With the rapid development of wireless communication technology, the demands for higher communication rates are increasing. Higher communication rate corresponds to higher DoF. Interference alignment, which is an emerging interference management technique, is able to substantially increase the DoF of wireless communication systems. This thesis mainly studies the delay-based interference alignment technique. The key problem lies in the design of the transmission scheme and the appropriate allocation of the propagation delay, so as to achieve the desired DoF of different wireless networks. In addition, through delay-based interference alignment, the achievability of extreme points of the DoF region of different wireless networks can be proved.

Η διερεύνηση της περιοχής χωρητικότητας και της περιοχής επιτεύξιμων ρυθμών μετάδοσης καναλιών αποτελεί βασικό αντικείμενο της Θεωρίας Πληροφορίας. Η Ευθυγράμμιση Παρεμβολών είναι μια καινούρια ιδέα που δίνει μια εναλλακτική οπτική στο αντικείμενο αυτό, μέσω της διαφορετικής λογικής που εισάγει σχετικά με την κωδικοποίηση και τη μετάδοση της πληροφορίας. Σε πρόσφατες δημοσιεύσεις έχουν προταθεί μοντέλα που επιτρέπουν την εφαρμογή της θεωρίας της Ευθυγράμμισης Παρεμβολών και τη χρήση της σε πρακτικά συστήματα επικοινωνιών και καταδεικνύουν την υπεροχή της σε σχέση με συμβατικές μεθόδους. Παράλληλα, παρόλο που προς το παρόν έχει δοθεί έμφαση στην εδραίωση της Ευθυγράμμισης Παρεμβολών στις επικοινωνίες, η μαθηματική της βάση καθιστά δυνατή την εφαρμογή της σε αντικείμενα που ανήκουν σε άλλους τομείς. Σκοπός της παρούσας εργασίας είναι η μελέτη και η εφαρμογή της Αναδρομικής Ευθυγράμμισης Παρεμβολών για μετάδοση πληροφορίας σε Συστήματα Επικοινωνιών, καθώς και η διερεύνηση της απόδοσης της μεθόδου σε πρακτικά συστήματα. / The study of the channel capacity region and the achievable rate region is one of the main topics of Information Theory. Interference Alignment is a new idea that provides new insights through the introduction of a different viewpoint on data encoding and transmission. In recent publications, models have been proposed that allow the application of the theory of Interference Alignment to practical communication systems and demonstrate its superiority compared to traditional approaches. Furthermore, although for the time being emphasis has been put on establishing the use of Interference Alignment to communication systems, its mathematical formulation makes possible its use to other areas. The purpose of this thesis is to study and to apply Retrospective Interference Alignment to data transmission in communication systems and to evaluate the performance of the method in practical systems.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Devido ao rÃpido crescimento e os agressivos requisitos de vazÃo nas atuais redes sem fio, como os sistemas celulares de 4 a GeraÃÃo, a interferÃncia se tornou um problema que nÃo pode mais ser negligenciado. Neste contexto, o Alinhamento de InterferÃncia (IA) tem surgido como uma tÃcnica promissora que possibilita transmissÃes livres de interferÃncia com elevada eficiÃncia espectral. No entanto, trabalhos recentes tÃm focado principalmente nos ganhos teÃricos que esta tÃcnica pode prover, enquanto esta dissertaÃÃo visa dar um passo na direÃÃo de esclarecer alguns dos problemas prÃticos de implementaÃÃo da tÃcnica em redes celulares, bem como comparÃ-la com outras tÃcnicas bem estabelecidas. Uma rede composta por trÃs cÃlulas foi escolhida como cenÃrio inicial de avaliaÃÃo, para o qual diversos fatores realistas foram considerados de modo a realizar diferentes anÃlises. A primeira anÃlise foi baseada em imperfeiÃÃes de canal, cujos resultados mostraram que o IA à mais robusto aos erros de estimaÃÃo de canal que o BD (do inglÃs, Block Diagonalization), enquanto as duas abordagens sÃo igualmente afetadas pela correlaÃÃo entre as antenas. O impacto de uma interferÃncia externa nÃo-coordenada, que foi modelada por diferentes matrizes de covariÃncia de modo a emular vÃrios cenÃrios, tambÃm foi avaliado. Os resultados mostraram que as modificaÃÃes feitas nos algoritmos de IA podem melhorar bastante seus desempenho, com uma vantagem para o algoritmo que suprime um Ãnico fluxo de dados, quando sÃo comparadas as taxas de erro de bit alcanÃadas por cada um. Para combinar os fatores das anÃlises anteriores, as variaÃÃes temporais de canal foram consideradas. Neste conjunto de simulaÃÃes, alÃm da presenÃa