1,293 research outputs found

    Active RIS Assisted Rate-Splitting Multiple Access Network: Spectral and Energy Efficiency Tradeoff

    Get PDF
    With the increasing demand of high data rate and massive access in both ultra-dense and industrial Internet-of-things networks, spectral efficiency (SE) and energy efficiency (EE) are regarded as two important and inter-related performance metrics for future networks. In this paper, we investigate a novel integration of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) into cellular systems to achieve a desirable tradeoff between SE and EE. Different from the commonly used passive RIS, we adopt reflection elements with active load to improve a newly defined metric, called resource efficiency (RE), which is capable of striking a balance between SE and EE. This paper focuses on the RE optimization by jointly designing the base station (BS) transmit precoding and RIS beamforming (BF) while guaranteeing the transmit and forward power budgets of the BS and RIS, respectively. To efficiently tackle the challenges for solving the RE maximization problem due to its fractional objective function, coupled optimization variables, and discrete coefficient constraint, the formulated nonconvex problem is solved by proposing a two-stage optimization framework. For the outer stage problem, a quadratic transformation is used to recast the fractional objective into a linear form, and a closed-form solution is obtained by using auxiliary variables. For the inner stage problem, the system sum rate is approximated into a linear function. Then, an alternating optimization (AO) algorithm is proposed to optimize the BS precoding and RIS BF iteratively, by utilizing the penalty dual decomposition (PDD) method. Simulation results demonstrate the superiority of the proposed design compared to other benchmarks

    Beam scanning by liquid-crystal biasing in a modified SIW structure

    Get PDF
    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Synthetic Aperture Radar (SAR) Meets Deep Learning

    Get PDF
    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications

    Get PDF
    This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration. Furthermore, a conventional LMA codebook with codewords uniformly distributed on a hypersphere may not be channel-adaptive and may lead to increased signal detection complexity. In this paper, we conceive an MU-LMA system employing a full-array structured (FAS) transmitter and propose two algorithms accordingly. The proposed FAS-based system addresses the SAS structural problems and can support larger numbers of users. For LMAimposed constant-power downlink precoding, we propose an FASbased normalized block diagonalization (FAS-NBD) algorithm. However, the forced normalization may result in performance degradation. This degradation, together with the aforementioned codebook design problems, is difficult to solve analytically. This motivates us to propose a Deep Learning-enhanced (FAS-DLNBD) algorithm for adaptive codebook design and codebookindependent decoding. It is shown that the proposed algorithms are robust to imperfect knowledge of channel state information and yield excellent error performance. Moreover, the FAS-DLNBD algorithm enables signal detection with low complexity as the number of bits per codeword increases

    Rate-splitting multiple access for non-terrestrial communication and sensing networks

    Get PDF
    Rate-splitting multiple access (RSMA) has emerged as a powerful and flexible non-orthogonal transmission, multiple access (MA) and interference management scheme for future wireless networks. This thesis is concerned with the application of RSMA to non-terrestrial communication and sensing networks. Various scenarios and algorithms are presented and evaluated. First, we investigate a novel multigroup/multibeam multicast beamforming strategy based on RSMA in both terrestrial multigroup multicast and multibeam satellite systems with imperfect channel state information at the transmitter (CSIT). The max-min fairness (MMF)-degree of freedom (DoF) of RSMA is derived and shown to provide gains compared with the conventional strategy. The MMF beamforming optimization problem is formulated and solved using the weighted minimum mean square error (WMMSE) algorithm. Physical layer design and link-level simulations are also investigated. RSMA is demonstrated to be very promising for multigroup multicast and multibeam satellite systems taking into account CSIT uncertainty and practical challenges in multibeam satellite systems. Next, we extend the scope of research from multibeam satellite systems to satellite- terrestrial integrated networks (STINs). Two RSMA-based STIN schemes are investigated, namely the coordinated scheme relying on CSI sharing and the co- operative scheme relying on CSI and data sharing. Joint beamforming algorithms are proposed based on the successive convex approximation (SCA) approach to optimize the beamforming to achieve MMF amongst all users. The effectiveness and robustness of the proposed RSMA schemes for STINs are demonstrated. Finally, we consider RSMA for a multi-antenna integrated sensing and communications (ISAC) system, which simultaneously serves multiple communication users and estimates the parameters of a moving target. Simulation results demonstrate that RSMA is beneficial to both terrestrial and multibeam satellite ISAC systems by evaluating the trade-off between communication MMF rate and sensing Cramer-Rao bound (CRB).Open Acces

