A survey on hybrid beamforming techniques in 5G : architecture and system model perspectives

The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers' structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain

Utilization of cloud RAN architecture with eCPRI fronthaul in 5G network

With increased reliability, massive network capacity, and extremely reduced latency, 5G expands the mobile ecosystem into new realms. 5G impacts every industry and innovation, making transportation and conveyance safer, remote healthcare, accuracy agriculture, digitized logistics, and much more. In this age, 5G calls for new levels of flexibility and broadness in architecting, scaling, and deploying telecommunication networks, which need a further step ahead in technology and enter Cloud Technology. Cloud technology provides fascinating possibilities to complement the existing tried and tested technologies in the Radio Access Network (RAN) domain. Cloud RAN (CRAN) refers to relying on RAN functions over an inclusive platform instead of a purpose-built hardware platform. It represents a progression in wireless communication technology, leveraging the Common public radio interface (CPRI) standard, Dense Wavelength Division Multiplexing (DWDM) innovation, and millimeter wave (mmWave) propagation for extended-range signals. A CRAN network comprises of three fundamental elements. The initial element is the Distant Wireless Unit (DRU) or Remote Radio Component (RRH), utilized within a network to link wireless devices to entry points; these units are equipped with transceivers for transmitting and receiving signals. Next, a Baseband Unit (BBU) centre or hub serves as a centralized site functioning as a data processing hub. Separate BBU modules can be assembled independently or interconnected to distribute resources, adapting to the network's changing dynamics and needs. Communication among these modules boasts remarkably high bandwidth and exceptionally low latency. The BBU can be further segmented into DU (Distributed Unit) and CU (Centralized Unit). The third crucial component is a fronthaul or conveyance network – the connecting layer between a baseband unit (BBU) and a set of RRUs, utilizing optical fibres, cellular links, or mmWave communication. The goal of this thesis is to find a way to utilize the 5G RAN Architecture as efficiently as possible and for this purpose, Enhanced Common Public Radio Interface (eCPRI) or enhanced CPRI fronthaul is adopted instead of CPRI as it is a manner of splitting up the functions performed by baseband unit and putting some of that in the RRU so it can reduce the burden on the fibre. Enhanced CPRI makes it possible to send some data packets to a virtual Distributed Unit (vDU) and others to a virtual Centralized Unit (vCU) which results in reduced data traffic on fibre. The first part of this research paper focuses on considering and learning about the 5G Cloud RAN architecture's main components, some cloud RAN history, and important components included in the 5G Cloud RAN. In the second part, research goes in depth about the fronthaul gateway technology that is eCPRI structure, its functional split, its difference from CPRI in structure and functionality, and how it is enhanced and developed. Considering CRAN specifications, it will also include some eCPRI protocol delay management and timing studies. Finally, Test cases are developed that can authenticate the low latency and high throughput of data with eCPRI fronthaul in 5G Cloud RAN as compared to CPRI fronthaul. The inspiration behind this is to recreate the model with substantial changes that work with an ideal behaviour of a subsystem, with this a tool or an environment can be obtained that maximizes the efficiency of 5G CRAN. It will also permit network architects and designers to experiment with new features, which can reduce costs, save time, improve latency. It can also provide a tool to verification engineers that will help them to generate optimal replies of the system necessary for evaluating the practical realization of that system

