761 research outputs found

    Securing NextG networks with physical-layer key generation: A survey

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    As the development of next-generation (NextG) communication networks continues, tremendous devices are accessing the network and the amount of information is exploding. However, with the increase of sensitive data that requires confidentiality to be transmitted and stored in the network, wireless network security risks are further amplified. Physical-layer key generation (PKG) has received extensive attention in security research due to its solid information-theoretic security proof, ease of implementation, and low cost. Nevertheless, the applications of PKG in the NextG networks are still in the preliminary exploration stage. Therefore, we survey existing research and discuss (1) the performance advantages of PKG compared to cryptography schemes, (2) the principles and processes of PKG, as well as research progresses in previous network environments, and (3) new application scenarios and development potential for PKG in NextG communication networks, particularly analyzing the effect and prospects of PKG in massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), artificial intelligence (AI) enabled networks, integrated space-air-ground network, and quantum communication. Moreover, we summarize open issues and provide new insights into the development trends of PKG in NextG networks

    Optimization of Beyond 5G Network Slicing for Smart City Applications

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    Transitioning from the current fifth-generation (5G) wireless technology, the advent of beyond 5G (B5G) signifies a pivotal stride toward sixth generation (6G) communication technology. B5G, at its essence, harnesses end-to-end (E2E) network slicing (NS) technology, enabling the simultaneous accommodation of multiple logical networks with distinct performance requirements on a shared physical infrastructure. At the forefront of this implementation lies the critical process of network slice design, a phase central to the realization of efficient smart city networks. This thesis assumes a key role in the network slicing life cycle, emphasizing the analysis and formulation of optimal procedures for configuring, customizing, and allocating E2E network slices. The focus extends to catering to the unique demands of smart city applications, encompassing critical areas such as emergency response, smart buildings, and video surveillance. By addressing the intricacies of network slice design, the study navigates through the complexities of tailoring slices to meet specific application needs, thereby contributing to the seamless integration of diverse services within the smart city framework. Addressing the core challenge of NS, which involves the allocation of virtual networks on the physical topology with optimal resource allocation, the thesis introduces a dual integer linear programming (ILP) optimization problem. This problem is formulated to jointly minimize the embedding cost and latency. However, given the NP-hard nature of this ILP, finding an efficient alternative becomes a significant hurdle. In response, this thesis introduces a novel heuristic approach the matroid-based modified greedy breadth-first search (MGBFS) algorithm. This pioneering algorithm leverages matroid properties to navigate the process of virtual network embedding and resource allocation. By introducing this novel heuristic approach, the research aims to provide near-optimal solutions, overcoming the computational complexities associated with the dual integer linear programming problem. The proposed MGBFS algorithm not only addresses the connectivity, cost, and latency constraints but also outperforms the benchmark model delivering solutions remarkably close to optimal. This innovative approach represents a substantial advancement in the optimization of smart city applications, promising heightened connectivity, efficiency, and resource utilization within the evolving landscape of B5G-enabled communication technology

    Coverage Performance Analysis of Reconfigurable Intelligent Surface-aided Millimeter Wave Network with Blockage Effect