da interferÃncia externa, os prÃ-codificadores sÃo calculados atravÃs de medidas atrasadas de canal, levando a resultados que corroboraram com as anÃlises anteriores. Um fato recorrente percebido em todas as anÃlises anteriores à o dilema entre aplicar os algoritmos baseados em BD, para que se consiga alcanÃar maiores capacidades, ou enviar a informaÃÃo atravÃs de um enlace mais confiÃvel utilizando o IA. Uma maneira de esclarecer este dilema à efetivamente realizar simulaÃÃes a nÃvel sistÃmico, para isto foi aplicado um simulador sistÃmico composto por um grande nÃmero de setores. Como resultado, todas as anÃlises realizadas neste simulador mostraram que a tÃcnica de IA atinge desempenhos intermediÃrios entre a nÃo cooperaÃÃo e os algoritmos baseados na prÃ-codificaÃÃo conjunta. Uma das principais contribuiÃÃes deste trabalho foi mostrar alguns cenÃrios em que a tÃcnica do IA pode ser aplicada. Por exemplo, quando as estimaÃÃes dos canais nÃo sÃo tÃo confiÃveis à melhor aplicar o IA do que os esquemas baseados no processamento conjunto. TambÃm mostrou-se que as modificaÃÃes nos algoritmos de IA, que levam em consideraÃÃo a interferÃncia externa, podem melhorar consideravelmente o desempenho dos algoritmos. Finalmente, o IA se mostrou uma tÃcnica adequada para ser aplicada em cenÃrios em que a interferÃncia à alta e nÃo à possÃvel ter um alto grau de cooperaÃÃo entre os setores vizinhos. / Due to the rapid growth and the aggressive throughput requirements of current wireless networks, such as the 4th Generation (4G) cellular systems, the interference has become an issue that cannot be neglected anymore. In this context, the Interference Alignment (IA) arises as a promising technique that enables transmissions free of interference with high-spectral efficiency. However, while recent works have focused mainly on the theoretical gains that the technique could provide, this dissertation aims to go a step further and clarify some of the practical issues on the implementation of this technique in a cellular network, as well as compare it to other well-established techniques. As an initial evaluation scenario, a 3-cell network was considered, for which several realistic factors were taken into account in order to perform different analyses. The first analysis was based on channel imperfections, for which the results showed that IA is more robust than Block Diagonalization (BD) regarding the Channel State Information (CSI) errors, but both are similarly affected by the correlation among transmit antennas. The impact of uncoordinated interference was also evaluated, by modeling this interference with different covariance matrices in order to mimic several scenarios. The results showed that modifications on the IA algorithms can boost their performance, with an advantage to the approach that suppresses one stream, when the Bit Error Rate (BER) is compared. To combine both factors, the temporal channel variations were taken into account. At these set of simulations, besides the presence of an external interference, the precoders were calculated using a delayed CSI, leading to results that corroborate with the previous analyses. A recurring fact on the herein considered analyses was the dilemma of weather to apply the Joint Processing (JP)-based algorithms in order to achieve higher sum capacities or to send the information through a more reliable link by using IA. A reasonable step towards solving this dilemma is to actually perform the packet transmissions, which was accomplished by employing a system-level simulator composed by a large number of Transmission Points (TPs). As a result, all analyses conducted with this simulator showed that the IA technique can provide an intermediate performance between the non-cooperation and the full cooperation scheme. Concluding, one of the main contributions of this work has been to show some scenarios/cases where the IA technique can be applied. For instance, when the CSI is not reliable it can be better to use IA than a JP-based scheme. Also, the modifications on the algorithms to take into account the external interference can boost their performance. Finally, the IA technique finds itself in-between the conventional transmissions and Coordinated Multi-Point (CoMP). IA achieves an intermediate performance, while requiring a certain degree of cooperation among the neighboring sectors, but demanding less infrastructure than the JP-based schemes.

