In this thesis, we design advanced interference management techniques for future wireless networks under the availability of perfect and imperfect channel state information (CSI). We do so by considering a generalized imperfect CSI model where the variance of the channel estimation error depends on the signal-to-noise ratio (SNR). First, we analyze the performance of standard linear precoders, namely channel inversion (CI) and regularized CI (RCI), in downlink of cellular networks by deriving the received signal-to-interference-plus-noise ratio (SINR) of each user subject to both perfect and imperfect CSI. In this case, novel bounds on the asymptotic performance of linear precoders are derived, which determine howmuch accurate CSI should be to achieve a certain quality of service (QoS). By relying on the knowledge of error variance in advance, we propose an adaptive RCI technique to further improve the performance of standard RCI subject to CSI mismatch. We further consider transmit-power efficient design of wireless cellular networks. We propose two novel linear precoding techniques which can notably decrease the deployed power at transmit side in order to secure the same average output SINR at each user compared to standard linear precoders like CI and RCI. We also address a more sophisticated interference scenario, i.e., wireless interference networks, wherein each of the K transmitters communicates with its corresponding receiver while causing interference to the others. The most representative interference management technique in this case is interference alignment (IA). Unlike standard techniques like time division multiple access (TDMA) and frequency division multiple access (FDMA) where the achievable degrees of freedom (DoF) is one, with IA, the achievable DoF scales up with the number of users. Therefore, in this thesis, we quantify the asymptotic performance of IA under a generalized CSI mismatch model by deriving novel bounds on asymptotic mean loss in sum rate and the achievable DoF. We also propose novel least squares (LS) and minimum mean square error (MMSE) based IA techniques which are able to outperform standard IA schemes under perfect and imperfect CSI. Furthermore, we consider the implementation of IA in coordinated networks which enable us to decrease the number of deployed antennas in order to secure the same achievable DoF compared to standard IA techniques.

This research concerns analysis of system capacity, development of adaptive transmission schemes with known channel state information at the transmitter (CSIT) and design of new signal detection and channel estimation schemes with low complexity in some multiple antenna systems. We first analyze the sum-rate capacity of the downlink of a cellular system with multiple transmit antennas and multiple receive antennas assuming perfect CSIT. We evaluate the ergodic sum-rate capacity and show how the sum-rate capacity increases as the number of users and the number of receive antennas increases. We develop upper and lower bounds on the sum-rate capacity and study various adaptive MIMO schemes to achieve, or approach, the sum-rate capacity. Next, we study the minimum outage probability transmission schemes in a multiple-input-single-output (MISO) flat fading channel assuming partial CSIT. Considering two special cases: the mean feedback and the covariance feedback, we derive the optimum spatial transmission directions and show that the associated optimum power allocation scheme, which minimizes the outage probability, is closely related to the target rate and the accuracy of the CSIT. Since CSIT is obtained at the cost of feedback bandwidth, we also consider optimal allocation of bandwidth between the data channel and the feedback channel in order to maximize the average throughput of the data channel in MISO, flat fading, frequency division duplex (FDD) systems. We show that beamforming based on feedback CSI can achieve an average rate larger than the capacity without CSIT under a wide range of mobility conditions. We next study a SAGE-aided List-BLAST detection scheme for MIMO systems which can achieve performance close to that of the maximum-likelihood detector with low complexity. Finally, we apply the EM and SAGE algorithms in channel estimation for OFDM systems with multiple transmit antennas and compare them with a recently proposed least-squares based estimation algorithm. The EM and SAGE algorithms partition the problem of estimating a multi-input channel into independent channel estimation for each transmit-receive antenna pair, therefore avoiding the matrix inversion encountered in the joint least-squares estimation.

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.