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    In order to solve spectrum resource shortage and satisfy immense wireless data traffic demands, millimeter wave (mmWave) frequency with large available bandwidth has been proposed for wireless communication in 5G and beyond 5G. However, mmWave communications are susceptible to blockages. This characteristic limits the network performance. Meanwhile, reconfigurable intelligent surface (RIS) has been proposed to improve the propagation environment and extend the network coverage. Unlike traditional wireless technologies that improve transmission quality from transceivers, RISs enhance network performance by adjusting the propagation environment. One of the promising applications of RISs is to provide indirect line-of-sight (LoS) paths when the direct LoS path between transceivers does not exist. This application makes RIS particularly useful in mmWave communications. With effective RIS deployment, the mmWave RIS-aided network performance can be enhanced significantly. However, most existing works have analyzed RIS-aided network performance without exploiting the flexibility of RIS deployment and/or considering blockage effect, which leaves huge research gaps in RIS-aided networks. To fill the gaps, this thesis develops RIS-aided mmWave network models considering blockage effect under the stochastic geometry framework. Three scenarios, i.e., indoor, outdoor and outdoor-to-indoor (O2I) RIS-aided networks, are investigated. Firstly, LoS propagation is hard to be guaranteed in indoor environments since blockages are densely distributed. Deploying RISs to assist mmWave transmission is a promising way to overcome this challenge. In the first paper, we propose an indoor mmWave RIS-aided network model capturing the characteristics of indoor environments. With a given base station (BS) density, whether deploying RISs or increasing BS density to further enhance the network coverage is more cost-effective is investigated. We present a coverage calculation algorithm which can be adapted for different indoor layouts. Then, we jointly analyze the network cost and coverage probability. Our results indicate that deploying RISs with an appropriate number of BSs is more cost-effective for achieving an adequate coverage probability than increasing BSs only. Secondly, for a given total number of passive elements, whether fewer large-scale RISs or more small-scale RISs should be deployed has yet to be investigated in the presence of the blockage effect. In the second paper, we model and analyze a 3D outdoor mmWave RIS-aided network considering both building blockages and human-body blockages. Based on the proposed model, the analytical upper and lower bounds of the coverage probability are derived. Meanwhile, the closed-form coverage probability when RISs are much closer to the UE than the BS is derived. In terms of coverage enhancement, we reveal that sparsely deployed large-scale RISs outperform densely deployed small-scale RISs in scenarios of sparse blockages and/or long transmission distances, while densely deployed small-scale RISs win in scenarios of dense blockages and/or short transmission distances. Finally, building envelope (the exterior wall of a building) makes outdoor mmWave BS difficult to communicate with indoor UE. Transmissive RISs with passive elements have been proposed to refract the signal when the transmitter and receiver are on the different side of the RIS. Similar to reflective RISs, the passive elements of a transmissive RIS can implement phase shifts and adjust the amplitude of the incident signals. By deploying transmissive RISs on the building envelope, it is feasible to implement RIS-aided O2I mmWave networks. In the third paper, we develop a 3D RIS-aided O2I mmWave network model with random indoor blockages. Based on the model, a closed-form coverage probability approximation considering blockage spatial correlation is derived, and multiple-RIS deployment strategies are discussed. For a given total number of RIS passive elements, the impact of blockage density, the number and locations of RISs on the coverage probability is analyzed. All the analytical results have been validated by Monte Carlo simulation. The observations from the result analysis provide guidelines for the future deployment of RIS-aided mmWave networks

    A survey on reconfigurable intelligent surfaces: wireless communication perspective

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    Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS-assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS-assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi-users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed

    Analysis and Design of Algorithms for the Improvement of Non-coherent Massive MIMO based on DMPSK for beyond 5G systems

