scholarly journals Multiple Unmanned Aerial Vehicles Deployment and User Pairing for Non-Orthogonal Multiple Access Schemes

2020 ◽  
Author(s):  
Jie Wang ◽  
Miao Liu ◽  
Jinlong Sun ◽  
Guan Gui ◽  
Haris Gacanin ◽  
...  
Keyword(s):  
Wireless Communications ◽  
Unmanned Aerial Vehicles ◽  
Message Passing ◽  
Multiple Access ◽  
Base Station ◽  
Spectrum Efficiency ◽  
User Pairing ◽  
Aerial Vehicles ◽  
Multiple Uavs ◽  
Multiple Unmanned Aerial Vehicles

Non-orthogonal multiple access (NOMA) significantly improves the connectivity opportunities and enhances the spectrum efficiency (SE) in the fifth generation and beyond (B5G) wireless communications. Meanwhile, emerging B5G services demand of higher SE in the NOMA based wireless communications. However, traditional ground-to-ground (G2G) communications are hard to satisfy these demands, especially for the cellular uplinks. To solve these challenges, this paper proposes a multiple unmanned aerial vehicles (UAVs) aided uplink NOMA method. In detail, multiple hovering UAVs relay data for a part of ground users (GUs) and share the sub-channels with the left GUs that communicate with the base station (BS) directly. Furthermore, this paper proposes a K-means clustering based UAV deployment and location based user pairing scheme to optimize the transceiver association for the multiple UAVs aided NOMA uplinks. Finally, a sum power minimization based resource allocation problem is formulated with the lowest quality of service (QoS) constraints. We solve it with the message-passing algorithm and evaluate the superior performances of the proposed scheduling and paring schemes on SE and energy efficiency (EE). Extensive experiments are conducted to compare the performances of the proposed schemes with those of the single UAV aided NOMA uplinks, G2G based NOMA uplinks, and the proposed multiple UAVs aided uplinks with a random UAV deployment. Simulation results demonstrate that the proposed multiple UAVs deployment and user pairing based NOMA scheme significantly improves the EE and the SE of the cellular uplinks at the cost of only a little relaying power consumption of the UAVs.

scholarly journals Multiple Unmanned Aerial Vehicles Deployment and User Pairing for Non-Orthogonal Multiple Access Schemes

2020 ◽  
Author(s):  
Jie Wang ◽  
Miao Liu ◽  
Jinlong Sun ◽  
Guan Gui ◽  
Haris Gacanin ◽  
...  
Keyword(s):  
Wireless Communications ◽  
Unmanned Aerial Vehicles ◽  
Message Passing ◽  
Multiple Access ◽  
Base Station ◽  
Spectrum Efficiency ◽  
User Pairing ◽  
Aerial Vehicles ◽  
Multiple Uavs ◽  
Multiple Unmanned Aerial Vehicles

Non-orthogonal multiple access (NOMA) significantly improves the connectivity opportunities and enhances the spectrum efficiency (SE) in the fifth generation and beyond (B5G) wireless communications. Meanwhile, emerging B5G services demand of higher SE in the NOMA based wireless communications. However, traditional ground-to-ground (G2G) communications are hard to satisfy these demands, especially for the cellular uplinks. To solve these challenges, this paper proposes a multiple unmanned aerial vehicles (UAVs) aided uplink NOMA method. In detail, multiple hovering UAVs relay data for a part of ground users (GUs) and share the sub-channels with the left GUs that communicate with the base station (BS) directly. Furthermore, this paper proposes a K-means clustering based UAV deployment and location based user pairing scheme to optimize the transceiver association for the multiple UAVs aided NOMA uplinks. Finally, a sum power minimization based resource allocation problem is formulated with the lowest quality of service (QoS) constraints. We solve it with the message-passing algorithm and evaluate the superior performances of the proposed scheduling and paring schemes on SE and energy efficiency (EE). Extensive experiments are conducted to compare the performances of the proposed schemes with those of the single UAV aided NOMA uplinks, G2G based NOMA uplinks, and the proposed multiple UAVs aided uplinks with a random UAV deployment. Simulation results demonstrate that the proposed multiple UAVs deployment and user pairing based NOMA scheme significantly improves the EE and the SE of the cellular uplinks at the cost of only a little relaying power consumption of the UAVs.

