Fading-ratio-based selection for massive MIMO systems under line-of-sight propagation

in Wireless Networks (2022)

Massive multiple-input multiple-output (MIMO) enables increased throughput by using spatial multiplexing. However, the throughput may severely degrade when the number of users served by a single base ... [more ▼]

Massive multiple-input multiple-output (MIMO) enables increased throughput by using spatial multiplexing. However, the throughput may severely degrade when the number of users served by a single base station increases, especially under line-of-sight (LoS) propagation. Selecting users is a possible solution to deal with this problem. In the literature, the user selection algorithms can be divided into two classes: small-scale fading aware (SSFA) and large scale fading aware (LSFA) algorithms. The LSFA algorithms are good solutions for massive MIMO systems under non LoS propagation since the small-scale fading does not affect the system performance under this type of propagation. For the LoS case, the small scale fading has a great impact on the system performance, requiring the use of SSFA algorithms. However, disregarding the large-scale fading is equivalent to assuming that all users are equidistant from the base station and experience the same level of shadowing, which is not a reasonable approximation in practical applications. To address this shortcoming, a new user selection algorithm called the fading-ratio-based selection (FRBS) is proposed. FRBS considers both fading information to drop those users that induce the highest interference to the remaining ones. Simulation results considering LoS channels show that using FRBS yields near optimum downlink throughput, which is similar to that of the state-of-the-art algorithm, but with much lower computational complexity. Moreover, the use of FRBS with zero forcing precoder resulted in 26.28% improvement in the maximum throughput when compared with SSFA algorithms, and 35.39% improvement when compared with LSFA algorithms. [less ▲]

Online Learning and Adaptive Filters

Book published by Cambridge University Press (2022)

Learn to solve the unprecedented challenges facing Online Learning and Adaptive Signal Processing in this concise, intuitive text. The ever-increasing amount of data generated every day requires new ... [more ▼]

Learn to solve the unprecedented challenges facing Online Learning and Adaptive Signal Processing in this concise, intuitive text. The ever-increasing amount of data generated every day requires new strategies to tackle issues such as: combining data from a large number of sensors; improving spectral usage, utilizing multiple-antennas with adaptive capabilities; or learning from signals placed on graphs, generating unstructured data. Solutions to all of these and more are described in a condensed and unified way, enabling you to expose valuable information from data and signals in a fast and economical way. The up-to-date techniques explained here can be implemented in simple electronic hardware, or as part of multi-purpose systems. Also featuring alternative explanations for online learning, including newly developed methods and data selection, and several easily implemented algorithms, this one-of-a-kind book is an ideal resource for graduate students, researchers, and professionals in online learning and adaptive filtering. [less ▲]