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    Mención Internacional en el título de doctorNowadays, it is nearly impossible to think of a service that does not rely on wireless communications. By the end of 2022, mobile internet represented a 60% of the total global online traffic. There is an increasing trend both in the number of subscribers and in the traffic handled by each subscriber. Larger data rates, smaller extreme-to-extreme (E2E) delays and greater number of devices are current interests for the development of mobile communications. Furthermore, it is foreseen that these demands should also be fulfilled in scenarios with stringent conditions, such as very fast varying wireless communications channels (either in time or frequency) or scenarios with power constraints, mainly found when the equipment is battery powered. Since most of the wireless communications techniques and standards rely on the fact that the wireless channel is somehow characterized or estimated to be pre or post-compensated in transmission (TX) or reception (RX), there is a clear problem when the channels vary rapidly or the available power is constrained. To estimate the wireless channel and obtain the so-called channel state information (CSI), some of the available resources (either in time, frequency or any other dimension), are utilized by including known signals in the TX and RX typically known as pilots, thus avoiding their use for data transmission. If the channels vary rapidly, they must be estimated many times, which results in a very low data efficiency of the communications link. Also, in case the power is limited or the wireless link distance is large, the resulting signal-tointerference- plus-noise ratio (SINR) will be low, which is a parameter that is directly related to the quality of the channel estimation and the performance of the data reception. This problem is aggravated in massive multiple-input multiple-output (massive MIMO), which is a promising technique for future wireless communications since it can increase the data rates, increase the reliability and cope with a larger number of simultaneous devices. In massive MIMO, the base station (BS) is typically equipped with a large number of antennas that are coordinated. In these scenarios, the channels must be estimated for each antenna (or at least for each user), and thus, the aforementioned problem of channel estimation aggravates. In this context, algorithms and techniques for massive MIMO without CSI are of interest. This thesis main topic is non-coherent massive multiple-input multiple-output (NC-mMIMO) which relies on the use of differential M-ary phase shift keying (DMPSK) and the spatial diversity of the antenna arrays to be able to detect the useful transmitted data without CSI knowledge. On the one hand, hybrid schemes that combine the coherent and non-coherent schemes allowing to get the best of both worlds are proposed. These schemes are based on distributing the resources between non-coherent (NC) and coherent data, utilizing the NC data to estimate the channel without using pilots and use the estimated channel for the coherent data. On the other hand, new constellations and user allocation strategies for the multi-user scenario of NC-mMIMO are proposed. The new constellations are better than the ones in the literature and obtained using artificial intelligence techniques, more concretely evolutionary computation.This work has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant agreement No. 813391. The PhD student was the Early Stage Researcher (ESR) number 2 of the project. This work has also received funding from the Spanish National Project IRENE-EARTH (PID2020-115323RB-C33) (MINECO/AEI/FEDER, UE), which funded the work of some coauthors.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretario: Matilde Pilar Sánchez Fernández.- Vocal: Eva Lagunas Targaron

    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 ( ÎĽm -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

    Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems

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    © 2023 IEEE. This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/10.1109/TVT.2023.3249353.[Abstract]: The combination of multiple-input multiple-output (MIMO) systems and intelligent reflecting surfaces (IRSs) is foreseen as a critical enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system. Both approaches aim to maximize the system sum-rate for every channel realization. The first proposed solution is a novel contextual bandit (CB) framework with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions, and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is appropriately met. Both proposals perform remarkably better than state-of-the-art heuristic methods in scenarios with high multi-user interference.This work has been supported by grants ED431C 2020/15 and ED431G 2019/01 (to support the Centro de Investigación de Galicia “CITIC”) funded by Xunta de Galicia and ERDF Galicia 2014-2020; and by grants PID2019-104958RB-C42 (ADELE) and BES-2017-081955 funded by MCIN/AEI/10.13039/501100011033.Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G 2019/0

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Robust Integrated Data and Energy Transfer Aided by Intelligent Reflecting Surfaces: Successive Target Migration Optimization Towards Energy Sustainability

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    Intelligent reflecting surfaces (IRSs) can actively adjust the wireless environment. However, accurate channel estimation on IRS-aided communication systems is difficult to obtain. Therefore, we study a robust beamforming design for an IRS-aided integrated data and energy transfer (IDET) with imperfect channel state information (CSI). Against the uncertain channel estimation error, we robustly design the transmit beamformers of the transmitter and the passive reflecting beamformer of the IRS to minimize the transmit power by satisfying both the wireless data transfer (WDT) and wireless energy transfer (WET) requirements for realising energy-sustainability in 6G. A successive target migration optimization (STMO) algorithm is proposed to obtain a robust design. The transmit covariance matrices are optimized by relaxing rank-one constraints, when a passive reflecting beamformer is given. Then, the target to minimize the transmit power is migrated to maximize the QoS requirements of energy users due to the fixed transmit power. A local optimal reflecting beamformer is obtained for improving the attainable WET performance, when the transmit covariance matrices are given. Finally, we prove that the rank-one transmit beamformers can always be found, which have the same WET and WDT performance as the transmit covariance matrices. The numerical results demonstrate the advantage of our design
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