En els sistemes de comunicacions on el transmissor disposa de certa informació sobre l'estat del canal (CSI), es possible dissenyar esquemes lineals de precodificació que assignin la potència de manera òptima induint guanys considerables, sigui en termes de capacitat, sigui en termes de la fiabilitat de l'enllaç de comunicacions. A la pràctica, aquest coneixement del canal mai és perfecte i, per tant, el senyal transmès es veurà degradat degut al desajust entre la informació que el transmissor disposi del canal i el seu estat real.En aquest context, aquesta tesi estudia dos problemes diferents però alhora estretament relacionats: el disseny d'un esquema pràctic de seguiment del canal en transmissió per canals variants en temps, i el disseny d'esquemes lineals de precodificació que siguin robustos a la incertesa del canal. La primera part de la tesi proposa el disseny d'un esquema de seguiment de canal que, mitjançant un enllaç de retorn de baixa capacitat, proporcioni al transmissor una informació acurada sobre el seu estat. Històricament, aquest tipus d'esquemes han rebut fortes crítiques degut a la gran quantitat d'informació que és necessari transmetre des del receptor cap el transmissor. Aquesta tesi, doncs, posa especial èmfasi en el disseny d'aquest canal de retorn. La solució que es proposa, basada en el filtre de Kalman, utilitza un esquema que recorda al transmissor DPCM. Les variacions del canal són tractades mitjançant dos predictors lineals idèntics situats en el transmissor i en el receptor, i un canal de retorn que assisteix el transmissor amb l'error de predicció. L'interès d'aquest esquema diferencial és que permet seguir les variacions del canal amb només dos o quatre bits per coeficient complex, fins i tot en canals ràpidament variants.La resta de la tesi cobreix el segon objectiu, l'estudi de diferents esquemes d'assignació de potències quan el coneixement del canal en transmissió no és perfecte. El problema es planteja per a un sistema MIMO OFDM com a formulació més general, incloent els casos d'una sola antena, de l'esquema beamforming i del canal multiplicatiu com a casos particulars.Primerament s'ha plantejat l'optimització dels criteris de mínim error quadràtic mig (MMSE) i mínima BER sense codificar. La innovació en el treball presentat a la tesi, respecte a altres treballs que segueixen els mateixos criteris de disseny, ha estat la formulació Bayesiana del problema per al disseny dels algoritmes robustos.La tesi continua amb el plantejament d'estratègies robustes d'assignació de potència destinades a minimitzar la BER codificada. Per aquesta tasca s'han utilitzat criteris de teoria de la informació. Possiblement una de les principals contribucions d'aquesta tesi ha estat el plantejament del cut-off rate com a paràmetre de disseny. Aquest criteri s'introdueix com alternativa a la capacitat de canal o a la informació mutual per al disseny del transmissor quan s'inclou codificació de canal. La ultima part de la tesi proposa un interleaver adaptatiu de baixa complexitat que, utilitzant el coneixement del canal disponible en el transmissor, assigna estratègicament els bits no només per combatre les ràfegues d'errors, sinó també per lluitar contra els esvaïments que puguin presentar les diferents portadores del canal per a una realització concreta. El disseny d'aquest interleaver, anomenat "interleaver RCPC" està basat en els codis Rate-Compatible Punctured Convolutional Codes. Com s'il·lustra a partir del resultats numèrics, l'ús d'aquest interleaver millora les prestacions dels algoritmes quan es comparen amb les que s'obtindrien si s'utilitzes un interleaver de bloc o un interleaver pseudo-aleatori. / When the transmitter of a communication system disposes of some Channel State Information (CSI), it is possible to design linear precoders that optimally allocate the power inducing high gains either in terms of capacity or in terms of reliable communications. In practical scenarios, this channel knowledge is not perfect and thus the transmitted signal suffers from the mismatch between the CSI at the transmitter and the real channel.In that context, this thesis deals with two different, but related, topics: the design of a feasible transmitter channel tracker for time varying channels, and the design of optimal linear precoders robust to imperfect channel estimates.The first part of the thesis proposes the design of a channel tracker that provides an accurate CSI at the transmitter by means of a low capacity feedback link. Historically, those schemes have been criticized because of the large amount of information to be transmitted from the receiver to the transmitter. This thesis focuses, thus, the attention in an accurate design of the return link. The proposed solution is based on the Kalman filter and follows a scheme that reminds the well known DPCM transmitter. The channel variability is processed by two identical linear predictors located at the transmitter and at the receiver, and a feedback link that assists the transmitter with the prediction error. The interest of this differential scheme is that allows to track the channel variations with only two or four bits per complex channel coefficient even in fast time-varying channels.The rest of the thesis covers the second topic, studying different robust power allocation algorithms when the CSI is not perfectly known at the transmitter. For the sake of generality, the problem is formulated for the general MIMO OFDM case, encompassing the single antenna transmission, the beamforming schemes and the frequency-flat fading channels as particular cases. First, the minimum MSE and the minimum uncoded BER parameters are chosen to be optimized, evaluating the performance of the algorithms in terms of uncoded BER. The basic novelty with respect to previous works that considers the same strategies of design is the proposal of a Bayesian approach for the design of the robust algorithms.Next the study is extended by proposing robust power allocation strategies focused on the minimization of the coded BER. For this purpose, information-theoretic criteria are used. Probably, one of the main contributions in the thesis is the proposal of the cut-off rate as a parameter of design whose maximization is directly related to the coded BER. This criterion is introduced as an alternative to the channel capacity and the mutual information for the design of optimal transceivers in the presence of any channel coding stage. The last part of the thesis proposes a low complexity adaptive interleaver that, making use of the CSI available at the transmitter, reallocates the bits not only to combat the bursty channel errors but also to combat the specific distribution of the faded subcarriers as a function of the channel response. The design of this interleaver, named as "RCPC interleaver", is based on the Rate-Compatible Punctured Convolutional Codes. As shown by numerical results, the use of this interleaver improves the performance of the algorithms when they are compared with the classical block interleavers and pseudo-random interleavers.

