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We present a distributed antenna array supporting open-loop distributed beamforming at 1.5 GHz. Based on a scalable, high-accuracy internode ranging technique, we demonstrate open-loop beamforming experiments using three transmitting nodes. To support distributed beamforming without feedback from the destination, the relative positions of the nodes in the distributed array must be known with accuracies below $lambda/15$ of the beamforming carrier frequency to ensure that the array maintains at least 90% coherent beamforming gain at the receive location. For operations in the microwave range, this leads to range estimation accuracies of centimeters or lower. We present a scalable, high-accuracy waveform and new approaches to refine range measurements to significantly improve the estimation accuracy. Using this waveform with a three-node array, we demonstrate high-accuracy ranging simultaneously between multiple nodes, from which phase corrections on two secondary nodes are implemented to maintain beamforming with the primary node, thereby supporting open-loop distributed beamforming. Upon movement of the nodes, the range estimation is used to dynamically update the phase correction, maintaining beamforming as the nodes move. We show the first open-loop distributed beamforming at 1.5 GHz with two-node and three-node arrays, demonstrating the ability to implement and maintain phase-based beamforming without feedback from the destination.
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In future drone applications fast moving unmanned aerial vehicles (UAVs) will need to be connected via a high throughput ultra reliable wireless link. MmWave communication is assumed to be a promising technology for UAV communication, as the narrow beams cause little interference to and from the ground. A challenge for such networks is the beamforming requirement, and the fact that frequent handovers are required as the cells are small. In the UAV communication research community, mobility and especially handovers are often neglected, however when considering beamforming, antenna array sizes start to matter and the effect of azimuth and elevation should be studied, especially their impact on handover rate and outage capacity. This paper aims to fill some of this knowledge gap and to shed some light on the existing problems. This work will analyse the performance of 3D beamforming and handovers for UAV networks through a case study of a realistic 5G deployment using mmWave. We will look at the performance of a UAV flying over a city utilizing a beamformed mmWave link.
This paper is concerned with a bilinear control problem for enhancing convection-cooling via an incompressible velocity field. Both optimal open-loop control and closed-loop feedback control designs are addressed. First and second order optimality conditions for characterizing the optimal solution are discussed. In particular, the method of instantaneous control is applied to establish the feedback laws. Moreover, the construction of feedback laws is also investigated by directly utilizing the optimality system with appropriate numerical discretization schemes. Computationally, it is much easier to implement the closed-loop feedback control than the optimal open-loop control, as the latter requires to solve the state equations forward in time, coupled with the adjoint equations backward in time together with a nonlinear optimality condition. Rigorous analysis and numerical experiments are presented to demonstrate our ideas and validate the efficacy of the control designs.
Wireless networks have been widely deployed for many Internet-of-Things (IoT) applications, like smart cities and precision agriculture. Low Power Wide Area Networking (LPWAN) is an emerging IoT networking paradigm to meet three key requirements of IoT applications, i.e., low cost, large scale deployment and high energy efficiency. Among all available LPWAN technologies, LoRa networking has attracted much attention from both academia and industry, since it specifies an open standard and allows us to build autonomous LPWAN networks without any third-party infrastructure. Many LoRa networks have been developed recently, e.g., managing solar plants in Carson City, Nevada, USA and power monitoring in Lyon and Grenoble, France. However, there are still many research challenges to develop practical LoRa networks, e.g., link coordination, resource allocation, reliable transmissions and security. This article provides a comprehensive survey on LoRa networks, including the technical challenges of deploying LoRa networks and recent solutions. Based on our detailed analysis of current solutions, some open issues of LoRa networking are discussed. The goal of this survey paper is to inspire more works on improving the performance of LoRa networks and enabling more practical deployments.
In this paper, we introduce an open-source model MOVESTAR to calculate the fuel consumption and pollutant emissions of motor vehicles. This model is developed based on U.S. Environmental Protection Agencys (EPA) Motor Vehicle Emission Simulator (MOVES), which provides an accurate estimate of vehicle emissions under a wide range of user-defined conditions. Originally, MOVES requires users to specify many parameters through its software, including vehicle types, time periods, geographical areas, pollutants, vehicle operating characteristics, and road types. In this paper, MOVESTAR is developed as a simplified version, which only takes the second-by-second vehicle speed data and vehicle type as inputs. To enable easy integration of this model, its source code is provided in various languages, including Python, MATLAB and C++. A case study is introduced in this paper to illustrate the effectiveness of the model in the development of advanced vehicle technology.
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