The scarce and overpopulated radio spectrum is going to present a major barrier to the growth and development of future wireless networks. As such, spectrum sharing seems to be inevitable to accommodate the exploding demand for high data rate applications. A major challenge to realizing the potential advantages of spectrum sharing is interference management. This thesis deals with interference management techniques in noncooperative networks. In specific, interference alignment is used as a powerful technique for interference management. We use the degrees of freedom (DoF) as the figure of merit to evaluate the performance improvement due to the interference management schemes. This dissertation is organized in two parts. In the first part, we consider the K-user multiple input multiple output (MIMO) Gaussian interference channel (IC) with M antennas at each transmitter and N antennas at each receiver. This channel models the interaction between K transmitter-receiver pairs sharing the same spectrum for data communication. It is assumed that the channel coefficients are constant and are available at all nodes prior to data transmission. A new cooperative upper-bound on the DoF of this channel is developed which outperforms the known bounds. Also, a new achievable transmission scheme is provided based on the idea of interference alignment. It is shown that the achievable DoF meets the upper-bound when the number of users is greater than a certain threshold, and thus it reveals the channel DoF. In the second part, we consider communication over MIMO interference and X channels in a fast fading environment. It is assumed that the transmitters obtain the channel state information (CSI) after a finite delay which is greater than the coherence time of the channel. In other words, the CSI at the transmitters becomes outdated prior to being exploited for the current transmission. New transmission schemes are proposed which exploit the knowledge of the past CSI at the transmitters to retrospectively align interference in the subsequent channel uses. The proposed transmission schemes offer DoF gain compared to having no CSI at transmitters. The achievable DoF results are the best known results for these channels. Simple cooperative upper-bounds are developed to prove the tightness of our achievable results for some network configurations.

Recently, interference alignment (IA) has emerged as a promising technique to effectively mitigate interference in wireless communication systems. It has also evolved as a powerful technique to achieve the optimal degrees of freedom of interference channel. IA can be constructed in many domains such as space, time, frequency and codes. Currently, most researches on developing IA assume that channel state information (CSI) is well-known at the transceiver. However, in practice, perfect CSI at the transceiver can¡¦t be obtained due to many factors such as channel estimation error, quantization error, and feedback error. Under our investigation, the performance of IA is very sensitive to imperfect CSI. Therefore, this thesis proposes a spatial domain IA scheme for the three-user multiple-input multiple-output (MIMO) downlink interference channels, and analyzes the effect of channel estimation errors by modeling the estimation error as independent complex Gaussian random variables. The approximated bit error rate (BER) for the system with MIMO Zero-Forcing equalizer using IA is derived.

