verkon peittoaluetta. Väitöskirjassa tarkastellaan LF-menetelmää kolmessa eri verkkotopologiassa jotka ovat yksisuuntainen monitulo-monilähtövälitinverkko (multiple-input multiple-output, MIMO), moniliityntävälitinkanava (multiple access relay channel, MARC), sekä yleinen moniläheinen monivälitinverkko. Työssä johdetaan matemaattinen esitys estotilan todennäköisyydelle (outage probability) yksisuuntaisessa MIMO-välitinverkossa olettaen ortogonaalisen tila-aika lohkokoodin (orthogonal space-time block code, OSTBC) käyttö. Estotilan todennäköisyys esitetään käyttäen toisteastta (diversity order), joka saadaan johdettua tunnetusta max-flow min-cut lauseesta, jota puolestaan käytetään yleisesti erilaisten verkkojen kapasiteettien laskentaan. MARC-topologiassa hyödynnetään ei-ortogonaalista lähetystä verkon datavirran kasvattamiseen. Häviöttömän lähetyksen informaatioteoreettinen kapasiteettialue saadaan johdettua MAC-auttajan kanssa. Lähestymistavassa Slepian-Wolf- sekä MAC-kapasiteettialueet yhdistyvät. Alueelle, jossa kahden lähteen lähetysnopeudet ovat sellaiset, että vastaanotto on häviötöntä, annetaan riittävä ehto, jolloin johdettu estotilan todennäköisyys on teoreettinen yläraja. Suorituskykyä evaluoidaan myös tietokonesimulaatioilla, joissa käytetään erilaisia akkumulaattoriavusteisia turbokoodeja lähettimessä, ehdoton tai (exclusive or, XOR) pohjaista monen käyttäjän dekoodausta välittimessä sekä iteratiivista yhteisdekoodausta vastaanottimessa. Yleisessä monilähteisessä monivälitinverkossa keskitytään alkuperäisen lähetyksen estotilatodennäköisyyteen. Teoreettinen analyysi sekä simulaatiot osoittavat, että LF:n estotilan todennäköisyys on pienempi kuin DF:n.

In this thesis, we investigate physical-layer network coding in an L × M × K relay network, where L source nodes want to transmit messages to K sink nodes via M relay nodes. We focus on the information processing at the relay nodes and the compute-and-forward framework. Nested lattice codes are used, which have the property that every linear combination of codewords is a valid codeword. This property is essential for physical-layer network coding. Because the actual network coding occurs on the physical layer, the network coding coefficients are determined by the channel realizations. Finding the optimal network coding coefficients for given channel realizations is a non-trivial optimization problem. In this thesis, we provide an algorithm to find network coding coefficients that result in the highest data rate at a chosen relay. The solution of this optimization problem is only locally optimal, i.e., it is optimal for a particular relay. If we consider a multi-hop network, each potential receiver must get enough linear independent combinations to be able to decode the individual messages. If this is not the case, outage occurs, which results in data loss. In this thesis, we propose a new strategy for choosing the network coding coefficients locally at the relays without solving the optimization problem globally. We thereby reduce the solution space for the relays such that linear independence between their decoded linear combinations is guaranteed. Further, we discuss the influence of spatial correlation on the optimization problem. Having solved the optimization problem, we combine physical-layer network coding with physical-layer secrecy. This allows us to propose a coding scheme to exploit untrusted relays in multi-user relay networks. We show that physical-layer network coding, especially compute-and-forward, is a key technology for simultaneous and secure communication of several users over an untrusted relay. First, we derive the achievable secrecy rate for the two-way relay channel. Then, we enhance this scenario to a multi-way relay channel with multiple antennas. We describe our implementation of the compute-and-forward framework with software-defined radio and demonstrate the practical feasibility. We show that it is possible to use the framework in real-life scenarios and demonstrate a transmission from two users to a relay. We gain valuable insights into a real transmission using the compute-and-forward framework. We discuss possible improvements of the current implementation and point out further work. / In dieser Arbeit untersuchen wir Netzwerkcodierung auf der Übertragungsschicht in einem Relay-Netzwerk, in dem L Quellen-Knoten Nachrichten zu K Senken-Knoten über M Relay-Knoten senden wollen. Der Fokus dieser Arbeit liegt auf der Informationsverarbeitung an den Relay-Knoten und dem Compute-and-Forward Framework. Es werden Nested Lattice Codes eingesetzt, welche die Eigenschaft besitzen, dass jede Linearkombination zweier Codewörter wieder ein gültiges Codewort ergibt. Dies ist eine Eigenschaft, die für die Netzwerkcodierung von entscheidender Bedeutung ist. Da die eigentliche Netzwerkcodierung auf der Übertragungsschicht stattfindet, werden die Netzwerkcodierungskoeffizienten von den Kanalrealisierungen bestimmt. Das Finden der optimalen Koeffizienten für gegebene Kanalrealisierungen ist ein nicht-triviales Optimierungsproblem. Wir schlagen in dieser Arbeit einen Algorithmus vor, welcher Netzwerkcodierungskoeffizienten findet, die in der höchsten Übertragungsrate an einem gewählten Relay resultieren. Die Lösung dieses Optimierungsproblems ist zunächst nur lokal, d. h. für dieses Relay, optimal. An jedem potentiellen Empfänger müssen ausreichend unabhängige Linearkombinationen vorhanden sein, um die einzelnen Nachrichten decodieren zu können. Ist dies nicht der Fall, kommt es zu Datenverlusten. Um dieses Problem zu umgehen, ohne dabei das Optimierungsproblem global lösen zu müssen, schlagen wir eine neue Strategie vor, welche den Lösungsraum an einem Relay soweit einschränkt, dass lineare Unabhängigkeit zwischen den decodierten Linearkombinationen an den Relays garantiert ist. Außerdem diskutieren wir den Einfluss von räumlicher Korrelation auf das Optimierungsproblem. Wir kombinieren die Netzwerkcodierung mit dem Konzept von Sicherheit auf der Übertragungsschicht, um ein Übertragungsschema zu entwickeln, welches es ermöglicht, mit Hilfe nicht-vertrauenswürdiger Relays zu kommunizieren. Wir zeigen, dass Compute-and-Forward ein wesentlicher Baustein ist, um solch eine sichere und simultane Übertragung mehrerer Nutzer zu gewährleisten. Wir starten mit dem einfachen Fall eines Relay-Kanals mit zwei Nutzern und erweitern dieses Szenario auf einen Relay-Kanal mit mehreren Nutzern und mehreren Antennen. Die Arbeit wird abgerundet, indem wir eine Implementierung des Compute-and-Forward Frameworks mit Software-Defined Radio demonstrieren. Wir zeigen am Beispiel von zwei Nutzern und einem Relay, dass sich das Framework eignet, um in realen Szenarien eingesetzt zu werden. Wir diskutieren mögliche Verbesserungen und zeigen Richtungen für weitere Forschungsarbeit auf.

No description available.