Análise de desempenho de receptores baseados em reticulados para MIMO e fastICA em sistemas MIMO cego massivos

Orientador: Gustavo FraidenraichTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Estatísticas da taxa-soma dos decodificadores Integer Forcing (IF) e outros decodificadores baseados em reticulados para sistemas de Múltipla Entrada e Múltipla Saída (MIMO) são analisadas. Duas aproximações para a taxa-soma de decodificadores lineares são derivadas. A primeira aproximação é baseada no algoritimo Gauss-Lagrange para sistemas com duas antenas no transmissor e receptor (arranjo 2x2) e canais descorrelacionados. A segunda aproximação considera um sistema com um arranjo nxn de antenas, para o caso correlacionado e descorrelacionado e é baseado no segundo teorema de Minkowiski O desempenho de decodificadores IF e Compute and Forward Transform (CFT) são analisados na presença de erro de estimação de canal. Uma aproximação para a taxa-soma média na presença de erros de estimação de canal e canais com realização fixa é derivada. Uma aproximação para a taxa-soma ergódica dos decodificadores IF na presença de canais correlacionados e descorrelacionados também é derivada. Decodificadores lineares IF atraíram atenção significativa devido ao seu potencial de atingir melhor desempenho do que outros decodificadores lineares, especialmente quando as matrizes de canal são aproximadamente singulares. No entanto, uma análise mais profunda de seu desempenho na presença de canais não determinísticos é necessária para que se possa quantificar sua vantagem em relação a decodificadores lineares clássicos e para que se possa corretamente projetar sistemas baseados nestes decodificadoresAbstract: The statistics of the sum-rate of Integer Forcing (IF) and other lattice-based Multiple Input Multiple Output (MIMO) systems are analyzed. Two approximations to the achievable sum-rate of the IF linear receiver and their respective analytical probability density functions (PDF) are derived. The first approximation is based on the Gauss-Lagrange algorithm for systems with two antennas at the transmitter and receiver (2x2 arrays) and uncorrelated channels. The second approximation considers an nxn array for both correlated and uncorrelated channels and its derivation is based on Minkowiski's second theorem. The performance of IF and Compute and Forward Transform (CFT) receivers is also analyzed under the presence of channel estimation errors. An approximation to their average sum-rate in the presence of these errors for fixed channel realizations is derived. An approximation to the Ergodic IF sum-rate for correlated and uncorrelated channels is also derived. IF linear receiver has attracted significant attention recently due to their potential to perform better than other linear receivers, especially in the presence of channel matrices that are close to singular. However, a more in-depth analysis of its performance in the presence of non-deterministic channels is necessary in order to quantify its advantage over classical linear receivers and to correctly design systems that rely on these decoders. Another contribution of this work involves blind decoding in Massive MIMO systems. We propose a variation to the fast Independent Component Analysis (fastICA) which takes into consideration the shape of the constellations to obtain better performanceDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia ElétricaCAPE

On the application of massive mimo systems to machine type communications

This paper evaluates the feasibility of applying massive multiple-input multiple-output (MIMO) to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel, devised to exclusively consume data traffic from machine type communications (MTC) devices, the capacity (i.e., number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time-frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without the previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically, as well as the MMSE estimator, with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of the sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines toward a better understanding of the use of massive MIMO in MTC networks

Centralized and partial decentralized design for the Fog Radio Access Network

Fog Radio Access Network (F-RAN) has been shown to be a promising network architecture for the 5G network. With F-RAN, certain amount of signal processing functionalities are pushed from the Base Station (BS) on the network edge to the BaseBand Units (BBU) pool located remotely in the cloud. Hence, partially centralized network operation and management can be achieved, which can greatly improve the energy and spectral efficiency of the network, in order to meet the requirements of 5G. In this work, the optimal design for both uplink and downlink of F-RAN are intensively investigated

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

On the Spectral and Energy Efficiencies of Full-Duplex Cell-Free Massive MIMO

In-band full-duplex (FD) operation is practically more suited for short-range communications such as WiFi and small-cell networks, due to its current practical limitations on the self-interference cancellation. In addition, cell-free massivemultiple-input multiple-output (CF-mMIMO) is a new and scalable version of MIMO networks, which is designed to bring service antennas closer to end user equipments (UEs). To achieve higher spectral and energy efficiencies (SE-EE) of a wireless network, it is of practical interest to incorporate FD capability into CF-mMIMO systems to utilize their combined benefits. We formulate a novel and comprehensive optimization problem for the maximization of SE and EE in which power control, access point-UE (AP-UE) association and AP selection are jointly optimized under a realistic power consumption model, resulting in a difficult class of mixed-integer nonconvex programming. To tackle the binary nature of the formulated problem, we propose an efficient approach by exploiting a strong coupling between binary and continuous variables, leading to a more tractable problem. In this regard, two low-complexity transmission designs based on zero-forcing (ZF) are proposed. Combining tools from inner approximation framework and Dinkelbach method, we develop simple iterative algorithms with polynomial computational complexity in each iteration and strong theoretical performance guaranteed. Furthermore, towards a robust design for FD CFmMIMO, a novel heap-based pilot assignment algorithm is proposed to mitigate effects of pilot contamination. Numerical results show that our proposed designs with realistic parameters significantly outperform the well-known approaches (i.e., smallcell and collocated mMIMO) in terms of the SE and EE. Notably, the proposed ZF designs require much less execution time than the simple maximum ratio transmission/combining