This research concerns analysis of system capacity, development of adaptive transmission schemes with known channel state information at the transmitter (CSIT) and design of new signal detection and channel estimation schemes with low complexity in some multiple antenna systems. We first analyze the sum-rate capacity of the downlink of a cellular system with multiple transmit antennas and multiple receive antennas assuming perfect CSIT. We evaluate the ergodic sum-rate capacity and show how the sum-rate capacity increases as the number of users and the number of receive antennas increases. We develop upper and lower bounds on the sum-rate capacity and study various adaptive MIMO schemes to achieve, or approach, the sum-rate capacity. Next, we study the minimum outage probability transmission schemes in a multiple-input-single-output (MISO) flat fading channel assuming partial CSIT. Considering two special cases: the mean feedback and the covariance feedback, we derive the optimum spatial transmission directions and show that the associated optimum power allocation scheme, which minimizes the outage probability, is closely related to the target rate and the accuracy of the CSIT. Since CSIT is obtained at the cost of feedback bandwidth, we also consider optimal allocation of bandwidth between the data channel and the feedback channel in order to maximize the average throughput of the data channel in MISO, flat fading, frequency division duplex (FDD) systems. We show that beamforming based on feedback CSI can achieve an average rate larger than the capacity without CSIT under a wide range of mobility conditions. We next study a SAGE-aided List-BLAST detection scheme for MIMO systems which can achieve performance close to that of the maximum-likelihood detector with low complexity. Finally, we apply the EM and SAGE algorithms in channel estimation for OFDM systems with multiple transmit antennas and compare them with a recently proposed least-squares based estimation algorithm. The EM and SAGE algorithms partition the problem of estimating a multi-input channel into independent channel estimation for each transmit-receive antenna pair, therefore avoiding the matrix inversion encountered in the joint least-squares estimation.

