Application of evolutionary computation techniques in emerging optimization problems in 5G and beyond wireless systems

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2021.Os sistemas comunicação sem fio 5G e além (B5G, do inglês Beyong 5G) permitirão a plena implantação de aplicações existentes, como carros autônomos, redes de sensores massivas e casas inteligentes. Para tornar essas aplicações possíveis, requisitos rigorosos, como alta eficiência espectral e ultra baixa latência de comunicação, devem ser atendidos. Para atender a esses requisitos, diferentes tecnologias-chave estão em desenvolvimento, como comunicações de Ondas Milimétricas (mmWave, do inglês Millimeter Wave) e Superfícies Refletivas Inteligentes (IRS, do inglês Intelligent Reflecting Surfaces). As comunicações mmWave têm atraído grande interesse devido ao abundante espectro de frequência disponível, ao contrário das bandas congestionadas adotadas nas redes 4G. No entanto, as bandas mmWave apresentam características de propagação desfavoráveis. Para superar tais problemas de propagação, o uso de beamforming altamente direcional é uma solução eficaz. Além disso, recentemente, uma tecnologia de baixo custo e alta eficiência energética denominada IRS, uma meta-superfície equipada com um grande número de elementos passivos de baixo custo, capaz de refletir o sinal incidente com uma dada mudança de fase/amplitude, foi desenvolvida para otimizar a capacidade da rede. Implantando densamente IRSs em redes de comunicação sem fio e coordenando seus elementos de maneira inteligente, os canais sem fio entre o transmissor e o receptor podem ser intencional e deterministicamente controlados para melhorar a qualidade do sinal no receptor. Embora essas tecnologias tenham inúmeros benefícios para o desempenho do sistema, elas apresentam muitos desafios em sua implantação. Mais especificamente, embora as bandas mmWave permitam considerar o uso de beamforming altamente direcional tanto na BS quanto no UE, isto pode representar um desafio para o processo de Acesso Inicial (IA, do inglês Initial Access) pois, uma vez que a transmissão omnidirecional não pode ser aplicada, devido ao seu baixo ganho de potência e SNR recebido, a duração geral do IA pode ser muito longa. O atraso causado pela busca direcional deve ser pequeno para atender a alguns dos requisitos das redes B5G como baixa latência de ponta-a-ponta. Além disso, apesar da capacidade das IRSs de controlar os canais sem fio, o projeto do beamforming na BS e na IRS é um problema desafiador devido à necessidade de estimar a informação de estado do canal (CSI, do inglês Channel State Information) de todos os links do sistema. No entanto, para estimar o CSI entre a IRS e a BS ou entre a IRS e o UE, cada elemento da IRS precisa ser equipado com uma cadeia de radiofrequência (RF, do inglês Radio Frequency), o que aumenta consideravelmente o custo e o consumo de energia do sistema e vai contra algumas das principais vantagens de utilizar IRSs em sistemas de comunicação sem fio. Portanto, motivados pelos problemas emergentes acima, nesta tese, pretendemos desenvolver novos métodos baseados em técnicas de Computação Evolutiva tais como, Algoritmos Genéticos (GA, do inglês Genetic Algorithm) e Otimização por Enxame de Partículas (PSO, do inglês Particle Swarm Optimization), visando resolver o problema de IA e realizar o projeto do beamforming na BS e IRS sem conhecimento prévio do CSI na BS. Os resultados obtidos nesta tese mostram que os métodos desenvolvidos podem reduzir consideravelmente o atraso e alcançar um desempenho próximo ao ótimo no problema de projeto do beamforming na BS e IRS com sobrecarga de treinamento reduzida.Abstract: Beyond 5G (B5G) wireless systems will enable the deployment of demanding applications such as autonomous cars, massive sensor networks, and smart homes. To make these applications possible, stringent requirements such as improved spectrum efficiency and low communication latency must be fulfilled. In order to meet these requirements, different key technologies are in development such as millimeter Wave (mmWave) communications and Intelligent Reflecting Surfaces (IRS). The mmWave communications have attracted great interest due to the abundant available spectrum, unlike the congested bands adopted in the 4G networks. However, the mmWave bands present poor propagation characteristics. To overcome these propagation issues, the use of highly directional beamforming is an effective solution. In addition, recently, an energy-efficient and low-cost technology named IRS, which is a meta-surface equipped with a large number of low-cost passive elements, capable of reflecting the incident signal with a given phase/amplitude shift, was developed to increase the network capacity. By densely deploying IRSs in wireless communication networks and intelligently coordinating their elements, the wireless channels between the transmitter and receiver can be intentionally and deterministically controlled to improve the signal quality at the receiver. Although these technologies have uncountable benefits for the system performance, they present many challenges in their deployment. More specifically, although the mmWave bands allow to consider highly directional beamforming at the BS and UE, this can be challenging for the Initial Access (IA) process. Since omnidirectional transmission may not be applied, due to its low power gain and received SNR, the overall duration of IA can be very long. The delay caused by directional search must be small to meet some of the B5G requirements for low end-to-end latency. Moreover, despite the capacity of controlling the wireless channels of the IRSs, designing the beamforming at the BS and at the IRS is a challenging problem due to the necessity of estimating the channel state information (CSI) of all system links. However, to estimate the CSI between IRS and BS or between IRS and UE, each element of the IRS needs to be equipped with one radio-frequency (RF) chain which greatly increases the cost and energy consumption of the system and goes against some of the original advantages of using an IRS. Therefore, motivated by the above emerging problems, in this thesis, we intend to develop new methods based on Evolutionary Computation techniques, i.e., Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to solve the IA problem and to design the beamforming at the BS and IRS without CSI. Results show that the developed methods can reduce the IA delay and achieve a close-to-optimal performance in the IRS beamforming problem with reduced training overhead

RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency, ultra-low latency, and ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this paper, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments and electromagnetic interference. We summarize corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as next-generation multiple-access (NGMA), simultaneous wireless information and power transfer (SWIPT), and millimeter wave (mmWave). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.Comment: 30 pages, 15 figure

An MRL-Based Design Solution for RIS-Assisted MU-MIMO Wireless System under Time-Varying Channels

Utilizing Deep Reinforcement Learning (DRL) for Reconfigurable Intelligent Surface (RIS) assisted wireless communication has been extensively researched. However, existing DRL methods either act as a simple optimizer or only solve problems with concurrent Channel State Information (CSI) represented in the training data set. Consequently, solutions for RIS-assisted wireless communication systems under time-varying environments are relatively unexplored. However, communication problems should be considered with realistic assumptions; for instance, in scenarios where the channel is time-varying, the policy obtained by reinforcement learning should be applicable for situations where CSI is not well represented in the training data set. In this paper, we apply Meta-Reinforcement Learning (MRL) to the joint optimization problem of active beamforming at the Base Station (BS) and phase shift at the RIS, motivated by MRL's ability to extend the DRL concept of solving one Markov Decision Problem (MDP) to multiple MDPs. We provide simulation results to compare the average sum rate of the proposed approach with those of selected forerunners in the literature. Our approach improves the sum rate by more than 60% under time-varying CSI assumption while maintaining the advantages of typical DRL-based solutions. Our study's results emphasize the possibility of utilizing MRL-based designs in RIS-assisted wireless communication systems while considering realistic environment assumptions.Comment: To be published in proceedings of the 2023 IEEE Conference on Global Communications (GLOBECOM