Ground Terminals Research Articles

UAV-Mounted Mobile Base Station Placement via Sparse Recovery

In order to deploy minimum number of unmanned aerial vehicle (UAV)-mounted mobile base stations (MBSs) to service all given ground terminals, this paper proposes an MBS placement based on sparse recovery (MBS-PBSR) algorithm. By exploiting the sparsity inherent in the differences between any two dedicated MBSs, the problem of UAV-mounted MBS placement could be formulated as an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm constrained optimization problem, which is then be solved by the reweighted ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm method. Subsequently, the resulted solutions to the MBS placement are adjusted by the iterative redundant circle deletion algorithm, eventually leading to the redundant MBSs removal as much as possible. Simulation results demonstrate that our proposed MBS-PBSR algorithm works well with affordable computational complexity, and is nearly optimum in the sense of the number of deployed UAV-mounted MBSs.

MOBILE SATELLITE COMMUNICATION SYSTEM WITH PASSIVE TERMINALS BASED ON MODULATING RETRO-REFLECTORS

In this work, a qualitative and quantitative comparison of satellite mobile communications systems with various altitudes of the orbits of spacecraft was carried out. A study of the conditions for the spacecraft to be located in the visibility range of the ground terminal and an analysis of the time spent by the satellite in the visibility zone depending on the height of the orbit was made. According to the results of this analysis and taking into account the fact that the low orbit area around the Earth is very densely filled with artificial satellites and space debris, it can be concluded that it is advisable to increase the radius of the orbit of telecommunication satellites. At the same time the geostationary orbit, which has obvious advantages in terms of coverage, is also quite densely filled with spacecraft, and is very high for the efficient operation of low-power mobile ground terminals. Taking into account these features, it is concluded that the use of medium-height orbits for telecommunication satellite systems is promising for which global coverage of the Earth’s surface can be achieved using several dozen satellites. In addition, the distance to the Earth’s surface will be small enough to provide a high signal-to-noise ratio and, as a result, the system will provide a high bitrate. A computer simulation of the operation of a satellite telecommunication system was carried out for a different number of spacecraft and various parameters of their orbits in order to determine the conditions for achieving global coverage. This simulation confirms the conclusions regarding the feasibility of using medium orbits. The concept of using passive terminals based on modulating retroreflectors was proposed. It means the reflection of a satellite signal by a ground terminal without emitting its own electromagnetic energy. The energy of the terminals is spent only on the modulation of the reflected signal. This approach will significantly reduce the energy consumption of ground terminals and thereby increase mobility. It is proposed to carry out phase and polarization modulation of the signal using liquid crystals or metamaterials.

Efficient Local Map Search Algorithms for the Placement of Flying Relays

This paper studies the optimal unmanned aerial vehicle (UAV) placement problem for wireless networking. The UAV operates as a flying wireless relay to provide coverage extension for a base station (BS) and deliver capacity boost to a user shadowed by obstacles. While existing methods rely on statistical models for potential blockage of a direct propagation link, we propose an approach capable of leveraging local terrain information to offer performance guarantees. The proposed method allows to strike the best trade-off between minimizing propagation distances to ground terminals and discovering good propagation conditions. The algorithm only requires several propagation parameters, but it is capable to avoid deep propagation shadows and is proven to find the globally optimal UAV position. Only a local exploration over the target area is required, and the maximum length of search trajectory is linear to the geographical scale. Hence, it lends itself to online search. Significant throughput gains are found when compared to other positioning approaches based on statistical propagation models.

Joint Attitude and Power Optimization for UAV-Aided Downlink Communications

In this paper, we investigate the unmanned aerial vehicle (UAV)-aided communications, where a UAV as an aerial base station (BS) transmits data to multiple ground terminals (GTs) simultaneously and an antenna array is equipped on the UAV. Note that in practice, the spatial resolution of antenna array varies with different directions. Thus, given one user distribution, the direction of antenna array on UAV may be optimized to support the best multiuser spatial separation. Based on this observation, we propose to maximize the minimum throughput of all GTs by jointly optimizing the attitude of UAV and the transmit power for each GT, where the attitude includes both location and direction information. We develop two efficient sub-optimal solutions for this non-convex problem. The interference among the co-scheduled GTs is dramatically reduced through direction adjustment of antenna array. Finally, simulation results are provided to verify the proposed algorithms.

