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).
Reliable positioning services are extremely important for users in mountainous environments. However, in such environments, the service reliability of conventional wireless positioning technologies is often disappointing due to frequent nonline-of-sight (NLoS) propagation and poor geometry of available anchor nodes. Hence, we propose a unmanned aerial vehicle (UAV)-enabled positioning system that utilizes UAV's mobility to overcome the above challenges. In this article, we first analyze and model the major causes of service failures in the proposed system. In particular, a geometry-based NLoS probability model is established based on the digital elevation models (DEM) of realistic terrain for reliability analysis. Subsequently, we propose a reliability prediction method and derive the corresponding metric to evaluate the system's ability to provide reliable positioning services. Moreover, we also develop a voting-based method for the further enhancement of service reliability. Monte-Carlo simulations show that in mountainous environments, the proposed reliability prediction method could achieve a prediction accuracy that is at least 36.8% higher than that of the existing technique. In addition, in the experiments conducted in two typical valley scenarios, the proposed reliability enhancement method improves the service reliability of the proposed system by 23% and 29%, respectively. These numerical results demonstrate the strong potential of the proposed system and methods for reliable positioning.
Intelligent transport systems demand the provision of a continuous high-accuracy positioning service. However, a vehicle positioning system typically has to operate in dense urban areas where conventional satellite-based positioning systems suffer severe performance degradation. 5G technology presents a new paradigm to provide ubiquitous connectivity, where the vehicle-to-everything (V2X) communication turns out to be highly conducive to enable both accurate positioning and the emerging Internet of Vehicles (IoV). Due to the high probability of line-ofsight (LoS) communication, as well as the diversity and number of reference stations, the application of ultra-dense networks (UDN) in the vehicle-to-infrastructure (V2I) subsystem is envisaged to complement the existing positioning technologies. Moreover, the cooperative determination of location information could be enhanced by the vehicle-to-vehicle (V2V) subsystem. In this article, we propose a V2X integrated positioning methodology in UDN, in which the V2I, V2V and Inertial Navigation Systems (INS) are unified for data fusion. This formulation is an iterative high-dimensional estimation problem, and an efficient multiple particle filter (MPF)-based method is proposed for solving it. In order to mitigate the non-line-of-sight (NLoS) impact and provide a relatively accurate input to the MPF, we introduce an advanced anchor selection method using the geometry-based K-Means clustering (GK) algorithm based on the characteristics of network densification. Numerical results demonstrate that utilizing the GK algorithm in the proposed integrated positioning system could achieve 18.7% performance gains in accuracy, as compared with a state-of-art approach.Index Terms-Vehicle to everything (V2X), Internet of Vehicles (IoV), vehicle positioning, multiple particle filter (MPF), ultradense networks (UDN), non-line-of-sight (NLoS).
Unmanned aerial vehicle (UAV)-enabled positioning that uses UAVs as aerial anchor nodes is a promising solution for providing positioning services in harsh environments. In previous research, the state sensing and control of UAVs were either ignored or simply set to be performed continuously, resulting in system instability or waste of wireless resources. Therefore, in this article, we propose a quality-of-service (QoS)-oriented UAV-enabled positioning system based on the concept of sensingcommunication-control (SCC) co-design. We first establish the mathematical models of UAV state sensing and control. Then, the influence of sensing scheduling and transmission failure on UAV stability, as well as the performance of positioning services in the presence of UAV control error, are analyzed.Based on these models and analysis results, we further study the problem of minimizing the amount of data transmitted by optimizing the sensing scheduling and blocklength allocation under the condition of satisfying each user's demand. Finally, an efficient scheme is developed to solve this mixed-integer nonlinear problem. Numerical results show that the proposed system could work efficiently and meet users' requirements. In addition, compared with two benchmark schemes, our scheme reduces the failure rate or resource consumption of positioning services by more than 76.2% or 82.7%.
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