Global mobile data traffic has more than doubled in the past four years, and will only increase throughout the upcoming years. Modern cellular systems are striving to enable communications at high data rates over wide geographical areas to meet the surge in data demand. This requires advanced technologies to mitigate fundamental effects of wireless communications like path-loss, shadowing, small-scale fading, and interference. Two of such technologies are: i) deploying multiple antennas at the transmitter and receiver, and ii) employing an extra radio, called the relay, to forward messages from the transmitter to the receiver. The advantages of both technologies can be leveraged by using multiple antennas at the relay, transmitter, and receiver. Multiple-antenna relay-assisted communication is emerging as one promising technique for expanding the overall capacity of cellular networks. Taking full advantage of multiple-antenna relay-assisted cellular systems requires transmission strategies for jointly configuring the transmitters and receivers based on knowledge of the wireless propagation medium. This dissertation proposes such transmission strategies for wireless multiple-antenna relay-assisted systems. Two popular types of relays are considered: i) amplify-and-forward relays (the relays simply apply linear signal processing to their observed signals before retransmitting) and ii) decode-and-forward relays (the relays decode their observed signals and then re-encode before retransmitting). The first part of this dissertation considers the three-node multiple-antenna amplify-and-forward relay channel. Algorithms for adaptively selecting the number of data streams and subsets of transmit antennas at the transmitter and relay to provide reliable transmission at a guaranteed rate are proposed. Expressions for extracting spatial characteristics of the end-to-end multiple-antenna relay channel are derived. The second part of the dissertation presents interference management strategies that are developed specifically for two models of multiple-antenna relay interference channels where a number of relays assist multiple transmitters to communicate with multiple receivers. One model uses amplify-and-forward relays while the other uses decode-and-forward relays. Based on the idea of interference alignment, these strategies aim at maximizing the sum of achievable end-to-end rates. Simulation results show that the proposed transmission strategies with multiple-antenna relays achieve higher capacity and reliability than both those without relays and those with single-antenna relays. / text

A majority of the data transmitted and processed today has an inherent structured high-dimensional nature, either because of the process of encoding using high-dimensional codebooks for providing a systematic structure, or dependency of the data on a large number of agents or variables. As a result, many problem setups associated with transmission and processing of data have a structured high-dimensional aspect to them. This dissertation takes a look at two such problems, namely, communication over networks using network coding, and learning the structure of graphical representations like Markov networks using observed data, from an information-theoretic perspective. Such an approach yields intuition about good coding architectures as well as the limitations imposed by the high-dimensional framework. Th e dissertation studies the problem of network coding for networks having multiple transmission sessions, i.e., multiple users communicating with each other at the same time. The connection between such networks and the information-theoretic interference channel is examined, and the concept of interference alignment, derived from interference channel literature, is coupled with linear network coding to develop novel coding schemes off ering good guarantees on achievable throughput. In particular, two setups are analyzed – the first where each user requires data from only one user (multiple unicasts), and the second where each user requires data from potentially multiple users (multiple multicasts). It is demonstrated that one can achieve a rate equalling a signi ficant fraction of the maximal rate for each transmission session, provided certain constraints on the network topology are satisfi ed. Th e dissertation also analyzes the problem of learning the structure of Markov networks from observed samples – the learning problem is interpreted as a channel coding problem and its achievability and converse aspects are examined. A rate-distortion theoretic approach is taken for the converse aspect, and information-theoretic lower bounds on the number of samples, required for any algorithm to learn the Markov graph up to a pre-speci fied edit distance, are derived for ensembles of discrete and Gaussian Markov networks based on degree-bounded graphs. The problem of accurately learning the structure of discrete Markov networks, based on power-law graphs generated from the con figuration model, is also studied. The eff ect of power-law exponent value on the hardness of the learning problem is deduced from the converse aspect – it is shown that discrete Markov networks on power-law graphs with smaller exponent values require more number of samples to ensure accurate recovery of their underlying graphs for any learning algorithm. For the achievability aspect, an effi cient learning algorithm is designed for accurately reconstructing the structure of Ising model based on power-law graphs from the con figuration model; it is demonstrated that optimal number of samples su ffices for recovering the exact graph under certain constraints on the Ising model potential values. / text

