2,144 research outputs found

    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

    University of Windsor Graduate Calendar 2023 Spring

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp

    Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies

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    High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G networks. HTS are mainly designed to provide higher data rates and capacities. Fueled by technological advancements including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas, HTS have emerged as a fundamental component for future network generation. This paper offers a comprehensive state-of-the-art of HTS systems, with a focus on standardization, patents, channel multiple access techniques, routing, load balancing, and the role of software-defined networking (SDN). In addition, we provide a vision for next-satellite systems that we named as extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed such that it maximizes spectrum reuse and data rates, and flexibly steers the capacity to satisfy user demand. We introduce a novel architecture for future regenerative payloads while summarizing the challenges imposed by this architecture

    University of Windsor Graduate Calendar 2023 Winter

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1026/thumbnail.jp

    Intégration des méthodes formelles dans le développement des RCSFs

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    In this thesis, we have relied on formal techniques in order to first evaluate WSN protocols and then to propose solutions that meet the requirements of these networks. The thesis contributes to the modelling, analysis, design and evaluation of WSN protocols. In this context, the thesis begins with a survey on WSN and formal verification techniques. Focusing on the MAC layer, the thesis reviews proposed MAC protocols for WSN as well as their design challenges. The dissertation then proceeds to outline the contributions of this work. As a first proposal, we develop a stochastic generic model of the 802.11 MAC protocol for an arbitrary network topology and then perform probabilistic evaluation of the protocol using statistical model checking. Considering an alternative power source to operate WSN, energy harvesting, we move to the second proposal where a protocol designed for EH-WSN is modelled and various performance parameters are evaluated. Finally, the thesis explores mobility in WSN and proposes a new MAC protocol, named "Mobility and Energy Harvesting aware Medium Access Control (MEH-MAC)" protocol for dynamic sensor networks powered by ambient energy. The protocol is modelled and verified under several features

    Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

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    In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions

    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 360∘360^{\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

    Computational efficiency maximization for UAV-assisted MEC network with energy harvesting in disaster scenarios

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    Wireless networks are expected to provide unlimited connectivity to an increasing number of heterogeneous devices. Future wireless networks (sixth-generation (6G)) will accomplish this in three-dimensional (3D) space by combining terrestrial and aerial networks. However, effective resource optimization and standardization in future wireless networks are challenging because of massive resource-constrained devices, diverse quality-of-service (QoS) requirements, and a high density of heterogeneous devices. Recently, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks are considered a potential candidate to provide effective and efficient solutions for disaster management in terms of disaster monitoring, forecasting, in-time response, and situation awareness. However, the limited size of end-user devices comes with the limitation of battery lives and computational capacities. Therefore, offloading, energy consumption and computational efficiency are significant challenges for uninterrupted communication in UAV-assisted MEC networks. In this thesis, we consider a UAV-assisted MEC network with energy harvesting (EH). To achieve this, we mathematically formulate a mixed integer non-linear programming problem to maximize the computational efficiency of UAV-assisted MEC networks with EH under disaster situations. A power splitting architecture splits the source power for communication and EH. We jointly optimize user association, the transmission power of UE, task offloading time, and UAV’s optimal location. To solve this optimization problem, we divide it into three stages. In the first stage, we adopt k-means clustering to determine the optimal locations of the UAVs. In the second stage, we determine user association. In the third stage, we determine the optimal power of UE and offloading time using the optimal UAV location from the first stage and the user association indicator from the second stage, followed by linearization and the use of interior-point method to solve the resulting linear optimization problem. Simulation results for offloading, no-offloading, offloading with EH, and no-offloading no-EH scenarios are presented with a varying number of UAVs and UEs. The results show the proposed EH solution’s effectiveness in offloading scenarios compared to no-offloading scenarios in terms of computational efficiency, bits computed, and energy consumptio

    Survey on Wi-Fi and Cellular Communication Technology for Advanced Metering Infrastructure (AMI) in a Developing Economy

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    Traditional energy meters have suffered from a lack of automated analysis and inaccuracy in reading energy consumption, which has brought about smart metering systems. Developing economies such as in Africa. still experience a setback in electricity monitoring and load distribution because of existing traditional meter systems in use. Communication technologies play an important role to improve the monitoring of energy consumption and ensure a road map toward a smart grid. This paper reviews communication technologies used for Advanced Metering Infrastructure (AMI) emphasizing Wi-Fi and Cellular technologies. Metrics used to evaluate their performance include cost, energy efficiency, coverage, deployment, latency, payload, and scalability. The review presents a benchmark for research on AMI communication technologies in developing economies. When adopted, the expected AMI benefits are reduced energy theft, cost efficiency, real-time analysis, security, and safety of energy supply in developing economies
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