Channel Estimation for mmWave Massive MIMO Based Access and Backhaul in Ultra-Dense Network

Millimeter-wave (mmWave) massive MIMO used for access and backhaul in ultra-dense network (UDN) has been considered as the promising 5G technique. We consider such an heterogeneous network (HetNet) that ultra-dense small base stations (BSs) exploit mmWave massive MIMO for access and backhaul, while macrocell BS provides the control service with low frequency band. However, the channel estimation for mmWave massive MIMO can be challenging, since the pilot overhead to acquire the channels associated with a large number of antennas in mmWave massive MIMO can be prohibitively high. This paper proposes a structured compressive sensing (SCS)-based channel estimation scheme, where the angular sparsity of mmWave channels is exploited to reduce the required pilot overhead. Specifically, since the path loss for non-line-of-sight paths is much larger than that for line-of-sight paths, the mmWave massive channels in the angular domain appear the obvious sparsity. By exploiting such sparsity, the required pilot overhead only depends on the small number of dominated multipath. Moreover, the sparsity within the system bandwidth is almost unchanged, which can be exploited for the further improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterpart, and it can approach the performance bound.Comment: 6 pages, 5 figures. Millimeter-wave (mmWave), mmWave massive MIMO, compressive sensing (CS), hybrid precoding, channel estimation, access, backhaul, ultra-dense network (UDN), heterogeneous network (HetNet). arXiv admin note: substantial text overlap with arXiv:1604.03695, IEEE International Conference on Communications (ICC'16), May 2016, Kuala Lumpur, Malaysi

Massive MIMO transmission techniques

Next generation of mobile communication systems must support astounding data traffic increases, higher data rates and lower latency, among other requirements. These requirements should be met while assuring energy efficiency for mobile devices and base stations. Several technologies are being proposed for 5G, but a consensus begins to emerge. Most likely, the future core 5G technologies will include massive MIMO (Multiple Input Multiple Output) and beamforming schemes operating in the millimeter wave spectrum. As soon as the millimeter wave propagation difficulties are overcome, the full potential of massive MIMO structures can be tapped. The present work proposes a new transmission system with bi-dimensional antenna arrays working at millimeter wave frequencies, where the multiple antenna configurations can be used to obtain very high gain and directive transmission in point to point communications. A combination of beamforming with a constellation shaping scheme is proposed, that enables good user isolation and protection against eavesdropping, while simultaneously assuring power efficient amplification of multi-level constellations

Beamforming management and beam training in 5G system

Massive multiple-input-multiple-output (MIMO) antenna system with beamforming technique is an integral part of upcoming 5G new radio (NR) system. For the upcoming deployment of 5G NR system in both stand-alone (SA) and non-stand-alone (NSA) structure, beamforming plays an important role to achieve its key features and meet the estimated requirement. To be employed with massive MIMO antenna structure, beamforming will allow 5G system to serve several users at a time with better throughput and spectral usage. Beamforming will also minimize the path loss due to high susceptibility of millimetre wave and provide beamforming gain. For a wide range of benefit scheme, beamforming is currently a hot topic regarding the deployment of 5G. With the advantage of both analog and digital beamforming, hybrid beamforming structure can provide better system benchmark performance in terms of cost and flexibility. Switched beam training and adaptive beam training approaches and algorithms are developed in order to reduce training time, signalling overhead and misdetection probability. Some of the approaches and algorithm are addressed in this thesis. Beamforming management ensures the initiation and sustainability of the established link between transmitter and receiver through different processes. Beam tracking helps to keep track of the receiver devices during mobility. As beamforming is related to antenna configuration, near-field spherical wave front incident problem was ignored, and all the references and examples presented in this topic was obtained with a far-field propagation perspective. To avoid mutual coupling between antenna elements and grating lobe problems in antenna radiation pattern, each element is separated by half of the wavelength. This thesis paper aims to provide a broader view into beamforming scenario, starting from the basics of beamforming to training the beams and management aspects in the hardware part of 5G structure. Another goal is to present the necessity of beamforming in a 5G system by stating different benefits scheme such as spatial diversity, interference suppression, energy efficiency, spectral efficiency and so on. These benefits are justified by evaluating various research paper and MATLAB simulations

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

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

Energy efficiency using cloud management of LTE networks employing fronthaul and virtualized baseband processing pool

The cloud radio access network (C-RAN) emerges as one of the future solutions to handle the ever-growing data traffic, which is beyond the physical resources of current mobile networks. The C-RAN decouples the traffic management operations from the radio access technologies, leading to a new combination of a virtualized network core and a fronthaul architecture. This new resource coordination provides the necessary network control to manage dense Long-Term Evolution (LTE) networks overlaid with femtocells. However, the energy expenditure poses a major challenge for a typical C-RAN that consists of extended virtualized processing units and dense fronthaul data interfaces. In response to the power efficiency requirements and dynamic changes in traffic, this paper proposes C-RAN solutions and algorithms that compute the optimal backup topology and network mapping solution while denying interfacing requests from low-flow or inactive femtocells. A graph-coloring scheme is developed to label new formulated fronthaul clusters of femtocells using power as the performance metric. Additional power savings are obtained through efficient allocations of the virtualized baseband units (BBUs) subject to the arrival rate of active fronthaul interfacing requests. Moreover, the proposed solutions are used to reduce power consumption for virtualized LTE networks operating in the Wi-Fi spectrum band. The virtualized network core use the traffic load variations to determine those femtocells who are unable to transmit to switch them off for additional power savings. The simulation results demonstrate an efficient performance of the given solutions in large-scale network models

Fast heuristic algorithm for 5G network energy consumption optimization

This study focuses on 5G network, which deploys small cells to form multi-hop topologies using high capacity backhaul wireless links to provide localized capacity. Nowadays, high energy efficiency is very important because powering on unnecessarily a massive amount of macro cells or small cells may lead to increased expenses, CO2 emission and environmental destruction. Based on a given MILP that solves the energy consumption optimization problem in a 5G network, this research proposes a heuristic algorithm based on integer relaxation that accelerates the resolution of the MILP. The heuristic algorithm could diminish the route options by striking out the impossible links or links with lower possibility to be used. Our numerical evaluations demonstrate that the proposed algorithm can find very good solutions in short time and has similar performance in terms of energy efficiency over a large number of traffic scenarios