1,089 research outputs found

    Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

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    The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Current issues of the management of socio-economic systems in terms of globalization challenges

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    The authors of the scientific monograph have come to the conclusion that the management of socio-economic systems in the terms of global challenges requires the use of mechanisms to ensure security, optimise the use of resource potential, increase competitiveness, and provide state support to economic entities. Basic research focuses on assessment of economic entities in the terms of global challenges, analysis of the financial system, migration flows, logistics and product exports, territorial development. The research results have been implemented in the different decision-making models in the context of global challenges, strategic planning, financial and food security, education management, information technology and innovation. The results of the study can be used in the developing of directions, programmes and strategies for sustainable development of economic entities and regions, increasing the competitiveness of products and services, decision-making at the level of ministries and agencies that regulate the processes of managing socio-economic systems. The results can also be used by students and young scientists in the educational process and conducting scientific research on the management of socio-economic systems in the terms of global challenges

    Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning

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    Hierarchical Federated Learning (HFL) is a distributed machine learning paradigm tailored for multi-tiered computation architectures, which supports massive access of devices' models simultaneously. To enable efficient HFL, it is crucial to design suitable incentive mechanisms to ensure that devices actively participate in local training. However, there are few studies on incentive mechanism design for HFL. In this paper, we design two-level incentive mechanisms for the HFL with a two-tiered computing structure to encourage the participation of entities in each tier in the HFL training. In the lower-level game, we propose a coalition formation game to joint optimize the edge association and bandwidth allocation problem, and obtain efficient coalition partitions by the proposed preference rule, which can be proven to be stable by exact potential game. In the upper-level game, we design the Stackelberg game algorithm, which not only determines the optimal number of edge aggregations for edge servers to maximize their utility, but also optimize the unit reward provided for the edge aggregation performance to ensure the interests of cloud servers. Furthermore, numerical results indicate that the proposed algorithms can achieve better performance than the benchmark schemes

    Multicriteria Consensus Models to Support Intelligent Group Decision-Making

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    The development of intelligent systems is progressing rapidly, thanks to advances in information technology that enable collective, automated, and effective decision-making based on information collected from diverse sources. Group decision-making (GDM) is a key part of intelligent decision-making (IDM), which has received considerable attention in recent years. IDM through GDM refers to a decision-making problem where a group of intelligent decision-makers (DMs) evaluate a set of alternatives with respect to specific attributes. Intelligent communication among DMs aims to give orders to the available alternatives. However, GDM models developed for IDM must incorporate consensus support models to effectively integrate input from each DM into the final decision. Many efforts have been made to design consensus models to support IDM, depending on the decision problem or environment. Despite promising results, significant gaps remain in research on the design of such support models. One major drawback of existing consensus models is their dependence on the type of decision environment, making them less generalizable. Moreover, these models are often static and cannot respond to dynamic changes in the decision environment. Another limitation is that consensus models for large-scale decision environments lack an efficient communication regime to enable DM interactions. To address these challenges, this dissertation proposes developing consensus models to support IDM through GDM. To address the generalization issue of existing consensus models, reinforcement learning (RL) is proposed. RL agents can be built on the Markov decision process to enable IDM, potentially removing the generalization issue of consensus support models. Contrary to most consensus models, which assume static decision environments, this dissertation proposes a computationally efficient dynamic consensus model to support dynamic IDM. Finally, to facilitate secure and efficient interactions among intelligent DMs in large-scale problems, Blockchain technology is proposed to speed up the consensus process. The proposed communication regime also includes trust-building mechanisms that employ Blockchain protocols to remove enduring and limitative assumptions on opinion similarity among agents

    Model Development and Investigations on Ion Homeostasis

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    The environment surrounding an organism, a cell and an organelle is constantly changing. To keep organisms functioning there is an everlasting need to regulate and adapt in order to keep the internal environment relatively constant. Homeostasis is the term used to describe this ability of a system to regulate and stabilize its environment. Different processes and compensatory mechanisms are employed to do this. Homeostasis is also the overall theme binding this thesis together, spanning from iron regulation in plants to the regulation of calcium (Ca2+) in humans. Ever since the term emerged, scientists have been searching for answers on how biological control mechanisms function and how they are able to maintain homeostasis. The work presented in this thesis is based on a computational approach using systems biology and control mechanisms like negative feedback and integral control. Controller motifs based on negative feedback loops between a controlled and manipulated/compensatory variable was previously identified by the research group, and has been used as a basis for the computational calculations and models. Plants need iron for their growth and development, and even though this essential nutrient is difficult to access through the soil due to its availability. In the soil iron is strongly bound as Fe2O3, and plants have developed different strategies for iron uptake. Iron is also of great importance for human nutrition. Iron deficiency is one of the major causes of anaemia. Anaemia is a world wide problem and is a condition with too few red bloods cells or where the haemoglobin level within these is lower than usual. Iron regulation and homeostasis was modeled for non-graminaceous plants, with Arabidopsis thaliana as a model species. Since iron is toxic for plants at high levels it needs to be under homeostatic control. A model in agreement with experimental observations was developed. Iron-dependent degradation of the high-affinity transporter IRT1 was included in agreement with experimental findings, as well as the importance of the transcription factor FIT for the regulation of cytosolic iron. Auxiliary feedback was also introduced and investigated in the model. The role of such feedback is to help improve adaptation kinetics without an influence to the set-point, resulting in a significant improvement of the system response time. Homeostasis was also explored in order to see whether oscillatory conditions, which are common in biological systems, could show robust homeostasis. Homeostatic oscillators were identified, where compensatory frequency or amplitude levels lead to the average level corresponding to the set-point. This indicates that even during sustained oscillatory conditions homeostasis can be observed, suggesting an extension of the concept. Frequency control with the frequency being homeostatically regulated have also been described by us. Cytosolic calcium (Ca2+) is a biological example of one of these conditions where oscillations, transients etc. take place even though Ca2+ is under strict homeostatic control. Dysregulation of cytosolic Ca2+ is critical as it will affect cellular signaling and promote apoptosis at high levels. A simple initial model of oscillating Ca2+ regulation was used as an example of oscillatory homeostats, which spiked the interest to investigate Ca2+ homeostasis on a cellular level. Thus started the approach on building a model on cytosolic Ca2+ homeostasis and regulatory mechanisms in non-excitable cells. The work was started from an initial simple model based on erythrocytes with few organelles by studying the inflow and outflow mechanisms through the plasma membrane. Hysteretic properties in the plasma membrane Ca2+ ATPase (PMCA) was studied and identified, and compared well with experimental results. We also suggest that the inflow of Ca2+ could be inhibited by carboxyeosin which was used as an inhibitor in experimental research based on model calculations fitting well with these. For the Ca2+ induced Ca2+ release mechanism through the inositol 1,4,5-trisphosphate receptor (IP3R) a dicalcic model has been presented. Comparing theoretical calculations with experimental bell-shaped curves of the Ca2+ dependency of the IP3R channel at different IP3 levels, a cooperativity of 2 has been suggested in the inhibition by Ca2+. Cooperativity in the capacitative Ca2+ entry was also investigated and compared to experiments. Finally, even though oscillations was not the focus of this latest project, the cellular model can show sustained Ca2+ oscillations with period length ranging from a few seconds up to 30 hours