Wireless systems in which multiple users simultaneously access the propagation medium suffer from co-channel interference. Untreated interference limits the total amount of data that can be communicated reliably across the wireless links. If interfering users allocate a portion of the system's resources for information exchange and coordination, the effect of interference can be mitigated. Interference alignment (IA) is an example of a cooperative signaling strategy that alleviates the problem of co-channel interference and promises large gains in spectral efficiency. To enable alignment in practical wireless systems, channel state information (CSI) must be shared both efficiently and accurately. In this dissertation, I develop low-overhead CSI feedback strategies that help networks realize the information-theoretic performance of IA and facilitate its adoption in practical systems. The developed strategies leverage the concepts of analog, digital, and differential feedback to provide IA networks with significantly more accurate and affordable CSI when compared to existing solutions. In my first contribution, I develop an analog feedback strategy to enable IA in multiple antenna systems; multiple antennas are one of IA's key enabling technologies and perhaps the most promising IA use case. In my second contribution, I leverage temporal correlation to improve CSI quantization in limited feedback single-antenna systems. The Grassmannian differential strategy developed provides several orders of magnitude in CSI compression and ensures almost-perfect IA performance in various fading scenarios. In my final contribution, I complete my practical treatment of IA by revisiting its performance when CSI acquisition overhead is explicitly accounted for. This last contribution settles the viability of IA, from a CSI acquisition perspective, and demonstrates the utility of the proposed feedback strategies in transitioning interference alignment from theory to practice. / text

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.

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / Em se tratando de comunicaÃÃes mÃveis, a troca de informaÃÃes sobre os estados do canal entre as antenas receptoras e transmissoras à uma importante ferramenta para a melhoria do desempenho do sistema. Assim, nesse trabalho foram analisados sistemas MIMO multiplexados espacialmente, Spatially Multiplexed MIMO (SM-MIMO), com informaÃÃes do estado do canal no transmissor, Channel State Information (CSI), limitadas e duas tÃcnicas de detecÃÃo linear do sinal e prÃ-equalizaÃÃo do sinal Zero Forcing (ZF) e Minimum Mean Square Error (MMSE). Para essa limitaÃÃo dois esquemas foram considerados: Quantization Codebook (QC) e Compressive Sensing (CS). Compressive Sensing à usado para gerar um CSI comprimido a ser enviado pelas antenas receptoras por um canal de feedback a fim de reduzir a quantidade de informaÃÃo enviada pelas mesmas. Portanto, nesse trabalho, o desempenho das duas tÃcnicas foram comparadas por simulaÃÃes computacionais das curvas da taxa de erro de bit, Bit Error Rate (BER), de acordo com a variaÃÃo da relaÃÃo sinal ruÃdo, Signal to Noise Ratio (SNR), considerando as duas abordagens QC e CS. AlÃm disso, a presenÃa do erro de quantizaÃÃo e do ruÃdo, no canal de feedback, tambÃm foi avaliada para o esquema de CS. / Concerning to mobile communications, the information exchange over the channel states between receiving antennas and transmiting antennas is an important tool to enhance the system performance. Thus, in this work, spatially multiplexed MIMO (SM-MIMO) systems with limited Channel State Information (CSI) were analyzed considering two techniques of linear signal detection and pre-equalization Zero Forcing (ZF) and Minimum Mean Square Error (MMSE). Due to this limitation two schemes were considered: Quantization Codebook (QC) e Compressive Sensing (CS). Compressive Sensing is used to generate a reduced CSI feedback to the transmitter in order to reduce feedback load into the system. Therefore, in this work, the performance of the techniques were compared by computational simulations of Bit Error Rate (BER) curves according to the variation of the Signal to Noise Ratio (SNR) for the two considered approaches QC and CS. Furthermore, the presence of quantization error and noise, in the feedback link, were also evaluated for the CS scheme.

In energy harvesting communications, the transmitters have to adapt transmission to the availability of energy harvested during communication. The performance of the transmission depends on the channel conditions which vary randomly due to mobility and environmental changes. During this work, we consider the problem of power allocation taking into account the energy arrivals over time and the quality of channel state information (CSI) available at the transmitter, in order to maximize the throughput. Differently from previous work, the CSI at the transmitter is not perfect and may include estimation errors. We solve this problem with respect to the energy harvesting constraints. Assuming a perfect knowledge of the CSI at the receiver, we determine the optimal power policy for different models of the energy arrival process (offline and online model). Indeed, we obtain the power allocation scheme when the transmitter has either perfect CSI or no CSI. We also investigate of utmost interest the case of fading channels with imperfect CSI. Moreover, a study of the asymptotic behavior of the communication system is proposed. Specifically, we analyze of the average throughput in a system where the average recharge rate goes asymptotically to zero and when it is very high.