    Security and Privacy for Modern Wireless Communication Systems

    Get PDF
    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

    Model-Predictive Control in Communication Networks

    Get PDF
    This dissertation consists of 8 papers, separated into 3 groups. The first 3 papers show, how model-predictive control can be applied to queueing networks and contain a detailed proof of throughput optimality. Additionally, numerous network examples are discussed, and a connection between the stability properties of assembly queues and random walks on quotient spaces is established. The next two papers develop algorithms, with which robust forecasts of delay can be obtained in queueing networks. To that end, a notion of robustness is proposed, and the network control policy is designed to meet this goal. For the last 3 papers, focus is shifted towards Age-of-Information. Two main contributions are the derivation of the distribution of the Age-of-Information values in networks with clocked working cycles and an algorithm for the exact numerical evaluation of the Age-of-Information state-space in a similar set-up

    BLE-based Indoor Localization and Contact Tracing Approaches

    Get PDF
    Internet of Things (IoT) has penetrated different aspects of modern life with smart sensors being prevalent within our surrounding indoor environments. Furthermore, dependence on IoT-based Contact Tracing (CT) models has significantly increased mainly due to the COVID-19 pandemic. There is, therefore, an urgent quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions leveraging accurate indoor localization/tracking approaches. In this context, the first objective of this Ph.D. thesis is to enhance accuracy of Bluetooth Low Energy (BLE)-based indoor localization. BLE-based localization is typically performed based on the Received Signal Strength Indicator (RSSI). Extreme fluctuations of the RSSI occurring due to different factors such as multi-path effects and noise, however, prevent the BLE technology to be a reliable solution with acceptable accuracy for dynamic tracking/localization in indoor environments. In this regard, first, an IoT dataset is constructed based on multiple thoroughly separated indoor environments to incorporate the effects of various interferences faced in different spaces. The constructed dataset is then used to develop a Reinforcement Learning (RL)-based information fusion strategy to form a multiple-model implementation consisting of RSSI, Pedestrian dead reckoning (PDR), and Angle-of-Arrival (AoA)-based models. In the second part of the thesis, the focus is devoted to application of multi-agent Deep Neural Networks (DNN) models for indoor tracking. DNN-based approaches are, however, prone to overfitting and high sensitivity to parameter selection, which results in sample inefficiency. Moreover, data labelling is a time-consuming and costly procedure. To address these issues, we leverage Successor Representations (SR)-based techniques, which can learn the expected discounted future state occupancy, and the immediate reward of each state. A Deep Multi-Agent Successor Representation framework is proposed that can adapt quickly to the changes in a multi-agent environment faster than the Model-Free (MF) RL methods and with a lower computational cost compared to Model-Based (MB) RL algorithms. In the third part of the thesis, the developed indoor localization techniques are utilized to design a novel indoor CT solution, referred to as the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) approach coupled with a Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms

    Specificity of the innate immune responses to different classes of non-tuberculous mycobacteria

    Get PDF
    Mycobacterium avium is the most common nontuberculous mycobacterium (NTM) species causing infectious disease. Here, we characterized a M. avium infection model in zebrafish larvae, and compared it to M. marinum infection, a model of tuberculosis. M. avium bacteria are efficiently phagocytosed and frequently induce granuloma-like structures in zebrafish larvae. Although macrophages can respond to both mycobacterial infections, their migration speed is faster in infections caused by M. marinum. Tlr2 is conservatively involved in most aspects of the defense against both mycobacterial infections. However, Tlr2 has a function in the migration speed of macrophages and neutrophils to infection sites with M. marinum that is not observed with M. avium. Using RNAseq analysis, we found a distinct transcriptome response in cytokine-cytokine receptor interaction for M. avium and M. marinum infection. In addition, we found differences in gene expression in metabolic pathways, phagosome formation, matrix remodeling, and apoptosis in response to these mycobacterial infections. In conclusion, we characterized a new M. avium infection model in zebrafish that can be further used in studying pathological mechanisms for NTM-caused diseases
    • …
    corecore