Multi-UAV Dynamic Wireless Networking With Deep Reinforcement Learning

This letter investigates a novel unmanned aerial vehicle (UAV)-enabled wireless communication system, where multiple UAVs transmit information to multiple ground terminals (GTs). We study how the UAVs can optimally employ their mobility to maximize the real-time downlink capacity while covering all GTs. The system capacity is characterized, by optimizing the UAV locations subject to the coverage constraint. We formula the UAV movement problem as a Constrained Markov Decision Process (CMDP) problem and employ Q-learning to solve the UAV movement problem. Since the state of the UAV movement problem has large dimensions, we propose Dueling Deep Q-network (DDQN) algorithm which introduces neural networks and dueling structure into Q-learning. Simulation results demonstrate the proposed movement algorithm is able to track the movement of GTs and obtains real-time optimal capacity, subject to coverage constraint.

Joint Trajectory-Resource Optimization in UAV-Enabled Edge-Cloud System With Virtualized Mobile Clone

This article studies an unmanned aerial vehicle (UAV)-enabled edge-cloud system, where UAV acts as a mobile edge computing (MEC) server interplaying with remote central cloud to provide computation services to ground terminals (GTs). The UAV-enabled edge-cloud system implements a virtualized network function, namely, mobile clone (MC), for each GT to help execute their offloaded tasks. Through such network function virtualization (NFV) implemented on top of the UAV-enabled edge-cloud system, GTs can have extended computation capability and prolonged battery lifetime. We aim to jointly optimize the allocation of resource and the UAV trajectory in the 3-D spaces to minimize the overall energy consumption of the UAV. The proposed solution, therefore, can extend the endurance of the UAV and support reliable MC functions for GTs. This article solves the complicated optimization problem through a block coordinate descent algorithm in an iterative way. In each iteration, the allocation of resource is modeled as a multiple constrained optimization problem given predefined UAV trajectory, which can be reformulated into a more tractable convex form and solved by successive convex optimization and Lagrange duality. Second, given the allocated resource, the optimization of the trajectory of rotary-wing/fixed-wing UAV can be formulated into a series of convex quadratically constrained quadratically program (QCQP) problems and solved by the standard convex optimization techniques. After the block coordinate descent algorithm converges to a prescribed accuracy, a high-quality suboptimal solution can be found. According to the simulation, the numerical results verify the effectiveness of our proposed solution in contrast to the baseline solutions.

UAV-Enabled Data Collection: Multiple Access, Trajectory Optimization, and Energy Trade-Off

In this paper, we consider a ground terminal (GT) to an unmanned aerial vehicle (UAV) wireless communication system where data from GTs are collected by an unmanned aerial vehicle. We propose to use the ground terminal-UAV (G-U) region for the energy consumption model. In particular, to fulfill the data collection task with a minimum energy both of the GTs and UAV, an algorithm that combines optimal trajectory design and resource allocation scheme is proposed which is supposed to solve the optimization problem approximately. We initialize the UAV’s trajectory firstly. Then, the optimal UAV trajectory and GT’s resource allocation are obtained by using the successive convex optimization and Lagrange duality. Moreover, we come up with an efficient algorithm aimed to find an approximate solution by jointly optimizing trajectory and resource allocation. Numerical results show that the proposed solution is efficient. Compared with the benchmark scheme which did not adopt optimizing trajectory, the solution we propose engenders significant performance in energy efficiency.

Grading of electric field distribution of AC polymeric outdoor insulators using field grading material

In this work, composite materials with nonlinear conductivity are used to enable grading of the electric field distribution along polymeric outdoor insulators, particularly near the high-voltage and ground terminals. A ZnO spherical microvaristor/carbon fiber (CF)/ silicone rubber (SR) composite is used as the field grading material (FGM). Finite element method-based numerical simulations are performed to analyze the electric field distributions of 220 and 500 kV composite insulators when the nonlinear conductivity type composite material is used. The 220 and 500 kV composite insulators are fabricated based on the results of the simulations and optical electric field sensors are used to measure their electric field distributions under normal operating conditions. The electric field distribution along the composite insulators are shown to be improved through use of the FGM, and the simulation results are consistent with the experimental results. These graded electric field distributions may reduce the degradation of the polymer materials used in composite insulators and extend their lifetimes. In addition, the dry flashover voltages of insulators with FGMs are higher than those of traditional insulators.

