[pt] Modulação Espacial com Pré-codificação Generalizada (Generalized Precoding Aided Spatial Modulation, GPSM) é uma estratégia de comunicação em sistemas MIMO em que o transmissor codifica a informação em duas entidades: a transmissão paralela de símbolos pertencentes à constelação de uma modulação digital, e os índices das posições do vetor de informação que transportam estes símbolos, denominada information bearing positions (IBP), enquanto as demais posições transportam zero. Além disto, o transmissor, previamente à transmissão dos dados pré-codifica o vetor de informação, possibilitando a redução da complexidade do nó receptor. Entre as vantagens desta estratégia destaca-se a concentração da energia transmitida apenas nas posições do vetor de informação que efetivamente contém símbolos, favorecendo o desempenho do sistema. Esta tese considera um sistema MIMO multiusuário (MU-MIMO) que emprega GPSM no enlace direto. O modelo de sinais desenvolvido para descrever este sistema evidencia a influência das matrizes que codificam as IBP no desempenho do sistema. Com base neste modelo, o sistema GPSM MU-MIMO é apresentado para três pré-codificadores lineares: Zero-Forcing, Block Diagonalization e Block Diagonalization de Duas Fases. Para cada um destes pré-codificadores são propostas estratégias de seleção das matrizes de codificação IBP, de acordo com a matriz que descreve o canal MU-MIMO corrente, a serem empregadas pelo transmissor visando melhorar o desempenho de detecção do sistema. As curvas de desempenho de detecção são comparadas a limitantes semianalíticos desenvolvidos. Por fim, considera-se o cenário em que existem mais antenas disponíveis na estação rádio-base e/ou nos usuários do que o número de cadeias de radiofrequência que os equipam. Esta tese apresenta estratégias ótimas e de complexidade reduzida de se explorar o uso das antenas mais favoráveis à transmissão e/ou recepção, em adição à escolha das matrizes de codificação IBP, com o objetivo de prover melhorias ao desempenho do sistema. / [en] Generalized Pre-coding Aided Spatial Modulation (GPSM) is a MIMO system communication strategy in which the transmitter encodes the information in two entities: the parallel transmission of symbols belonging to a digital modulation constellation, and the choice of the indices of the information vector elements that carries the informaiton symbols, denoted information bearing positions (IBP), while the remaing positions are set to zero. Besides, the transmitter precodes, prior to transmission, the information vector, which lets the receiver node benefit from complexity reduction. Among the advantages of this strategies, the concentration of the transmitted energy only on the information vector positions that transports modulated symbols, resulting in system performance improvement. This thesis considers a multiuser MIMO (MU-MIMO) that employs GPSM in the donwlink transmission. The presented signal model to describe this system evidences the influence of the IBP coding matrices on the system performance. Based on this model, GPSM MU-MIMO system is presented considering three linear precoders: Zero-Forcing, Block Diagonalization and Double-Stage Block Diagonalization. For each precoder, strategies to select the IBP encoding matrix, acording to the matrix that describes the current MU-MIMO channel, is proposed. These matrices are to be employed by the user, aiming at system detection performance improvement. Detection performance curves are compared to semianalytic lower bounds. Finally, a scenario in which that are a number of available antennas at the BS and/or at the users that exceed the number of radiofrequency chains. this thesis porposes optimal and reduced complexity strategies that exploit the use of the most favorable antennas for transmission and/or reception, in addition to the choice of the IBP enconding matrices, aiming at system performance improvement.

The use of multiple antennas to improve the reliability and capacity of wireless communication has been around for a while, leading to the concept of multiple-input multiple-output (MIMO) communications. To enable full MIMO potentials, the precoding design has been recognized as a crucial component. This thesis aims to design multiuser MIMO precoders of practical interest to achieve high reliability and capacity performance under various real-world constraints like inaccuracy of channel information acquired at the transmitter, hardware complexity, etc. Three prominent cases are considered which constitute the mainstream evolving directions of the current cellular communication standards and future 5G cellular communications. First, in a relay-assisted multiuser MIMO system, heavily quantized channel information obtained through limited feedback contributes to noticeable rate loss compared to when perfect channel information is available. This thesis derives an upper bound to characterize the system throughput loss caused by channel quantization error, and then develops a feedback quality control strategy to maintain the rate loss within a bounded range. Second, in a massive multiuser MIMO channel, due to the large array size, it is difficult to support each antenna with a dedicated radio frequency chain, thus making high-dimensional baseband precoding infeasible. To address this challenge, a low-complexity hybrid precoding scheme is designed to divide the precoding into two cascaded stages, namely, the low-dimensional baseband precoding and the high-dimensional phase-only processing at the radio frequency domain. Its performance is characterized in a closed form and demonstrated through computer simulations. Third, in a mmWave multiuser MIMO scenario, smaller wavelengths make it possible to incorporate excessive amounts of antenna elements into a compact form. However, we are faced with even worse hardware challenges as mixed signal processing at mmWave frequencies is more complex and power consuming. The channel sparsity is taken advantage of in this thesis to enable a simplified precoding scheme to steer the beam for each user towards its dominant propagation paths at the radio frequency domain only. The proposed scheme comes at significantly reduced complexity and is shown to be capable of achieving highly desirable performance based on asymptotic rate analysis. / Graduate

No description available.

Multiple-input, multiple-output (MIMO) techniques have been proposed to meet the needs for higher data rates and lower delays in future wireless communication systems. The downlink capacity of multiuser MIMO systems is achieved when the system transmits to several users simultaneously. Frequently, many more users request service than the transmitter can simultaneously support. Thus, the transmitter requires a scheduling algorithm for the users, which must balance the goals of increasing throughput, reducing multiuser interference, lowering delays, ensuring fairness and quality of service (QoS), etc. In this thesis, we investigate the application of genetic algorithms (GAs) to perform scheduling in multiuser MIMO systems. GAs are a fast, suboptimal, low-complexity method of solving optimization problems, such as the maximization of a scheduling metric, and can handle arbitrary functions and QoS constraints. We first examine a system that transmits using capacity-achieving dirty paper coding (DPC). Our proposed GA structure both selects users and determines their encoding order for DPC, which affects the rates they receive. Our GA can also schedule users independently on different carriers of a multi-carrier system. We demonstrate that the GA performance is close to that of an optimal exhaustive search, but at a greatly reduced complexity. We further show that the GA convergence time can be significantly reduced by tuning the values of its parameters. While DPC is capacity-achieving, it is also very complex. Thus, we also investigate GA scheduling with two linear precoding schemes, block diagonalization and successive zero-forcing. We compare the complexity and performance of the GA with "greedy" scheduling algorithms, and find the GA is more complex, but performs better at higher signal-to-noise ratios (SNRs) and smaller user pool sizes. Both algorithms are near-optimal, yet much less complex than an exhaustive search. We also propose hybrid greedy-genetic algorithms to gain benefits from both types of algorithms. Lastly, we propose an improved method of optimizing the transmit covariance matrices for successive zero-forcing. Our algorithm significantly improves upon the performance of the existing method at medium to high SNRs, and, unlike the existing method, can maximize a weighted sum rate, which is important for fairness and QoS considerations. / Communications

No description available.