Dual-battery empowered green cellular networks

With awareness of the potential harmful effects to the environment and climate change, on-grid brown energy consumption of information and communications technology (ICT) has drawn much attention. Cellular base stations (BSs) are among the major energy guzzlers in ICT, and their contributions to the global carbon emissions increase sustainedly. It is essential to leverage green energy to power BSs to reduce their on-grid brown energy consumption. However, in order to furthest save on-grid brown energy and decrease the on-grid brown energy electricity expenses, most existing green energy related works only pursue to maximize the green energy utilization while compromising the services received by the mobile users. In reality, dissatisfaction of services may eventually lead to loss of market shares and profits of the network providers. In this research, a dual-battery enabled profit driven user association scheme is introduced to jointly consider the traffic delivery latency and green energy utilization to maximize the profits for the network providers in heterogeneous cellular networks. Since this profit driven user association optimization problem is NP-hard, some heuristics are presented to solve the problem with low computational complexity. Finally, the performance of the proposed algorithm is validated through extensive simulations. In addition, the Internet of Things (IoT) heralds a vision of future Internet where all physical things/devices are connected via a network to promote a heightened level of awareness about our world and dramatically improve our daily lives. Nonetheless, most wireless technologies utilizing unlicensed bands cannot provision ubiquitous and quality IoT services. In contrast, cellular networks support large-scale, quality of service guaranteed, and secured communications. However, tremendous proximal communications via local BSs will lead to severe traffic congestion and huge energy consumption in conventional cellular networks. Device-to-device (D2D) communications can potentially offload traffic from and reduce energy consumption of BSs. In order to realize the vision of a truly global IoT, a novel architecture, i.e., overlay-based green relay assisted D2D communications with dual batteries in heterogeneous cellular networks, is introduced. By optimally allocating the network resource, the introduced resource allocation method provisions the IoT services and minimizes the overall energy consumption of the pico relay BSs. By balancing the residual green energy among the pico relay BSs, the green energy utilization is maximized; this furthest saves the on-grid energy. Finally, the performance of the proposed architecture is validated through extensive simulations. Furthermore, the mobile devices serve the important roles in cellular networks and IoT. With the ongoing worldwide development of IoT, an unprecedented number of edge devices imperatively consume a substantial amount of energy. The overall IoT mobile edge devices have been predicted to be the leading energy guzzler in ICT by 2020. Therefore, a three-step green IoT architecture is proposed, i.e., ambient energy harvesting, green energy wireless transfer and green energy balancing, in this research. The latter step reinforces the former one to ensure the availability of green energy. The basic design principles for these three steps are laid out and discussed. In summary, based on the dual-battery architecture, this dissertation research proposes solutions for the three aspects, i.e., green cellular BSs, green D2D communications and green devices, to hopefully and eventually actualize green cellular access networks, as part of the ongoing efforts in greening our society and environment

A Coalitional Model Predictive Control for the Energy Efficiency of Next-Generation Cellular Networks

Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates)

Energy and throughput efficient strategies for heterogeneous future communication networks

As a result of the proliferation of wireless-enabled user equipment and data-hungry applications, mobile data traffic has exponentially increased in recent years.This in-crease has not only forced mobile networks to compete on the scarce wireless spectrum but also to intensify their power consumption to serve an ever-increasing number of user devices. The Heterogeneous Network (HetNet) concept, where mixed types of low-power base stations coexist with large macro base stations, has emerged as a potential solution to address power consumption and spectrum scarcity challenges. However, as a consequence of their inflexible, constrained, and hardware-based configurations, HetNets have major limitations in adapting to fluctuating traffic patterns. Moreover, for large mobile networks, the number of low-power base stations (BSs) may increase dramatically leading to sever power consumption. This can easily overwhelm the benefits of the HetNet concept. This thesis exploits the adaptive nature of Software-defined Radio (SDR) technology to design novel and optimal communication strategies. These strategies have been designed to leverage the spectrum-based cell zooming technique, the long-term evolution licensed assisted access (LTE-LAA) concept, and green energy, in order to introduce a novel communication framework that endeavors to minimize overall network on-grid power consumption and to maximize aggregated throughput, which brings significant benefits for both network operators and their customers. The proposed strategies take into consideration user data demands, BS loads, BS power consumption, and available spectrum to model the research questions as optimization problems. In addition, this thesis leverages the opportunistic nature of the cognitive radio (CR) technique and the adaptive nature of the SDR to introduce a CR-based communication strategy. This proposed CR-based strategy alleviates the power consumption of the CR technique and enhances its security measures according to the confidentiality level of the data being sent. Furthermore, the introduced strategy takes into account user-related factors, such as user battery levels and user data types, and network-related factors, such as the number of unutilized bands and vulnerability level, and then models the research question as a constrained optimization problem. Considering the time complexity of the optimum solutions for the above-mentioned strategies, heuristic solutions were proposed and examined against existing solutions. The obtained results show that the proposed strategies can save energy consumption up to 18%, increase user throughput up to 23%, and achieve better spectrum utilization. Therefore, the proposed strategies offer substantial benefits for both network operators and users

