Joint time‐slot and power allocation algorithm for data and energy integrated networks supporting internet of things (IoT)

IoT is an essential enabler for smart cities and smart society. However, its deployment at large scale faces a big challenge: battery replacement as most IoT devices are battery‐powered or even battery‐less. In a hostile environment, it is infeasible to replace batteries. Radio frequency (RF)‐enable wireless energy transfer (WET) is a promising technology to solve this problem. Since RF is also used for wireless data communication, a data and energy integrated network (DEIN) is the way forward. Based on the DEIN technology, a time allocation model is designed in this paper to manage the RF energy and uplink data transmission in different time slots. In the IoT scenario, the DEIN's primary service is to collect environmental information such as temperature, humidity, and luminance. Therefore, the uplink data transmission of the battery‐powered/battery‐less IoT nodes deserves more attention. To increase the uplink data transmission in case of consuming less energy in the DEIN system, we propose a joint time slot and power allocation algorithm to minimize the system's consumed energy for transmitting per bit of uplink data. It aims to maximize the efficiency of the DEIN system's energy utilization, which helps to achieve an energy‐efficient DEIN

Optimization techniques for reliable data communication in multi-antenna wireless systems

This thesis looks at new methods of achieving reliable data communication in wireless communication systems using different antenna transmission optimization methods. In particular, the problems of exploitation of MIMO communication channel diversity, secure downlink beamforming techniques, adaptive beamforming techniques, resource allocation methods, simultaneous power and information transfer and energy harvesting within the context of multi-antenna wireless systems are addressed

AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks

This article investigates the unmanned aerial vehicle (UAV)-assisted wireless powered Internet-of-Things system, where a UAV takes off from a data center, flies to each of the ground sensor nodes (SNs) in order to transfer energy and collect data from the SNs, and then returns to the data center. For such a system, an optimization problem is formulated to minimize the average Age of Information (AoI) of the data collected from all ground SNs. Since the average AoI depends on the UAV's trajectory, the time required for energy harvesting (EH) and data collection for each SN, these factors need to be optimized jointly. Moreover, instead of the traditional linear EH model, we employ a nonlinear model because the behavior of the EH circuits is nonlinear by nature. To solve this nonconvex problem, we propose to decompose it into two subproblems, i.e., a joint energy transfer and data collection time allocation problem and a UAV's trajectory planning problem. For the first subproblem, we prove that it is convex and give an optimal solution by using Karush-Kuhn-Tucker (KKT) conditions. This solution is used as the input for the second subproblem, and we solve optimally it by designing dynamic programming (DP) and ant colony (AC) heuristic algorithms. The simulation results show that the DP-based algorithm obtains the minimal average AoI of the system, and the AC-based heuristic finds solutions with near-optimal average AoI. The results also reveal that the average AoI increases as the flying altitude of the UAV increases and linearly with the size of the collected data at each ground SN

UAV-Assisted Wireless Powered Cooperative Mobile Edge Computing:Joint Offloading, CPU Control, and Trajectory Optimization

This article investigates the unmanned-aerial-vehicle (UAV)-enabled wireless powered cooperative mobile edge computing (MEC) system, where a UAV installed with an energy transmitter (ET) and an MEC server provides both energy and computing services to sensor devices (SDs). The active SDs desire to complete their computing tasks with the assistance of the UAV and their neighboring idle SDs that have no computing task. An optimization problem is formulated to minimize the total required energy of UAV by jointly optimizing the CPU frequencies, the offloading amount, the transmit power, and the UAV’s trajectory. To tackle the nonconvex problem, a successive convex approximation (SCA)-based algorithm is designed. Since it may be with relatively high computational complexity, as an alternative, a decomposition and iteration (DAI)-based algorithm is also proposed. The simulation results show that both proposed algorithms converge within several iterations, and the DAI-based algorithm achieve the similar minimal required energy and optimized trajectory with the SCA-based one. Moreover, for a relatively large amount of data, the SCA-based algorithm should be adopted to find an optimal solution, while for a relatively small amount of data, the DAI-based algorithm is a better choice to achieve smaller computing energy consumption. It also shows that the trajectory optimization plays a dominant factor in minimizing the total required energy of the system and optimizing acceleration has a great effect on the required energy of the UAV. Additionally, by jointly optimizing the UAV’s CPU frequencies and the amount of bits offloaded to UAV, the minimal required energy for computing can be greatly reduced compared to other schemes and by leveraging the computing resources of idle SDs, the UAV’s computing energy can also be greatly reduced

Intelligent-Reflecting-Surface-Assisted UAV Communications for 6G Networks

In 6th-Generation (6G) mobile networks, Intelligent Reflective Surfaces (IRSs) and Unmanned Aerial Vehicles (UAVs) have emerged as promising technologies to address the coverage difficulties and resource constraints faced by terrestrial networks. UAVs, with their mobility and low costs, offer diverse connectivity options for mobile users and a novel deployment paradigm for 6G networks. However, the limited battery capacity of UAVs, dynamic and unpredictable channel environments, and communication resource constraints result in poor performance of traditional UAV-based networks. IRSs can not only reconstruct the wireless environment in a unique way, but also achieve wireless network relay in a cost-effective manner. Hence, it receives significant attention as a promising solution to solve the above challenges. In this article, we conduct a comprehensive survey on IRS-assisted UAV communications for 6G networks. First, primary issues, key technologies, and application scenarios of IRS-assisted UAV communications for 6G networks are introduced. Then, we put forward specific solutions to the issues of IRS-assisted UAV communications. Finally, we discuss some open issues and future research directions to guide researchers in related fields

Advanced User-centric Modeling for Future Wireless Communication Networks: Performance Analysis and Optimization

Due to the increasingly growing demand for high data rates and a massive number of connected devices, future wireless communication networks are required to provide much more resources than the current networks can do. As an emerging solution for future cellular networks, dense deployment of small cell base stations (BSs) has received a great deal of attention both in academia and industry. A major challenge in dense cellular networks is the interference experienced by the user from its neighboring active BSs. The effect of such interference is more deleterious at cell-edge users which limits the density of deployed BSs. An effective promising solution is to move from a cell-centric to a user-centric paradigm which allows each user to be connected to a set (cluster) of BSs instead of being associated with a single one. This will mitigate the interference effect and remove the cell boundaries, i.e, no cell-edge users. In this thesis, we develop novel BS clustering models to enable a user-centric BS cooperation for future wireless networks. Unlike the existing clustering models, where a user is served by a cluster of BSs with fixed size (either a fixed number of BSs or fixed cluster radius), our proposed models adapt the cluster of each user dynamically based on its channel condition and quality-of-service (QoS) requirements. To design user-centric networks, we focus on several technologies introduced for future wireless wireless communication systems such as millimeter wave (mmWave) and terahertz (THz) networks, unmanned aerial vehicle (UAV)-assisted networks, hybrid multi-tier networks, and energy harvesting networks. We first investigate the performance of a user-centric mmWave network under the proposed dynamic BS clustering model using tools from stochastic geometry. To maximize the system spectral efficiency, an optimization framework for the user’s serving cluster is developed. Then, a user-centric THz system is designed to compensate for the high pathloss and hence improve the coverage of THz networks. Both dynamic and static clustering approaches are considered, based on which we study the coverage probability of the user-centric THz network by using stochastic geometry. Then, to design an energy-efficient and reliable air-to-air connection in UAV networks, we design a 3D user-centric clustering model where a set of UAV transmitters spatially distributed in a 3D space in the sky are carefully selected to serve another UAV receiver. Analytical expressions for the spectral efficiency and energy efficiency of this user-centric UAV network are provided and an efficient and tractable optimization framework to maximize its energy efficiency is developed. In this thesis, we also implement a user-centric BS clustering for hybrid networks where THz, mmWave, and sub6-GHz BSs coexist. In this system, a user can be associated with the best BS cluster, from either a sub6-GHz, mmWave or THz tier based on either the maximum SINR criterion or the maximum rate criterion. Thus, with carefully planned networks, enabling hybrid user-centric wireless systems can provide ultra-high rates while maintaining sufficient coverage in future multitier networks. Furthermore, we adopt the proposed user-centric clustering model to enhance the joint rate and energy coverage of cellular networks with simultaneous wireless information and power transfer (SWIPT). For this setup, we aim to insure that the user can harvest sufficient energy in a given time slot and receive the required minimum data from a given serving cluster. Then, a mathematical optimization model for the time switching coefficient is developed to maximize the system joint rate and energy coverage performance. All analytical results are validated by simulation with comparison to some of the existing works, demonstrating that the proposed analytical frameworks are accurate and efficient in the design and deployment of future user-centric wireless networks

Ultra-Dense Networks in 5G and Beyond: Challenges and Promising Solutions

Ultra-Dense Network (UDN) is one of the promising and leading directions in Fifth Generation and beyond (5GB) networks. In UDNs, Small Cells (SCs) or Small Base Stations (SBSs) such as microcells, picocells, or femtocells are deployed in high densities where inter-site distances are within the range of few or tens of meters. UDNs also require that SCs are typically deployed in relatively large densities compared to the Human-Type Communication Users (HTCUs) such as smartphones, tablets, and/or laptops. Such SCs are characterized by their low transmission powers, small coverage areas, and low cost. Hence, the deployment of the SCs can be done either by the cellular network operators or by the customers themselves within their premises to maintain certain levels of Quality of Service (QoS). However, the randomness of the deployment of the SCs along with the small inter-site distances may degrade the achievable performance due to the uncontrolled Inter-Cell Interference (ICI). Therefore, idle mode capability is an inevitable feature in the high-density regime of SCs. In idle mode, a SC is switched off to prevent ICI when no user is associated to it. In doing so, we can imagine the UDN as a mobile network that keeps following the users to remain as close as possible to them. In 5G, different use cases are required to be supported such as enhanced Mobile Broad-Band (eMBB), Ultra-Reliable and Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). On one hand, the inevitable upcoming era of smart living requires unprecedented advances in enabling technologies to support the main building blocks of this era which are Internet of Things (IoT) devices. Machine-Type Communication (MTC), the cellular version of Machine-to-Machine (M2M) communication, constitutes the main enabling technology to support communications among such devices with minimal or even without human intervention. The massive number of these devices, Machine-Type Communication Devices (MTCDs), and the immense amount of traffic generated by them require a paramount shift in cellular and non-cellular wireless technologies to achieve the required connectivity. On the other hand, the sky-rocketing number of data hungry applications installed on human-held devices, or HTCUs, such as video conferencing and virtual reality applications require their own advances in the wireless infrastructure in terms of high capacity, enhanced reliability, and reduced latency. Throughout this thesis, we exploit the UDN infrastructure integrated with other 5G resources and enabling technologies to explore the possible opportunities in supporting both HTC and MTC, either solely or simultaneously. Given the shorter distances between transmitters and receivers encountered in UDNs, more realistic models of the path loss must be adopted such as the Stretched Exponential Path Loss (SEPL) model. We use tools from stochastic geometry to formulate novel mathematical frameworks that can be used to investigate the achievable performance without having to rely on extensive time-consuming Monte-Carlo simulations. Besides, the derived analytical expressions can be used to tune some system parameters or to propose some approaches/techniques that can be followed to optimize the performance of the system under certain circumstances. Tackling practical scenarios, the complexity, or sometimes in-feasibility, of providing unlimited backhaul capacity for the massive number of SCs must be considered. In this regard, we adopt multiple-association where each HTCU is allowed to associate with multiple SCs. By doing so, we carefully split the targeted traffic among several backhaul links to mitigate the bottleneck forced by limited backhaul capacities. It is noteworthy that for coexisting MTCDs with the HTCUs, activating more SCs would allow more MTCDs to be supported without introducing additional ICI towards the HTCUs. Targeting different application, multiple-association can be also adopted to tackle computation-intensive applications of HTCUs. In particular, for applications such as augmented reality and environment recognition that require heavy computations, a task is split and partially offloaded to multiple SCs with integrated Edge Computing Servers (ECSs). Then, the task partitions are processed in parallel to reduce the end-to-end processing delay. Based on relative densities between HTCUs and SCs, we use tools from stochastic geometry to develop an offline adaptive task division technique that further reduces the average end-to-end processing delay per user. With the frequent serious data breaches experienced in recent years, securing data has become more of a business risk rather than an information technology (IT) issue. Hence, we exploit the dense number of SCs found in UDN along with Physical Layer Security (PLS) protocols to secure data transfer. In particular, we again adopt multiple-association and split the data of HTCUs into multiple streams originating from different SCs to prevent illegitimate receivers from eavesdropping. To support massive number of MTCDs, we deploy the Non-Orthogonal Multiple-Access (NOMA) technique. Using power NOMA, more than one device can be supported over the same frequency/time resource and their signals are distinguished at the receiver using Successive Interference Cancellation (SIC). In the same scope, exploiting the available resources in 5G and beyond networks, we investigate a mMTC scenario in an UDN operating in the Millimeter Wave (mmWave) band and supported by wireless backhauling. In doing so, we shed lights on the possible gains of utilizing the mmWave band where the severe penetration losses of mmWave can be exploited to mitigate the significant ICI in UDNs. Also, the vast bandwidth available in the mmWave band helps to allocate more Resource Blocks (RBs) per SCs which corresponds to supporting more MTCDs