1,351 research outputs found
A Review of Consensus-based Multi-agent UAV Implementations
In this paper, a survey on distributed control applications for multi Unmanned Aerial Vehicles (UAVs) systems is proposed.The focus is on consensus-based control, and both rotary-wing and fixed-wing UAVs are considered. On one side, the latest experimental configurations for the implementation of formation flight are analysed and compared for multirotor UAVs. On the other hand, the control frameworks taking into account the mobility of the fixed-wing UAVs performing target tracking are considered. This approach can be helpful to assess and compare the solutions for practical applications of consensus in UAV swarms
Distributed 3D-Beam Reforming for Hovering-Tolerant UAVs Communication over Coexistence: A Deep-Q Learning for Intelligent Space-Air-Ground Integrated Networks
In this paper, we present a novel distributed UAVs beam reforming approach to
dynamically form and reform a space-selective beam path in addressing the
coexistence with satellite and terrestrial communications. Despite the unique
advantage to support wider coverage in UAV-enabled cellular communications, the
challenges reside in the array responses' sensitivity to random rotational
motion and the hovering nature of the UAVs. A model-free reinforcement learning
(RL) based unified UAV beam selection and tracking approach is presented to
effectively realize the dynamic distributed and collaborative beamforming. The
combined impact of the UAVs' hovering and rotational motions is considered
while addressing the impairment due to the interference from the orbiting
satellites and neighboring networks. The main objectives of this work are
two-fold: first, to acquire the channel awareness to uncover its impairments;
second, to overcome the beam distortion to meet the quality of service (QoS)
requirements. To overcome the impact of the interference and to maximize the
beamforming gain, we define and apply a new optimal UAV selection algorithm
based on the brute force criteria. Results demonstrate that the detrimental
effects of the channel fading and the interference from the orbiting satellites
and neighboring networks can be overcome using the proposed approach.
Subsequently, an RL algorithm based on Deep Q-Network (DQN) is developed for
real-time beam tracking. By augmenting the system with the impairments due to
hovering and rotational motion, we show that the proposed DQN algorithm can
reform the beam in real-time with negligible error. It is demonstrated that the
proposed DQN algorithm attains an exceptional performance improvement. We show
that it requires a few iterations only for fine-tuning its parameters without
observing any plateaus irrespective of the hovering tolerance
A Survey of Beam Management for mmWave and THz Communications Towards 6G
Communication in millimeter wave (mmWave) and even terahertz (THz) frequency
bands is ushering in a new era of wireless communications. Beam management,
namely initial access and beam tracking, has been recognized as an essential
technique to ensure robust mmWave/THz communications, especially for mobile
scenarios. However, narrow beams at higher carrier frequency lead to huge beam
measurement overhead, which has a negative impact on beam acquisition and
tracking. In addition, the beam management process is further complicated by
the fluctuation of mmWave/THz channels, the random movement patterns of users,
and the dynamic changes in the environment. For mmWave and THz communications
toward 6G, we have witnessed a substantial increase in research and industrial
attention on artificial intelligence (AI), reconfigurable intelligent surface
(RIS), and integrated sensing and communications (ISAC). The introduction of
these enabling technologies presents both open opportunities and unique
challenges for beam management. In this paper, we present a comprehensive
survey on mmWave and THz beam management. Further, we give some insights on
technical challenges and future research directions in this promising area.Comment: accepted by IEEE Communications Surveys & Tutorial
Introduction to the Special Issue on Sustainable Solutions for the Intelligent Transportation Systems
The intelligent transportation systems improve the transportation system’s operational efficiency and enhance its safety and reliability by high-tech means such as information technology, control technology, and computer technology. In recent years, sustainable development has become an important topic in intelligent transportation’s development, including new infrastructure and energy distribution, new energy vehicles and new transportation systems, and the development of low-carbon and intelligent transportation equipment. New energy vehicles’ development is a significant part of green transportation, and its automation performance improvement is vital for smart transportation.