Joint access-backhaul mechanisms in 5G cell-less architectures

Older generations of wireless networks, such as 1G and 2G were deployed using leased line, copper or fibre line as backhaul. Later, in 3G and 4G, microwave wireless links have also worked as backhaul links while the backbone of the network was still wireline-based. However, due to multiple different use cases and deployment scenarios of 5G, solo wireline based backhaul network is not a cost-efficient option for the operators anymore. For cost-efficient and fast deployment, wireless backhaul options are very attractive. As drawbacks, wireless backhaul links have capacity and distance limitations. To take the advantages of both the solutions, i.e., wired and wireless, 5G transport networks are anticipated to be a heterogeneous, complex, and with stringent performance requirements. To address the aforementioned challenges, wireless backhaul options are providing more attractive solutions, and hence, technologies using the same resources (e.g., frequency channels) may be used by both access and backhaul networks. In this scenario, blurring the separation line between access and backhaul networks allows resource sharing and cooperation between both the networks and minimizes the network deployment and maintenance cost significantly. Therefore, in 5G, the access and backhaul networks cannot be seen as separate entities; rather, we seek to integrate them together to ensure the best use of resources. In this thesis, firstly, we investigate the challenges and potential technologies of 5G transport network. Later, to address these challenges, we identify and present different approaches to perform joint access-backhaul mechanism. An initial performance evaluation of access-aware backhaul optimization is presented, where backhaul network is dynamically assigned with the required resources to serve the dynamic requirements of a 5G access network. The evaluation results and discussions manifest the resource efficiency of joint access-backhaul mechanisms. Functional splits in different layers of the access network comes as an intelligent solution to reduce the enormous capacity requirements of the transport network in a centralized radio access network approach, which tends to centralize almost all the functionalities into a central unit, leaving only radio frequency functions at the access points. From the joint access-backhaul mechanism perspective, we propose a novel technique, which takes the benefit of functional splits at physical layer, to design a heterogeneous transport network in an economical budget-limited and capacity-limited scenario. Till today, the limited capacity of the wireless backhaul links remains a challenge, and hence, frequency spectrum becomes scarce, and requires efficient utilization. To address this challenge, a joint spectrum sharing technique to implement joint accessbackhaul mechanism is presented. Evaluation results show that our proposed joint spectrum sharing technique, where spectrum allocation in the backhaul network follows the access network's traffic load, is fair and efficient in terms of spectrum utilization. We also propose a machine learning technique, which analyses data from a real network and estimates access network's traffic pattern, and subsequently, assigns bandwidth in the access network according to the traffic estimations. Presented evaluation results show that a well-trained machine learning model can be very efficient to obtain an efficient utilization of frequency spectrum.Las primeras generaciones de redes móviles, se implementaron utilizando líneas de cobre o fibra para la conexión entre la red de acceso y el núcleo de la red (conexión backhaul). Más tarde, los enlaces inalámbricos también han funcionado como backhaul mientras que la columna vertebral de la red seguía basada en cable. Sin embargo, debido a los múltiples escenarios de implementación de 5G, una red de backhaul basada solamente en cable ya no es una opción rentable para los operadores. Para una implementación rentable y rápida, las opciones de backhaul inalámbrico son muy atractivas. Como inconvenientes, los enlaces backhaul inalámbricos tienen limitaciones de capacidad y distancia. Para aprovechar las ventajas de ambas soluciones, es decir, cableadas e inalámbricas, se prevé que las redes de transporte 5G sean heterogéneas, complejas y con estrictos requisitos de rendimiento. Para abordar los desafíos antes mencionados, las opciones de backhaul inalámbrico brindan soluciones más atractivas y, por lo tanto, las tecnologías que usan los mismos recursos (por ejemplo, canales de frecuencia) pueden usarse tanto en las redes de acceso como en las de backhaul. En este escenario, desdibujar la línea de separación entre las redes de acceso y backhaul permite el intercambio de recursos y la cooperación entre ambas redes, y minimiza significativamente los costes de implementación y mantenimiento de la red. Por lo tanto, en 5G las redes de acceso y backhaul no pueden verse como entidades separadas; más bien consideraremos su integración para asegurar el mejor uso de los recursos. En esta tesis, en primer lugar, investigamos los desafíos y las tecnologías potenciales para la implementación de la red de backhaul 5G. Más tarde, para abordar dichos desafíos, identificamos diferentes enfoques para un mecanismo conjunto de gestión de la red de acceso y backhaul. Se presenta una evaluación de rendimiento inicial para la optimización de backhaul que tiene en cuenta el estado de la red de acceso, donde la red de backhaul se equipa dinámicamente con los recursos necesarios para cumplir con los requisitos de la red de acceso 5G. Los resultados de la evaluación manifiestan la mayor eficiencia de los mecanismos de gestión de recursos que consideran redes de acceso y backhaul conjuntamente. Las divisiones funcionales en diferentes capas de la red de acceso (functional splits) se presentan como una solución inteligente para reducir los enormes requisitos de capacidad de la red de transporte en un enfoque de red de acceso, que tiende a centralizar casi todas las funcionalidades en una unidad central, dejando solo las funciones más relacionadas con la transmisión/recepción de señales en los puntos de acceso. Desde la perspectiva del mecanismo conjunto de red de acceso y backhaul, proponemos una técnica novedosa, que aprovecha las divisiones funcionales en la capa física para diseñar una red de transporte heterogénea con un presupuesto económico y un escenario de capacidad limitada. Hasta el día de hoy, la capacidad limitada de los enlaces inalámbricos sigue siendo un desafío, dado que el espectro de frecuencias es escaso y requiere una utilización eficiente. Para hacer frente a este desafío, se presenta una técnica de gestión de recursos espectrales compartidos entre red de acceso y backhaul. Los resultados de la evaluación muestran que nuestra propuesta, donde la asignación de espectro en la red de backhaul se hace de acuerdo a la carga de tráfico de la red de acceso, es justa y eficiente. También proponemos una técnica de aprendizaje automático, que analiza datos de una red real y estima el patrón de tráfico de la red de acceso para, posteriormente, asignar ancho de banda en la red de acceso de acuerdo con dichas estimaciones. Los resultados de la evaluación presentados muestran que un modelo de aprendizaje automático bien entrenado puede ser una herramienta muy útil a la hora de obtener una utilización eficiente del espectro de frecuencias.Postprint (published version