How to Deploy Multiple UAVs for Providing Communication Service in an Unknown Region?

It is an effective way to deploy unmanned aerial vehicle (UAV)-mounted base stations for keeping ground communication in an unknown region when the ground communication facility is unavailable for some reason. This letter investigates the joint placement and power allocation problem of multiple UAVs for ground terminals’ (GTs) communication in an unknown region. In the beginning, UAVs have no information of GTs, which results in that UAVs have to keep flying for information gathering until find the optimal locations. In practice, however, imperfect channel state information (CSI) is obtained by UAVs and only neighboring UAVs can communicate with each other. To overcome these challenges, we leverage game theory to model this problem and propose a robust and distributed learning algorithm to converge to a stochastic stable state of maximizing the optimization objective, i.e., the sum of $\alpha $ -fairness of all GTs.

Impact of atmospheric turbulence and weather conditions on the performance of satellite-to-ground optical communication system based on multiaperture coherent receivers

For satellite-to-ground downlink optical communication systems, it is desirable to reduce the size, weight, and power profiles of the space terminals by employing high sensitivity and large collection area ground terminals. Multiaperture coherent receivers utilizing spatial diversity techniques can satisfy this requirement and have many advantages over the single large aperture coherent receivers. The influence of both atmospheric turbulence and weather conditions on the bit error rate performance of multiaperture coherent receivers utilizing maximum ratio combining, equal gain combining, and selection combining techniques is systematically investigated and compared for the satellite-to-ground downlink communication system. The results are useful for the design and adaptive optimization of the multiaperture coherent receiver-based downlink communication systems.

Joint Power, Altitude, Location and Bandwidth Optimization for UAV With Underlaid D2D Communications

In this letter, we aim to maximize the rate of a device-to-device (D2D) pair for a downlink unmanned aerial vehicle (UAV)-aided wireless communication system, where D2D users coexist in an underlaying manner. We jointly optimize the transmit power of the UAV and D2D users, the flying altitude and location of the UAV and ground terminals’ allocated bandwidth. To solve this problem, an iterative algorithm with low complexity is accordingly proposed. Simulation results show that the altitude of the UAV has an important impact on the system performance.

Joint Trajectory and Communication Design for Secure UAV Networks

This letter investigates a joint optimization problem of unmanned aerial vehicle (UAV) flight trajectory, downlink transmission power, and ground terminals (GTs) association under wiretap channels. Specifically, we consider a scenario where a UAV serves a group of GTs and maximizes the minimum secrecy rate to ensure the fairness among GTs. To solve the nonconvex optimization problem, we develop an iterative algorithm based on the successive convex approximation and alternating methods. The simulation results demonstrate that the proposed algorithm is effective and can significantly improve the minimum secrecy rate compared with the traditional flight scheme.

The multi-objective deployment optimization of UAV-mounted cache-enabled base stations

The deployment of unmanned aerial vehicle (UAV)-mounted base stations is emerging as an effective solution for providing wireless communication service to ground terminals (GTs) which have failed to be associated with ground base stations for some reason. Meanwhile, with the propose of reducing the transmission latency and easing the load of backhaul links between UAVs and the core network, UAVs are equipped with the ability of caching popular contents in the storage of base stations. In this paper, we investigate the efficient deployment problem of UAVs (such as transmitting power, number of UAVs, locations and caching) while guaranteeing the quality of service requirements. In this case, the UAV plays the role of a coordinator to provide high-quality communication service for GTs as well as maximize the benefit of caching. However, there exists an intractable issue that UAVs need to consider the optimization problem of multiple performance metrics with various types of optimization variables. To tackle the challenge, we propose a reinforcement learning-based approach to solve the multi-objective deployment problem while maintaining the optimal tradeoff between power consumption and backhaul saving. Numerical results evaluate the performance of the proposed algorithm.