Wireless interference networks refer to communication systems in which multiple source–destination pairs share the same transmission medium, and each source’s transmission interferes with the reception at non-intended destinations. Optimizing the transmission of each source–destination pair is interrelated with that of the other pairs, and characterizing the performance limits of these networks is a challenging task. Solving the problem of managing the interference and data communications for these networks would potentially make it possible to apply solutions to several existing and emerging communication systems. Wireless devices can carefully coordinate the use of scarce radio resources in order to deal effectively with interference and establish successful communications. In order to enable coordinated transmission, terminals must usually have a certain level of knowledge about the propagation environment; that is, channel state information (CSI). In practice, however, no CSI is a priori available at terminals (transmitters and receivers), and proper channel training mechanisms (such as pilot-based channel training and channel state feedback) should be employed to acquire CSI. This requires each terminal to share available radio resources between channel training and data transmissions. Allocating more resources for channel training leads to an accurate CSI estimation, and consequently, a precise coordination. However, it leaves fewer resources for data transmissions. This creates the need to investigate optimum resource allocation. This thesis investigates an information-theoretic approach towards the performance analysis of interference networks, and employs signal processing techniques to design transmission schemes for achieving these limits in the following scenarios. First, the smallest interference network with two single-input single-output (SISO) source–destination pairs is considered. A fixed-rate transmission is desired between each source–destination pair. Transmission schemes based on point-to-point codes are developed. The transmissions may not always attain successful communication, which means that outage events may be declared. The outage probability is quantified and the ε-outage achievable rate region is characterized. Next, a multi-user SISO interference network is studied. A pilot-assisted ergodic interference alignment (PAEIA) scheme is proposed to conduct channel training, channel state feedback, and data communications. The performance limits are evaluated, and optimum radio resource allocation problems are investigated. The analysis is extended to multi-cell wireless interference networks. A low-complexity pilot-assisted opportunistic user scheduling (PAOUS) scheme is proposed. The proposed scheme includes channel training, one-bit feedback transmission, user scheduling and data transmissions. The achievable rate region is computed, and the optimum number of cells that should be active simultaneously is determined. A multi-user MIMO interference network is also studied. Here, each source sends multiple data streams; specifically, the same number as the degrees of freedom of the network. Distributed transceiver design and power control algorithms are proposed that only require local CSI at terminals. / <p>QC 20141201</p>

The scarce and overpopulated radio spectrum is going to present a major barrier to the growth and development of future wireless networks. As such, spectrum sharing seems to be inevitable to accommodate the exploding demand for high data rate applications. A major challenge to realizing the potential advantages of spectrum sharing is interference management. This thesis deals with interference management techniques in noncooperative networks. In specific, interference alignment is used as a powerful technique for interference management. We use the degrees of freedom (DoF) as the figure of merit to evaluate the performance improvement due to the interference management schemes. This dissertation is organized in two parts. In the first part, we consider the K-user multiple input multiple output (MIMO) Gaussian interference channel (IC) with M antennas at each transmitter and N antennas at each receiver. This channel models the interaction between K transmitter-receiver pairs sharing the same spectrum for data communication. It is assumed that the channel coefficients are constant and are available at all nodes prior to data transmission. A new cooperative upper-bound on the DoF of this channel is developed which outperforms the known bounds. Also, a new achievable transmission scheme is provided based on the idea of interference alignment. It is shown that the achievable DoF meets the upper-bound when the number of users is greater than a certain threshold, and thus it reveals the channel DoF. In the second part, we consider communication over MIMO interference and X channels in a fast fading environment. It is assumed that the transmitters obtain the channel state information (CSI) after a finite delay which is greater than the coherence time of the channel. In other words, the CSI at the transmitters becomes outdated prior to being exploited for the current transmission. New transmission schemes are proposed which exploit the knowledge of the past CSI at the transmitters to retrospectively align interference in the subsequent channel uses. The proposed transmission schemes offer DoF gain compared to having no CSI at transmitters. The achievable DoF results are the best known results for these channels. Simple cooperative upper-bounds are developed to prove the tightness of our achievable results for some network configurations.

