The Internet-of-Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and three-dimensional (3D) positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technology to overcome the above shortcomings. In this paper, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices' transmission schedule over time, while ensuring the reliability of data collection and required 3D positioning performance. This formulation is a mixed-integer non-convex optimization problem, and an efficient differential evolution (DE) based method is proposed for solving it. Numerical results demonstrate that the proposed network and optimization method achieve significant performance gains in both energy efficient data collection and 3D device positioning, as compared with a conventional terrestrial IoT network.
Non-orthogonal multiple access (NOMA) has been identified as a promising technology in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication networks for Internet of things (IoT) applications, which has the advantages of both massive connectivity and high spectrum efficiency. However, few researchers have considered the probability of introducing NOMA to a positioning system. In this paper, a novel mmWave MIMO-NOMA based positioning system is proposed, which is capable of meeting the requirements of IoT applications. We establish a NOMA-based positioning model from the perspective of the system level, along with the design of transmission strategy. To characterize the positioning performance, the position error bound (PEB) is selected as an evaluation criteria and theoretical expressions of the PEB are provided. Simulations of comparing localization performance between NOMA and conventional orthogonal multiple access (OMA) are conducted by using the theoretical analysis. Numerical results show that the application of NOMA to localization is a viable way to reduce the PEB compared to OMA. Our work further shows under what circumstances can NOMA outperform OMA in terms of localization performance and the corresponding parameter settings.
As the cost and technical difficulty of jamming devices continue to decrease, jamming has become one of the major threats to positioning service. Unfortunately, most conventional wireless positioning technologies are vulnerable to jamming attacks due to inherent shortcomings like weak signal strength and unfavorable anchor geometry. Thanks to their high operational flexibility, unmanned aerial vehicles (UAVs) could be a promising solution to the above challenges. Therefore, in this article, we propose a UAV-assisted anti-jamming positioning system, in which multiple UAVs first utilize time-difference-ofarrival (TDoA) measurements from ground reference stations and double-response two-way ranging (DR-TWR) measurements from UAV-to-UAV links to perform self-localization as well as clock synchronization, and then act as anchor nodes to provide TDoA positioning service for ground users in the presence of jamming. To evaluate the feasibility and performance of the proposed system, we first derive the Cramér-Rao lower bound (CRLB) of UAV self-localization. Then, the impacts of UAV position uncertainty and synchronization errors caused by jamming on positioning service are modeled, and the theoretical root-mean-square error (RMSE) of user position estimate is further derived. Numerical results demonstrate that the proposed system is a promising alternative to existing positioning systems when their services are disrupted by jamming. The most notable advantage of the proposed system is that it is fully compatible with existing user equipment terminals and positioning methods. Index Terms-Unmanned aerial vehicle (UAV), anti-jamming positioning, time-difference-of-arrival (TDoA), double-response two-way ranging (DR-TWR).
In isolated regions, utilizing the unmanned aerial vehicle (UAV) as an aerial anchor node is a promising technique to enable location awareness of ground terminals (GTs). In this letter, considering a UAV swarm-enabled localization for a group of distributed GTs, we aim to minimize the maximum Cramer-Rao lower bound (CRLB) for position estimates with anchor uncertainty. Then, an efficient differential evolution (DE)based method is proposed to find a sub-optimal solution. In particular, the rigidity of the UAV swarm is recognized as a critical constraint in the problem formulation to provide a unique swarm coordinate configuration and to maintain a prescribed flight formation. A gradient-based local optimization for rigidity is then proposed and embedded in the DE algorithm. Numerical results demonstrate that our proposed designs can reach better performance in localization accuracy while ensuring the rigidity of the UAV swarm, as compared with a random approach.
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