Distributed Massive MIMO for LEO Satellite Networks

The ultra-dense deployment of interconnected satellites will characterize future low Earth orbit (LEO) mega-constellations. Exploiting this towards a more efficient satellite network (SatNet), this paper proposes a novel LEO SatNet architecture based on distributed massive multiple-input multiple-output (DM-MIMO) technology allowing ground user terminals to be connected to a cluster of satellites. To this end, we investigate various aspects of DM-MIMO-based satellite network design, the benefits of using this architecture, the associated challenges, and the potential solutions. In addition, we propose a distributed joint power allocation and handover management (D-JPAHM) technique that jointly optimizes the power allocation and handover management processes in a cross-layer manner. This framework aims to maximize the network throughput and minimize the handover rate while considering the quality-of-service (QoS) demands of user terminals and the power capabilities of the satellites. Moreover, we devise an artificial intelligence (AI)-based solution to efficiently implement the proposed D-JPAHM framework in a manner suitable for real-time operation and the dynamic SatNet environment. To the best of our knowledge, this is the first work to introduce and study DM-MIMO technology in LEO SatNets. Extensive simulation results reveal the superiority of the proposed architecture and solutions compared to conventional approaches in the literature.Comment: arXiv admin note: text overlap with arXiv:2106.0983

Reconfigurable Intelligent Surfaces for the Connectivity of Autonomous Vehicles

The use of real-time software-controlled reconfigurable intelligent surface (RIS) units is proposed to increase the reliability of vehicle-to-everything (V2X) communications. The optimum placement problem of the RIS units is formulated by considering their sizes and operating modes. The solution of the problem is given, where it is shown that the placement of the RIS depends on the locations of the transmitter and the receiver. The proposed RIS-supported highway deployment can combat the high path loss experienced by the use of higher frequency bands, including the millimeter-wave and the terahertz bands, that are expected to be used in the next-generation wireless networks, enabling the use of the existing base station deployment plans to remain operational, while providing reliable and energy-efficient connectivity for autonomous vehicles.Comment: 5 pages, 4 figures

Future Ultra-Dense LEO Satellite Networks: A Cell-Free Massive MIMO Approach

Low Earth orbit (LEO) satellite networks (SatNets) are envisioned to play a crucial role in providing global and ubiquitous connectivity efficiently. Accordingly, in the coming years, thousands of LEO satellites will be launched to create ultradense LEO mega-constellations, and the Third Generation Partnership Project (3GPP) is working on evolving fifth-generation (5G) systems to support such non-terrestrial networks (NTN). However, many challenges are associated with the deployment of LEOs from communications and networking perspectives. In this paper, we propose a novel cell-free massive multiple-input multiple-output (CF-mMIMO) based architecture for future ultra-dense LEO SatNets. We discuss various aspects of network design, such as duplexing mode, pilot assignment, beamforming, and handover management. In addition, we propose a joint optimization framework for the power allocation and handover management processes to maximize the network throughput and minimize the handover rate while ensuring quality-of-service (QoS) satisfaction for users. To the best of our knowledge, this is the first work to introduce and study CF-mMIMO-based LEO SatNets. Extensive simulation results demonstrate the superior performance of the proposed architecture and solutions compared to those of conventional single-satellite connectivity and handover techniques from the literature.Comment: 6 pages, 3 figure

Exciting Complexity: The Role of Motor Circuit Elements in ALS Pathophysiology

Amyotrophic lateral sclerosis (ALS) is a fatal disease, characterized by the degeneration of both upper and lower motor neurons. Despite decades of research, we still to date lack a cure or disease modifying treatment, emphasizing the need for a much-improved insight into disease mechanisms and cell type vulnerability. Altered neuronal excitability is a common phenomenon reported in ALS patients, as well as in animal models of the disease, but the cellular and circuit processes involved, as well as the causal relevance of those observations to molecular alterations and final cell death, remain poorly understood. Here, we review evidence from clinical studies, cell type-specific electrophysiology, genetic manipulations and molecular characterizations in animal models and culture experiments, which argue for a causal involvement of complex alterations of structure, function and connectivity of different neuronal subtypes within the cortical and spinal cord motor circuitries. We also summarize the current knowledge regarding the detrimental role of astrocytes and reassess the frequently proposed hypothesis of glutamate-mediated excitotoxicity with respect to changes in neuronal excitability. Together, these findings suggest multifaceted cell type-, brain area- and disease stage- specific disturbances of the excitation/inhibition balance as a cardinal aspect of ALS pathophysiology

Registration-free Face-SSD: Single shot analysis of smiles, facial attributes, and affect in the wild