    Pre-hospital trauma assessment and management of older patients and their association with patient outcomes: challenges and barriers

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    BACKGROUND: Saudi Arabia faces an increasing prehospital healthcare burden from older people with injuries, but little is known about their characteristics and current treatment. METHODS: This was a sequential explanatory mixed-methods design, preceded by a scoping review on the prehospital geriatric trauma care. A retrospective quantitative study was conducted using registry data from older patients (≥55 years) admitted by ambulances from 01/08/2017 to 31/10/2021 at a major trauma centre in Saudi Arabia. A qualitative study was conducted using a purposive sample of Saudi paramedics and ambulance technicians from Riyadh and Makkah using online semi-structured interviews and analysed using the framework method. The quantitative and qualitative findings were integrated. RESULTS: The quantitative study recruited 452 eligible cases and found most of them were admitted with low falls (53.7%), normal physiology, and extremities injuries (53.1%). The study identified no significant predictors of in-hospital death (p>0.05 for all predictors), although statistical power was limited. The qualitative study recruited twenty participants and identified that they reported age-related challenges including physiological changes, polypharmacy, and communication difficulties. They all wanted training and guidelines to improve their knowledge. They reported struggling with communication difficulties, inaccurate adverse outcomes predictions, difficult intravenous cannulations, and cultural restrictions affecting care provision for female patients. I identified organisational barriers (e.g. lack of shared patient records and lack of guidelines) and cultural barriers (e.g. barriers to assessing women, attitudes towards older people, and attitudes towards paramedics) that influenced implementation of knowledge. This study also found that the participants' perceptions aligned with the retrospective study’s cohort, and they acknowledged the difficulty of predicting death in older trauma patients. CONCLUSION: Ambulance clinicians in Saudi Arabia want guidelines and training in managing older trauma patients but these need to take into account the characteristics of older trauma patients and the cultural barriers that I identified

    FlipDyn with Control: Resource Takeover Games with Dynamics

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    We present the FlipDyn, a dynamic game in which two opponents (a defender and an adversary) choose strategies to optimally takeover a resource that involves a dynamical system. At any time instant, each player can take over the resource and thereby control the dynamical system after incurring a state-dependent and a control-dependent costs. The resulting model becomes a hybrid dynamical system where the discrete state (FlipDyn state) determines which player is in control of the resource. Our objective is to compute the Nash equilibria of this dynamic zero-sum game. Our contributions are four-fold. First, for any non-negative costs, we present analytical expressions for the saddle-point value of the FlipDyn game, along with the corresponding Nash equilibrium (NE) takeover strategies. Second, for continuous state, linear dynamical systems with quadratic costs, we establish sufficient conditions under which the game admits a NE in the space of linear state-feedback policies. Third, for scalar dynamical systems with quadratic costs, we derive the NE takeover strategies and saddle-point values independent of the continuous state of the dynamical system. Fourth and finally, for higher dimensional linear dynamical systems with quadratic costs, we derive approximate NE takeover strategies and control policies which enable the computation of bounds on the value functions of the game in each takeover state. We illustrate our findings through a numerical study involving the control of a linear dynamical system in the presence of an adversary.Comment: 17 Pages, 2 figures. Under review at IEEE TA

    Machine economies

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    peer reviewedThis fundamentals article discusses efficient machine economies in which non-human agents can autonomously exchange information and value. We first identify criteria for achieving Pareto efficiency in such economies by drawing on the Coase Theorem. We then translate these economic criteria to technical requirements before developing a framework that characterizes four types of machine economies. We discuss real-life examples for each type to highlight key challenges in achieving Pareto efficiency. In particular, we highlight that machine economies with human involvement in economic interactions and governance face significant challenges regarding perfect information, rationality, and transaction costs. Machine economies without human involvement, in turn, promise a high degree of Pareto efficiency, but there are still many open questions, particularly regarding machine-enforced governance. We conclude with opportunities for future research on the interactions and governance in machine economies

    Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing

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    Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling.Comment: 37 pages, 9 figures, accepted for publication in the IEEE Transactions on Wireless Communication
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