The demand for mobile wireless network resources is constantly on the rise, pushing for new communication technologies that are able to support unprecedented rates. In this thesis we address the issue by considering advanced interference management techniques to exploit the available resources more efficiently under relaxed channel state information (CSI) assumptions. While the initial studies focus on current half-duplex (HD) technology, we then move on to full-duplex (FD) communication due to its inherent potential to improve spectral efficiency. Work in this thesis is divided into four main parts as follows. In the first part, we focus on the two-cell two-user-per-cell interference broadcast channel (IBC) and consider the use of topological interference management (TIM) to manage inter-cell interference in an alternating connectivity scenario. Within this context we derive novel outer bounds on the achievable degrees of freedom (DoF) for different system configurations, namely, single-input single-output (SISO), multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) systems. Additionally, we propose new transmission schemes based on joint coding across states that exploit global topological information at the transmitter to increase achievable DoF. Results show that when a single state has a probability of occurrence equal to one, the derived bounds are tight with up to a twofold increase in achievable DoF for the best case scenario. Additionally, when all alternating connectivity states are equiprobable: the SISO system gains 11/16 DoF, achieving 96:4% of the derived outer bound; while the MISO/MIMO scenario has a gain of 1/2 DoF, achieving the outer bound itself. In the second part, we consider a general G-cell K-user-per-cell MIMO IBC and analyse the performance of linear interference alignment (IA) under imperfect CSI. Having imperfect channel knowledge impacts the effectiveness of the IA beamformers, and leads to a significant amount of residual leakage interference. Understanding the extent of this impact is a fundamental step towards obtaining a performance characterisation that is more relevant to practical scenarios. The CSI error model used is highly versatile, allowing the error to be treated either as a function of the signal-to-noise ratio (SNR) or as independent of it. Based on this error model, we derive a novel upper bound on the asymptotic mean sum rate loss and quantify the DoF loss due to imperfect CSI. Furthermore, we propose a new version of the maximum signal-to-interference plus noise ratio (Max-SINR) algorithm which takes into account statistical knowledge of the CSI error in order to improve performance over the naive counterpart in the presence of CSI mismatch. In the third part, we shift our attention to FD systems and consider weighted sum rate (WSR) maximisation for multi-user multi-cell networks where FD base-stations (BSs) communicate with HD downlink (DL) and uplink (UL) users. Since WSR problems are non-convex we transform them into weighted minimum mean squared error (WMMSE) ones that are proven to converge. Our analysis is first carried out for perfect CSI and then expanded to cater for imperfect CSI under two types of error models, namely, a norm-bounded error model and a stochastic error model. Additionally, we propose an algorithm that maximises the total DL rate subject to each UL user achieving a desired target rate. Results show that the use of FD BSs provides significant gains in achievable rate over the use of HD BSs, with a gain of 1:92 for the best case scenario under perfect CSI. They also demonstrate the robust performance of the imperfect CSI designs, and confirm that FD outperforms HD even under CSI mismatch conditions. Finally, the fourth part considers the use of linear IA to manage interference in a multi-user multi-cell network with FD BSs and HD users under imperfect CSI. The number of interference links present in such a system is considerably greater than that present in the HD network counterpart; thus, understanding the impact of residual leakage interference on performance is even more important for FD enabled networks. Using the same generalised CSI error model from the second part, we study the performance of IA by characterising the sum rate and DoF losses incurred due to imperfect CSI. Additionally, we propose two novel IA algorithms applicable to this network; the first one is based on minimising the mean squared error (MMSE), while the second is based on Max-SINR. The proposed algorithms exploit statistical knowledge of the CSI error variance in order to improve performance. Moreover, they are shown to be equivalent under certain conditions, even though the MMSE based one has lower computational complexity. Furthermore for the multi-cell case, we also derive the proper condition for IA feasibility.