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

A secure and energy-aware approach for cognitive radio communications

The cognitive radio (CR) technique has revealed a novel way of utilizing the precious radiospectrum via allowing unlicensed users to opportunistically access unutilized licensed bands. Using sucha technique enables agile and flexible access to the radio spectrum and can resolve the spectrum-scarcityproblem and maximize spectrum efficiency. However, two major impediments have been limiting thewidespread adoption of cognitive radio technology. The software-defined radio technology, which is theenabling technology for the CR technique, is power-hungry and this raises a major concern for battery-constrained devices such as smart phones and laptops. Secondly, the opportunistic and open nature ofthe CR can lead to major security concerns about the data being sent and how safe it is. In this paper,we introduce an energy-and-security-aware CR-based communication approach that alleviates the powerconsumption of the CR technique and enhances its security measures according to the confidentialitylevel of the data being sent. Furthermore, the proposed approach takes into account user-related factors,such as the user’s battery level and user’s data type, and network-related factors, such as the number ofunutilized bands and vulnerability level and then models the research question as a constrained optimizationproblem. Considering the time complexity of the optimum solution, we also propose a heuristic solution.We examine the proposed solution against existing solutions, and our obtained results show that theproposed approach can save energy consumption up to 18%, increase user throughput up to 20%, andachieve better spectrum utilization, up to 98%. Our proposed admission approach has the potential toopen a new research direction towards safer and greener cognitive radio techniques

Improved planning and resource management in next generation green mobile communication networks

In upcoming years, mobile communication networks will experience a disruptive reinventing process through the deployment of post 5th Generation (5G) mobile networks. Profound impacts are expected on network planning processes, maintenance and operations, on mobile services, subscribers with major changes in their data consumption and generation behaviours, as well as on devices itself, with a myriad of different equipment communicating over such networks. Post 5G will be characterized by a profound transformation of several aspects: processes, technology, economic, social, but also environmental aspects, with energy efficiency and carbon neutrality playing an important role. It will represent a network of networks: where different types of access networks will coexist, an increasing diversity of devices of different nature, massive cloud computing utilization and subscribers with unprecedented data-consuming behaviours. All at greater throughput and quality of service, as unseen in previous generations. The present research work uses 5G new radio (NR) latest release as baseline for developing the research activities, with future networks post 5G NR in focus. Two approaches were followed: i) method re-engineering, to propose new mechanisms and overcome existing or predictably existing limitations and ii) concept design and innovation, to propose and present innovative methods or mechanisms to enhance and improve the design, planning, operation, maintenance and optimization of 5G networks. Four main research areas were addressed, focusing on optimization and enhancement of 5G NR future networks, the usage of edge virtualized functions, subscriber’s behavior towards the generation of data and a carbon sequestering model aiming to achieve carbon neutrality. Several contributions have been made and demonstrated, either through models of methodologies that will, on each of the research areas, provide significant improvements and enhancements from the planning phase to the operational phase, always focusing on optimizing resource management. All the contributions are retro compatible with 5G NR and can also be applied to what starts being foreseen as future mobile networks. From the subscriber’s perspective and the ultimate goal of providing the best quality of experience possible, still considering the mobile network operator’s (MNO) perspective, the different proposed or developed approaches resulted in optimization methods for the numerous problems identified throughout the work. Overall, all of such contributed individually but aggregately as a whole to improve and enhance globally future mobile networks. Therefore, an answer to the main question was provided: how to further optimize a next-generation network - developed with optimization in mind - making it even more efficient while, simultaneously, becoming neutral concerning carbon emissions. The developed model for MNOs which aimed to achieve carbon neutrality through CO2 sequestration together with the subscriber’s behaviour model - topics still not deeply focused nowadays – are two of the main contributions of this thesis and of utmost importance for post-5G networks.Nos próximos anos espera-se que as redes de comunicações móveis se reinventem para lá da 5ª Geração (5G), com impactos profundos ao nível da forma como são planeadas, mantidas e operacionalizadas, ao nível do comportamento dos subscritores de serviços móveis, e através de uma miríade de dispositivos a comunicar através das mesmas. Estas redes serão profundamente transformadoras em termos tecnológicos, económicos, sociais, mas também ambientais, sendo a eficiência energética e a neutralidade carbónica aspetos que sofrem uma profunda melhoria. Paradoxalmente, numa rede em que coexistirão diferentes tipos de redes de acesso, mais dispositivos, utilização massiva de sistema de computação em nuvem, e subscritores com comportamentos de consumo de serviços inéditos nas gerações anteriores. O trabalho desenvolvido utiliza como base a release mais recente das redes 5G NR (New Radio), sendo o principal focus as redes pós-5G. Foi adotada uma abordagem de "reengenharia de métodos” (com o objetivo de propor mecanismos para resolver limitações existentes ou previsíveis) e de “inovação e design de conceitos”, em que são apresentadas técnicas e metodologias inovadoras, com o principal objetivo de contribuir para um desenho e operação otimizadas desta geração de redes celulares. Quatro grandes áreas de investigação foram endereçadas, contribuindo individualmente para um todo: melhorias e otimização generalizada de redes pós-5G, a utilização de virtualização de funções de rede, a análise comportamental dos subscritores no respeitante à geração e consumo de tráfego e finalmente, um modelo de sequestro de carbono com o objetivo de compensar as emissões produzidas por esse tipo de redes que se prevê ser massiva, almejando atingir a neutralidade carbónica. Como resultado deste trabalho, foram feitas e demonstradas várias contribuições, através de modelos ou metodologias, representando em cada área de investigação melhorias e otimizações, que, todas contribuindo para o mesmo objetivo, tiveram em consideração a retro compatibilidade e aplicabilidade ao que se prevê que sejam as futuras redes pós 5G. Focando sempre na perspetiva do subscritor da melhor experiência possível, mas também no lado do operador de serviço móvel – que pretende otimizar as suas redes, reduzir custos e maximizar o nível de qualidade de serviço prestado - as diferentes abordagens que foram desenvolvidas ou propostas, tiveram como resultado a resolução ou otimização dos diferentes problemas identificados, contribuindo de forma agregada para a melhoria do sistema no seu todo, respondendo à questão principal de como otimizar ainda mais uma rede desenvolvida para ser extremamente eficiente, tornando-a, simultaneamente, neutra em termos de emissões de carbono. Das principais contribuições deste trabalho relevam-se precisamente o modelo de compensação das emissões de CO2, com vista à neutralidade carbónica e um modelo de análise comportamental dos subscritores, dois temas ainda pouco explorados e extremamente importantes em contexto de redes futuras pós-5G