The development of intelligent transportation and green, low-carbon, and intelligent transportation equipment needs to be promoted, a significant feature of transportation development in the future. For intelligent infrastructure and energy
distribution facilities, the electricity for popular electric vehicles and renewable energy, such as nuclear power and hydrogen
power, should be considered
Active SLAM: A Review On Last Decade
This article presents a comprehensive review of the Active Simultaneous
Localization and Mapping (A-SLAM) research conducted over the past decade. It
explores the formulation, applications, and methodologies employed in A-SLAM,
particularly in trajectory generation and control-action selection, drawing on
concepts from Information Theory (IT) and the Theory of Optimal Experimental
Design (TOED). This review includes both qualitative and quantitative analyses
of various approaches, deployment scenarios, configurations, path-planning
methods, and utility functions within A-SLAM research. Furthermore, this
article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM),
focusing on collaborative aspects within SLAM systems. It includes a thorough
examination of collaborative parameters and approaches, supported by both
qualitative and statistical assessments. This study also identifies limitations
in the existing literature and suggests potential avenues for future research.
This survey serves as a valuable resource for researchers seeking insights into
A-SLAM methods and techniques, offering a current overview of A-SLAM
formulation.Comment: 34 pages, 8 figures, 6 table
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Dynamic Reconfiguration in Camera Networks: A Short Survey
There is a clear trend in camera networks towards enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally we discuss the main open research challenges in camera network reconfiguration
Opportunistic Angle of Arrival Estimation in Impaired Scenarios
This work if focused on the analysis and the development of Angle of Arrival (AoA) radio localization methods. The radio positioning system considered is constituted by a radio source and by a receiving array of antennas. The positioning algorithms treated in this work are designed to have a passive and opportunistic approach. The opportunistic attribute implies that the radio localization algorithms are designed to provide the AoA estimation with nearly-zero information on the transmitted signals. No training sequences or waveforms custom designed for localization are taken into account. The localization is termed passive since there is no collaboration between the transmitter and the receiver during the localization process. Then, the algorithms treated in this work are designed to eavesdrop already existing communication signals and to locate their radio source with nearly-zero knowledge of the signal and without the collaboration of the transmitting node. First of all, AoA radio localization algorithms can be classified in terms of involved signals (narrowband or broadband), antenna array pattern (L-shaped, circular, etc.), signal structure (sinusoidal, training sequences, etc.), Differential Time of Arrival (D-ToA) / Differential Phase of Arrival (D-PoA) and collaborative/non collaborative. Than, the most detrimental effects for radio communications are treated: the multipath (MP) channels and the impaired hardware. A geometric model for the MP is analysed and implemented to test the robustness of the proposed methods. The effects of MP on the received signals statistics from the AoA estimation point-of-view are discussed. The hardware impairments for the most common components are introduced and their effects in the AoA estimation process are analysed. Two novel algorithms that exploits the AoA from signal snapshots acquired sequentially with a time division approach are presented. The acquired signals are QAM waveforms eavesdropped from a pre-existing communication. The proposed methods, namely Constellation Statistical Pattern IDentification and Overlap (CSP-IDO) and Bidimensional CSP-IDO (BCID), exploit the probability density function (pdf) of the received signals to obtain the D-PoA. Both CSP-IDO and BCID use the statistical pattern of received signals exploiting the transmitter statistical signature. Since the presence of hardware impairments modify the statistical pattern of the received signals, CSP-IDO and BCID are able to exploit it to improve the performance with respect to (w.r.t.) the ideal case. Since the proposed methods can be used with a switched antenna architecture they are implementable with a reduced hardware contrariwise to synchronous methods like MUltiple SIgnal Classification (MUSIC) that are not applicable. Then, two iterative AoA estimation algorithms for the dynamic tracking of moving radio sources are implemented. Statistical methods, namely PF, are used to implement the iterative tracking of the AoA from D-PoA measures in two different scenarios: automotive and Unmanned Aerial Vehicle (UAV). The AoA tracking of an electric car signalling with a IEEE 802.11p-like standard is implemented using a test-bed and real measures elaborated with a the proposed Particle Swarm Adaptive Scattering (PSAS) algorithm. The tracking of a UAV moving in the 3D space is investigated emulating the UAV trajectory using the proposed Confined Area Random Aerial Trajectory Emulator (CARATE) algorithm
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