Bat algorithm–based beamforming for mmWave massive MIMO systems

© 2019 John Wiley & Sons, Ltd. In this paper, an optimized analog beamforming scheme for millimeter-wave (mmWave) massive MIMO system is presented. This scheme aims to achieve the near-optimal performance.by searching for the optimized combination of analog precoder and combiner. In order to compensate for the occurrence of attenuation in the magnitude of mmWave signals, the codebook-dependent analog beamforming in conjunction with precoding at transmitting end and combining signals at the receiving end is utilized. Nonetheless, the existing and traditional beamforming schemes involve a more difficult and complicated search for the optimal combination of analog precoder/combiner matrices from predefined codebooks. To solve this problem, we have referred to a modified bat algorithm to find the optimal combination value. This algorithm will explore the possible pairs of analog precoder/combiner as a way to come up with the best match in order to attain near-optimal performance. The analysis shows that the optimized beamforming scheme presented in this paper can improve the performance that is very close to the beam steering benchmark that we have considered.Published versio

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

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial