57 research outputs found

    Multi-objective resource optimization in space-aerial-ground-sea integrated networks

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    Space-air-ground-sea integrated (SAGSI) networks are envisioned to connect satellite, aerial, ground, and sea networks to provide connectivity everywhere and all the time in sixth-generation (6G) networks. However, the success of SAGSI networks is constrained by several challenges including resource optimization when the users have diverse requirements and applications. We present a comprehensive review of SAGSI networks from a resource optimization perspective. We discuss use case scenarios and possible applications of SAGSI networks. The resource optimization discussion considers the challenges associated with SAGSI networks. In our review, we categorized resource optimization techniques based on throughput and capacity maximization, delay minimization, energy consumption, task offloading, task scheduling, resource allocation or utilization, network operation cost, outage probability, and the average age of information, joint optimization (data rate difference, storage or caching, CPU cycle frequency), the overall performance of network and performance degradation, software-defined networking, and intelligent surveillance and relay communication. We then formulate a mathematical framework for maximizing energy efficiency, resource utilization, and user association. We optimize user association while satisfying the constraints of transmit power, data rate, and user association with priority. The binary decision variable is used to associate users with system resources. Since the decision variable is binary and constraints are linear, the formulated problem is a binary linear programming problem. Based on our formulated framework, we simulate and analyze the performance of three different algorithms (branch and bound algorithm, interior point method, and barrier simplex algorithm) and compare the results. Simulation results show that the branch and bound algorithm shows the best results, so this is our benchmark algorithm. The complexity of branch and bound increases exponentially as the number of users and stations increases in the SAGSI network. We got comparable results for the interior point method and barrier simplex algorithm to the benchmark algorithm with low complexity. Finally, we discuss future research directions and challenges of resource optimization in SAGSI networks

    Integration of hybrid networks, AI, Ultra Massive-MIMO, THz frequency, and FBMC modulation toward 6g requirements : A Review

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    The fifth-generation (5G) wireless communications have been deployed in many countries with the following features: wireless networks at 20 Gbps as peak data rate, a latency of 1-ms, reliability of 99.999%, maximum mobility of 500 km/h, a bandwidth of 1-GHz, and a capacity of 106 up to Mbps/m2. Nonetheless, the rapid growth of applications, such as extended/virtual reality (XR/VR), online gaming, telemedicine, cloud computing, smart cities, the Internet of Everything (IoE), and others, demand lower latency, higher data rates, ubiquitous coverage, and better reliability. These higher requirements are the main problems that have challenged 5G while concurrently encouraging researchers and practitioners to introduce viable solutions. In this review paper, the sixth-generation (6G) technology could solve the 5G limitations, achieve higher requirements, and support future applications. The integration of multiple access techniques, terahertz (THz), visible light communications (VLC), ultra-massive multiple-input multiple-output ( μm -MIMO), hybrid networks, cell-free massive MIMO, and artificial intelligence (AI)/machine learning (ML) have been proposed for 6G. The main contributions of this paper are a comprehensive review of the 6G vision, KPIs (key performance indicators), and advanced potential technologies proposed with operation principles. Besides, this paper reviewed multiple access and modulation techniques, concentrating on Filter-Bank Multicarrier (FBMC) as a potential technology for 6G. This paper ends by discussing potential applications with challenges and lessons identified from prior studies to pave the path for future research

    Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

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    Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

    Machine Learning Empowered Reconfigurable Intelligent Surfaces

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    Reconfigurable intelligent surfaces (RISs) or known as intelligent reflecting surfaces (IRSs) have emerged as potential auxiliary equipment for future wireless networks, which attracts extensive research interest in their characteristics, applications, and potential. RIS is a panel surface equipped with a number of reflective elements, which can artificially modify the propagation environment of the electrogenic signals. Specifically, RISs have the ability to precisely adjust the propagation direction, amplitude, and phase-shift of the signals, providing users with a set of cascaded channels in addition to direct channels, and thereby improving the communication performances for users. Compared with other candidate technologies such as active relays, RIS has advantages in terms of flexible deployment, economical cost, and high energy efficiency. Thus, RISs have been considered a potential candidate technique for future wireless networks. In this thesis, a wireless network paradigm for the sixth generation (6G) wireless networks is proposed, where RISs are invoked to construct smart radio environments (SRE) to enhance communication performances for mobile users. In addition, beyond the conventional reselecting-only RIS, a novel model of RIS is originally proposed, namely, simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS splits the incident signal into transmitted and reflected signals, making full utilization of them to generate 360360^{\circ} coverage around the STAR-RIS panel, improving the coverage of the RIS. In order to fully exert the channel domination and beamforming ability of the RISs and STAR-RSIs to construct SREs, several machine learning algorithms, including deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) approaches are developed to optimize the communication performance in respect of sum data rate or energy efficiency for the RIS-assisted networks. Specifically, several problems are investigated including 1) the passive beamforming problem of the RIS with consideration of configuration overhead is resolved by a DL and a DRL algorithm, where the time overhead of configuration of RIS is successfully reduced by the machine learning algorithms. Consequently, the throughput during a time frame improved 95.2%95.2\% by invoking the proposed algorithms; 2) a novel framework of mobile RISs-enhanced indoor wireless networks is proposed, and a FL enhanced DRL algorithm is proposed for the deployment and beamforming optimization of the RIS. The average throughput of the indoor users severed by the mobile RIS is improved 15.1%15.1\% compared to the case of conventional fixed RIS; 3) A STAR-RIS assisted multi-user downlink multiple-input single-output (MISO) communication system is investigated, and a pair of hybrid reinforcement learning algorithms are proposed for the hybrid control of the transmitting and reflecting beamforming of the STAR-RIS, which ameliorate 7%7\% of the energy efficiency of the STAR-RIS assisted networks; 4) A tile-based low complexity beamforming approach is proposed for STAR-RISs, and the proposed tile-based beamforming approach is capable of achieving homogeneous data rate performance with element-based beamforming with appreciable lower complexity. By designing and operating the computer simulation, this thesis demonstrated 1) the performance gain in terms of sum data rate or energy efficiency by invoking the proposed RIS in the wireless communication networks; 2) the data rate or energy efficient performance gain of the proposed STAR-RIS compared to the existing reflecting-only RIS; 3) the effect of the proposed machine learning algorithms in terms of convergence rate, optimality, and complexity compared to the benchmarks of existing algorithms

    On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds

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    Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed

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

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    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

    Future Wireless Networks: Towards Learning-driven Sixth-generation Wireless Communications

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    The evolution of wireless communication networks, from present to the emerging fifth-generation (5G) new radio (NR), and sixth-generation (6G) is inevitable, yet propitious. The thesis evolves around application of machine learning and optimization techniques to problems in spectrum management, internet-of-things (IoT), physical layer security, and intelligent reflecting surface (IRS). The first problem explores License Assisted Access (LAA), which leverages unlicensed resource sharing with the Wi-Fi network as a promising technique to address the spectrum scarcity issue in wireless networks. An optimal communication policy is devised which maximizes the throughput performance of LAA network while guaranteeing a proportionally fair performance among LAA stations and a fair share for Wi-Fi stations. The numerical results demonstrate more than 75 % improvement in the LAA throughput and a notable gain of 8-9 % in the fairness index. Next, we investigate the unlicensed spectrum sharing for bandwidth hungry diverse IoT networks in 5G NR. An efficient coexistence mechanism based on the idea of adaptive initial sensing duration (ISD) is proposed to enhance the diverse IoT-NR network performance while keeping the primary Wi-Fi network's performance to a bearable threshold. A Q-learning (QL) based algorithm is devised to maximize the normalized sum throughput of the coexistence Wi-Fi/IoT-NR network. The results confirm a maximum throughput gain of 51 % and ensure that the Wi-Fi network's performance remains intact. Finally, advanced levels of network security are critical to maintain due to severe signal attenuation at higher frequencies of 6G wireless communication. Thus, an IRS-based model is proposed to address the issue of network security under trusted-untrusted device diversity, where the untrusted devices may potentially eavesdrop on the trusted devices. A deep deterministic policy gradient (DDPG) algorithm is devised to jointly optimize the active and passive beamforming matrices. The results confirm a maximum gain of 2-2.5 times in the sum secrecy rate of trusted devices and ensure Quality-of-Service (QoS) for all the devices. In conclusion, the thesis has led towards efficient, secure, and smart communication and build foundation to address similar complex wireless networks
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