Holistic resource management in UAV-assisted wireless networks

Unmanned aerial vehicles (UAVs) are considered as a promising solution to assist terrestrial networks in future wireless networks (i.e., beyond fifth-generation (B5G) and sixth-generation (6G)). The convergence of various technologies requires future wireless networks to provide multiple functionalities, including communication, computing, control, and caching (4C), necessary for applications such as connected robotics and autonomous systems. The majority of existing works consider the developments in 4C individually, which limits the cooperation among 4C for potential gains. UAVs have been recently introduced to supplement mobile edge computing (MEC) in terrestrial networks to reduce network latency by providing mobile resources at the network edge in future wireless networks. However, compared to ground base stations (BSs), the limited resources at the network edge call for holistic management of the resources, which requires joint optimization. We provide a comprehensive review of holistic resource management in UAV-assisted wireless networks. Integrated resource management considers the challenges associated with aerial networks (such as three-dimensional (3D) placement of UAVs, trajectory planning, channel modelling, and backhaul connectivity) and terrestrial networks (such as limited bandwidth, power, and interference). We present architectures (source-UAV-destination and UAV-destination architecture) and 4C in UAV-assisted wireless networks. We then provide a detailed discussion on resource management by categorizing the optimization problems into individual or combinations of two (communication and computation) or three (communication, computation and control). Moreover, solution approaches and performance metrics are discussed and analyzed for different objectives and problem types. We formulate a mathematical framework for holistic resource management to minimize the linear combination of network latency and cost for user association while guaranteeing the offloading, computing, and caching constraints. Binary decision variables are used to allocate offloading and computing resources. Since the decision variables are binary and constraints are linear, the formulated problem is a binary linear programming problem. We propose a heuristic algorithm based on the interior point method by exploiting the optimization structure of the problem to get a sub-optimal solution with less complexity. Simulation results show the effectiveness of the proposed work when compared to the optimal results obtained using branch and bound. Finally, we discuss insight into the potential future research areas to address the challenges of holistic resource management in UAV-assisted wireless networks

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research