UAV-Aided Wireless Powered Communication Networks: Trajectory Optimization and Resource Allocation for Minimum Throughput Maximization

This paper investigates wireless powered communication network (WPCN) systems aided by unmanned aerial vehicle (UAV) where a UAV-mounted access point (AP) serves multiple energy-constrained ground terminals (GTs). Specifically, the UAVs first transmit the wireless energy transfer (WET) signals to charge the GTs in the downlink. Then, by utilizing the harvested energy, the GTs send their wireless information transmission (WIT) signals to the UAVs in the uplink. In this paper, depending on the operations of the UAVs, we consider two different scenarios, namely integrated and separated UAV WPCNs. First, in the integrated system, a UAV acts as a hybrid AP in which both energy transfer and information reception are performed at a single UAV. In contrast, for the separated UAV WPCN, we consider two UAVs each of which behaves as an information AP and an energy AP independently, and thus the information decoding and the energy transfer are separately processed at two different UAVs. In each system, we formulate two optimization problems taking into account a linear energy harvesting (EH) model and a practical non-linear model. To maximize the minimum throughput of the GTs, we jointly optimize the trajectories of the UAVs, the uplink power control, and the time resource allocation for the WET and the WIT. Since the formulated problems are non-convex, in the linear EH model-based system, we apply the concave-convex procedure by deriving appropriate convex bounds for non-convex constraints and identify the suboptimal solution for the problem by a proposed iterative algorithm. In the non-linear model-based system, we propose another algorithm to obtain an efficient solution by adopting the successive convex approximation method with the alternating optimization framework. Simulation results demonstrate the efficiency and the performance of the proposed algorithms compared to conventional schemes.

Joint Trajectory-Task-Cache Optimization in UAV-Enabled Mobile Edge Networks for Cyber-Physical System

This paper studies an unmanned aerial vehicle (UAV)-enabled mobile edge network for Cyber-Physical System (CPS), where UAV with fixed-wing or rotary-wing is dispatched to provide communication and mobile edge computing (MEC) services to ground terminals (GTs). To minimize the energy consumption so as to extend the endurance of the UAV, we intend to jointly optimize its 3D trajectory and the task-cache strategies among GTs to save the energies spent on flight propulsion and GT tasks. Such joint trajectory-task-cache problem is difficult to be optimally solved, as it is non-convex and involves multiple constraints. To tackle this problem, we reformulate the optimizing of task offloading and cache into two tractable linear program (LP) problems, and the optimizing of UAV trajectory into three convex Quadratically Constrained Quadratically Program (QCQP) problems on horizontal trajectory, vertical trajectory and flight time of the UAV respectively. Then a block coordinate descent algorithm is proposed to iteratively solve the formed sub-problems through a successive convex optimization (SCO) process. A high-quality sub-optimal solution to the joint problem then will be obtained, after the algorithm converging to a prescribed accuracy. The numerical results show the proposed solution significantly outperforms the baseline solution.

UAV Hovering Strategy Based on a Wirelessly Powered Communication Network

With the development of wireless power transfer technology, wirelessly powered communication networks (WPCNs), which can provide a sustainable energy supply for the user equipment, have received extensive attention from researchers. An unmanned aerial vehicle (UAV)-based WPCN (U-WPCN) can effectively solve the problems of energy shortage and weak signal strength for ground terminals in remote areas. To establish a highly efficient U-WPCN, the service area for each UAV platform should be determined such that each UAV platform will hover at the optimal position according to the locations of the ground terminals in its group. Through hovering position optimization, this paper proposes two networking strategies for improving networking efficiency and maximizing communication performance. A greedy algorithm is used to find the optimal hovering point for each area; thus, the hovering points of all UAVs in the U-WPCN are finally obtained. Simulation results show that the UAV utilization efficiency can be significantly increased through the economical mode, and the terminal transmission rates can be improved for a given number of drones in the performance mode.

A 3D Non-Stationary Wideband GBSM for Low-Altitude UAV-to-Ground V2V MIMO Channels

Due to the high-mobility of unmanned aerial vehicles (UAVs), actual UAV channel measurements show that low-altitude air-to-ground (A2G) channels in UAV communications illustrate non-stationary properties. This fact motivates us to develop a non-stationary channel model for UAV-to-ground links. In this paper, we propose a three-dimensional (3D) wideband non-stationary A2G vehicle-to-vehicle (V2V) geometry-based stochastic channel model (GBSM). In the proposed model, both UAVs and ground terminals can be moving, which makes the model more general. In order to mimic the non-stationary channel characteristics, parameters like the number of clusters, power, time delays, angels of departure (AoDs), and angles of arrival (AoAs) are all time-variant. The proposed model combining a line-of-sight (LoS) component, a ground reflection component, a cylinder model, and multiple confocal truncated ellipsoid models has the ability to investigate the impact of UAV heights and transceivers' movements on channel characteristics in diverse environments. Statistical properties like temporal autocorrelation function (ACF), spatial cross-correlation function (CCF), Doppler power spectral density (PSD), and stationary interval of A2G channels are derived and analyzed in detail. In addition, derived root mean square delay spread (RMS-DS), temporal ACF and spatial CCF are validated against channel measurement results. Furthermore, by adjusting channel parameters, the proposed GBSM is sufficiently generic and adaptable to model various UAV-to-ground communication scenarios.

Deployment and Trajectory Optimization of UAVs: A Quantization Theory Approach

Optimal deployment and movement of multiple unmanned aerial vehicles (UAVs) are studied. The considered scenario consists of several ground terminals (GTs) communicating with the UAVs using variable transmission power and fixed data rate. First, the static case of a fixed geographical GT density is analyzed. Using a high-resolution quantization theory, the corresponding best achievable performance (measured in terms of the average GT transmission power) is determined in the asymptotic regime of a large number of UAVs. Next, the dynamic case where the GT density is allowed to vary periodically through time is considered. For 1-D networks, an accurate formula for the total UAV movement that guarantees the best time-averaged performance is determined. In general, the tradeoff between the total UAV movement and the achievable performance is obtained through a Lagrangian approach. A corresponding trajectory optimization algorithm is introduced and shown to guarantee a convergent Lagrangian. Numerical simulations confirm the analytical findings. Extensions to different system models and performance measures are also discussed.

Medium earth orbit optical satellite communication networks: Ground terminals selection optimization based on the cloud‐free line‐of‐sight statistics

SummaryIn this paper, a methodology for the generation of cloud coverage time series correlated on both temporal and spatial domains and the estimation of cloud‐free line‐of‐sight (CFLOS) probability for a single optical ground station (OGS) and a network of OGSs (multiple OGS) for medium earth orbit (MEO) constellation satellite communication systems is presented. Spatial diversity is employed as a mitigation technique against cloud blockage to increase the availability of an OGS network (OGSN). In the second part of the paper, an effective algorithm based on the ant colony optimization (ACO) algorithm is proposed for the optimum selection of OGSs forming an OGSN. The proposed algorithm takes into consideration the spatial correlation between the OGSs. In the simulation results section, a MEO constellation system with 12 MEO satellites has been assumed. The integrated liquid water content (ILWC) statistical parameters that are necessary for the CFLOS time series are taken from ECMWF Re‐Analysis (ERA)‐Interim database, European Centre for Medium‐Range Weather Forecasts (ECMWF). Finally, numerical results for the optimum selection of OGSs using the proposed methodology are exhibited and commented. Interesting conclusions are drawn, and future work with technical challenges is briefly described.

Accurate Ground-based Near-Earth-Asteroid Astrometry Using Synthetic Tracking

Abstract Accurate astrometry is crucial for determining orbits of near-Earth-asteroids (NEAs) and therefore better tracking them. This paper reports on a demonstration of 10 mas level astrometric precision on a dozen NEAs using the Pomona College 40 inch telescope, at the JPL’s Table Mountain Facility. We used the technique of synthetic tracking (ST), in which many short-exposure (1 s) images are acquired and then combined in post-processing to track both target asteroid and reference stars across the field of view. This technique avoids the trailing loss and keeps the jitter effects from atmosphere and telescope pointing common between the asteroid and reference stars, resulting in higher astrometric precision than the 100 mas level astrometry from traditional approach of using long exposure images. Treating our ST of near-Earth asteroids as a proxy for observations of future spacecraft while they are downlinking data via their high rate optical communication laser beams, our approach shows precision plane-of-sky measurements can be obtained by the optical ground terminals for navigation. We also discuss how future data releases from the Gaia mission can improve our results.