A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks

The diverse service requirements coming with the advent of sophisticated applications as well as a large number of connected devices demand for revolutionary changes in the traditional distributed radio access network (RAN). To this end, Cloud-RAN (CRAN) is considered as an important paradigm to enhance the performance of the upcoming fifth generation (5G) and beyond wireless networks in terms of capacity, latency, and connectivity to a large number of devices. Out of several potential enablers, efficient resource allocation can mitigate various challenges related to user assignment, power allocation, and spectrum management in a CRAN, and is the focus of this paper. Herein, we provide a comprehensive review of resource allocation schemes in a CRAN along with a detailed optimization taxonomy on various aspects of resource allocation. More importantly, we identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH) selection, throughput maximization, spectrum management, network utility, and power allocation. Furthermore, we present emerging use-cases including heterogeneous CRAN, millimeter-wave CRAN, virtualized CRAN, Non- Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex enabled CRAN to illustrate how their performance can be enhanced by adopting CRAN technology. We then classify and discuss objectives and constraints involved in CRAN-based 5G and beyond networks. Moreover, a detailed taxonomy of optimization methods and solution approaches with different objectives is presented and discussed. Finally, we conclude the paper with several open research issues and future directions

Spectrum Sharing, Latency, and Security in 5G Networks with Application to IoT and Smart Grid

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency. To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario. In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices. To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches

Intelligent Resource Allocation in 5G Multi-Radio Heterogeneous Networks

The fast-moving evolution of wireless networks, which started less than three decades ago, has resulted in worldwide connectivity and influenced the development of a global market in all related areas. However, in recent years, the growing user traffic demands have led to the saturation of licensed and unlicensed frequency bands regarding capacity and load-over-time. On the physical layer the used spectrum efficiency is already close to Shannon’s limit; however the traffic demand continues to grow, forcing mobile network operators and equipment manufacturers to evaluate more effective strategies of the wireless medium access.One of these strategies, called cell densification, implies there are a growing number of serving entities, with the appropriate reduction of the per-cell coverage area. However, if implemented blindly, this approach will lead to a significant growth in the average interference level and overhead control signaling, which are both required to allow sufficient user mobility. Furthermore, the interference is also affected by the increasing variety of radio access technologies (RATs) and applications, often deployed without the necessary level of cooperation with technologies that are already in place.To overcome these problems today’s telecommunication standardization groups are trying to collaborate. That is why the recent agenda of the fifth generation wireless networks (5G) includes not only the development schedules for the particular technologies but also implies there should be an expansion of the appropriate interconnection techniques. In this thesis, we describe and evaluate the concept of heterogeneous networks (HetNets), which involve the cooperation between several RATs.In the introductory part, we discuss the set of the problems, related to HetNets, and review the HetNet development process. Moreover, we show the evolution of existing and potential segments of the multi-RAT 5G network, together with the most promising applications, which could be used in future HetNets.Further, in the thesis, we describe the set of key representative scenarios, including three-tier WiFi-LTE multi-RAT deployment, MTC-enabled LTE, and the mmWave-based network. For each of these scenarios, we define a set of unsolved issues and appropriate solutions. For the WiFi-LTE multi-RAT scenario, we develop the framework, enabling intelligent and flexible resource allocation between the involved RATs. For MTC-enabled LTE, we study the effect of massive MTC deployments on the performance of LTE random access procedure and propose some basic methods to improve its efficiency. Finally, for the mmWave scenario, we study the effects of connectivity strategies, human body blockage and antenna array configuration on the overall network performance. Next, we develop a set of validated analytical and simulation-based techniques which allow us to evaluate the performance of proposed solutions. At the end of the introductory part a set of HetNet-related demo activities is demonstrated

DYNAMIC USER-CENTRIC CAPACITY MAXIMIZATION BY OPTIMIZING ANTENNA PARAMETERS

Cellular network has become the primary means of voice as well as data communication. With sophisticated but affordable end user devices e.g. smartphones, tablet PCs etc. and ubiquity of mobile connectivity, users are able to access a range of multimedia services requiring low to high data rate and with desired quality of experience everywhere and all the times. However, mobile network operators (MNOs) always have limited bandwidth resources as compared to users’ demand, as bandwidth is the most expensive resource in the network. Thus MNOs always seek new tools and technologies to optimally utilize the available bandwidth to accommodate maximum number of users and provide high quality of services, maximizing the revenue in return. Especially, in the case of ultra-dense heterogeneous deployment of small cells equipped with massive-MIMO antenna configuration operating over mmWave spectrum in 5G, automated solution for dynamic spectrum optimization with respect to rapidly changing users and network requirement will be of critical importance. This thesis presents a novel scheme for spectral efficiency (SE) optimization through clustering of users. By clustering users with respect to their geographical concentration we propose a solution for dynamic steering of antenna beam by dynamically adjusting antenna azimuth and tilt angles with respect to the most focal point in every cell that would maximize overall SE in the system. The proposed framework thus introduces the notion of elastic cells that can be potential component of 5G networks. The proposed scheme decomposes large-scale system-wide optimization problem into small-scale local sub-problems and thus provides a low complexity solution for dynamic system wide optimization. Every sub-problem involves clustering of users to determine focal point of the cell for given user distribution in time and space, and determining new values of azimuth and tilt that would optimize the overall system SE performance. To this end, we proposed three user clustering algorithms to transform a given user distribution into the focal points that can be used in optimization process: the first is based on received signal to interference ratio (SIR) at the user; the second is based on received signal level (RSL) at the user; the third and final one is based on relative distances of users from the base stations. We also formulate and solve an optimization problem to determine optimal radii of clusters. The performances of proposed algorithms and framework are evaluated through system level simulations. Performance comparison against benchmark where no elastic cell deployed, shows that a gain in spectral efficiency of up to 26% is achievable depending upon user distribution in each cell