In this paper, we present a novel single shot face-related task analysis method, called Face-SSD, for detecting faces and for performing various face-related (classification/regression) tasks including smile recognition, face attribute prediction and valence-arousal estimation in the wild. Face-SSD uses a Fully Convolutional Neural Network (FCNN) to detect multiple faces of different sizes and recognise/regress one or more face-related classes. Face-SSD has two parallel branches that share the same low-level filters, one branch dealing with face detection and the other one with face analysis tasks. The outputs of both branches are spatially aligned heatmaps that are produced in parallel - therefore Face-SSD does not require that face detection, facial region extraction, size normalisation, and facial region processing are performed in subsequent steps. Our contributions are threefold: 1) Face-SSD is the first network to perform face analysis without relying on pre-processing such as face detection and registration in advance - Face-SSD is a simple and a single FCNN architecture simultaneously performing face detection and face-related task analysis - those are conventionally treated as separate consecutive tasks; 2) Face-SSD is a generalised architecture that is applicable for various face analysis tasks without modifying the network structure - this is in contrast to designing task-specific architectures; and 3) Face-SSD achieves real-time performance (21 FPS) even when detecting multiple faces and recognising multiple classes in a given image. Experimental results show that Face-SSD achieves state-of-the-art performance in various face analysis tasks by reaching a recognition accuracy of 95.76% for smile detection, 90.29% for attribute prediction, and Root Mean Square (RMS) error of 0.44 and 0.39 for valence and arousal estimation

Unmanned aircraft systems airspace integration: A game theoretical framework for concept evaluations

The focus of this paper is to present a game theoretical modeling framework for the integration of unmanned aircraft systems into the National Airspace System. The problem of predicting the outcome of complex scenarios, where manned and unmanned air vehicles coexist, is the research problem of this work. The fundamental gap in the literature is that the models of interaction between manned and unmanned vehicles are insufficient: 1) They assume that pilot behavior is known a priori, and 2) They disregard pilot reaction and the decision-making process. The contribution of this paper is to propose a realistic modeling framework that will fill this gap. The foundations of the proposed method are formedbygame theory, which investigates strategic decision makingbetween intelligent agents; bounded rationality concept, which is based on the fact that humans cannot always make perfect decisions; and reinforcement learning, which is shown to be effective in human learning in psychology literature. An analysis of integration is conducted using an example scenario in the presence of manned aircraft and fully autonomous unmanned aircraft systems equipped with sense-and-avoid algorithms. © Copyright 2016 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved

Effect of arsenic-phosphorus interaction on arsenic-induced oxidative stress in chickpea plants

Arsenic-induced oxidative stress in chickpea was investigated under glasshouse conditions in response to application of arsenic and phosphorus. Three levels of arsenic (0, 30 and 60 mg kg−1) and four levels of P (50, 100, 200, and 400 mg kg−1) were applied to soil-grown plants. Increasing levels of both arsenic and P significantly increased arsenic concentrations in the plants. Shoot growth was reduced with increased arsenic supply regardless of applied P levels. Applied arsenic induced oxidative stress in the plants, and the concentrations of H2O2 and lipid peroxidation were increased. Activity of superoxide dismutase (SOD) and concentrations of non-enzymatic antioxidants decreased in these plants, but activities of catalase (CAT) and ascorbate peroxidase (APX) were significantly increased under arsenic phytotoxicity. Increased supply of P decreased activities of CAT and APX, and decreased concentrations of non-enzymatic antioxidants, but the high-P plants had lowered lipid peroxidation. It can be concluded that P increased uptake of arsenic from the soil, probably by making it more available, but although plant growth was inhibited by arsenic the P may have partially protected the membranes from arsenic-induced oxidative stress

A game theoretical modeling and simulation framework for the integration of unmanned aircraft systems in to the national airspace

The focus of this paper is to present a game theoretical modeling and simulation frame- work for the integration of Unmanned Aircraft Systems (UAS) into the National Airspace system (NAS). The problem of predicting the outcome of complex scenarios, where UAS and manned air vehicles co-exist, is the research problem of this work. The fundamental gap in the literature in terms of developing models for UAS integration into NAS is that the models of interaction between manned and unmanned vehicles are insufficient. These models are insufficient because a) they assume that human behavior is known a priori and b) they disregard human reaction and decision making process. The contribution of this paper is proposing a realistic modeling and simulation framework that will fill this gap in the literature. The foundations of the proposed modeling method is formed by game theory, which analyzes strategic decision making between intelligent agents, bounded rationality concept, which is based on the fact that humans cannot always make perfect decisions, and reinforcement learning, which is shown to be effective in human behavior in psychology literature. These concepts are used to develop a simulator which can be used to obtain the outcomes of scenarios consisting of UAS, manned vehicles, automation and their interactions. An analysis of the UAS integration is done with a specifically designed scenario for this paper. In the scenario, a UAS equipped with sense and avoid algorithm, moves along a predefined trajectory in a crowded airspace. Then the effect of various system parameters on the safety and performance of the overall system is investigated. © 2016, American Institute of Aeronautics and Astronautics. All rights reserved