Survey of Cooperative Path Planning for Multiple Unmanned Aerial Vehicles

2014 ◽  
Vol 668-669 ◽  
pp. 388-393 ◽  
Author(s):  
Xiao Ming Cheng ◽  
Dong Cao ◽  
Chun Tao Li
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Cooperative Control ◽  
Control Methods ◽  
Future Time ◽  
Research Directions ◽  
Aerial Vehicles ◽  
Multiple Uavs ◽  
Problem Description ◽  
Multiple Unmanned Aerial Vehicles

As an important part of cooperative control for multiple unmanned aerial vehicles (UAVs), cooperative path planning can get optimal flight path which can satisfy different constraints. Research on cooperative path planning for multiple UAVs is summarized in this paper. Firstly, problem description and constraints are given. Then, solution frameworks and path coordination approaches are summarized. After that, several control methods commonly used in formation of multiple UAVs are introduced respectively. Lastly, possible research directions in the future time are put forward.

scholarly journals Hybrid Direction of Arrival Precoding for Multiple Unmanned Aerial Vehicles Aided Non-Orthogonal Multiple Access in 6G Networks

2022 ◽  
Vol 12 (2) ◽  
pp. 895
Author(s):  
Laura Pierucci
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Multiple Access ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Base Station ◽  
Flying Height ◽  
Maximization Problem ◽  
Aerial Vehicles ◽  
Sum Rate ◽  
Hybrid Beamforming

Unmanned aerial vehicles (UAV) have attracted increasing attention in acting as a relay for effectively improving the coverage and data rate of wireless systems, and according to this vision, they will be integrated in the future sixth generation (6G) cellular network. Non-orthogonal multiple access (NOMA) and mmWave band are planned to support ubiquitous connectivity towards a massive number of users in the 6G and Internet of Things (IOT) contexts. Unfortunately, the wireless terrestrial link between the end-users and the base station (BS) can suffer severe blockage conditions. Instead, UAV relaying can establish a line-of-sight (LoS) connection with high probability due to its flying height. The present paper focuses on a multi-UAV network which supports an uplink (UL) NOMA cellular system. In particular, by operating in the mmWave band, hybrid beamforming architecture is adopted. The MUltiple SIgnal Classification (MUSIC) spectral estimation method is considered at the hybrid beamforming to detect the different direction of arrival (DoA) of each UAV. We newly design the sum-rate maximization problem of the UAV-aided NOMA 6G network specifically for the uplink mmWave transmission. Numerical results point out the better behavior obtained by the use of UAV relays and the MUSIC DoA estimation in the Hybrid mmWave beamforming in terms of achievable sum-rate in comparison to UL NOMA connections without the help of a UAV network.

scholarly journals Equilibrium Optimizer with Generalized Opposition-based Learning for Multiple Unmanned Aerial Vehicles Path Planning

2021 ◽  
Author(s):  
Yang Chen ◽  
Dechang Pi ◽  
Bi Wang ◽  
Ali Wagdy Mohamed ◽  
Junfu Chen
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Dimensional Space ◽  
Optimization Techniques ◽  
High Dimensional ◽  
Population Activity ◽  
Opposition Based Learning ◽  
Aerial Vehicles ◽  
Multiple Uavs ◽  
Multiple Unmanned Aerial Vehicles

Abstract Multiple Unmanned Aerial Vehicles (UAVs) path planning is the benchmark problem of multiple UAVs application, which belongs to the non-deterministic polynomial problem. Its objective is to require multiple UAVs flying safely to the goal position according to their specific start position in three-dimensional space. This issue can be defined as a high-dimensional optimization problem, the solution of which requires optimization techniques with global optimization capabilities. Equilibrium optimizer (EO) is a population-based meta-heuristic algorithm. In order to improve the optimization ability of EO to solve high dimensional problems, this paper proposes a modified equilibrium optimizer with generalized opposition-based learning (MGOEO), which improves the population activity by increasing the internal mutation and cross of the population. In addition, the generalized opposition-based learning is used to construct the population, which can effectively ensure that the algorithm has ability to jump out of the limitation of local optimal. Firstly, numerical experiments show that MGOEO has better optimization precision than EO and several other swarm intelligent algorithms. Then, the paths of UAVs are simulated in three different obstacle environments. The simulation results show that MGOEO can obtain safe and smooth paths, which are better than EO and other eight state-of-the-art optimization algorithms.

scholarly journals Adaptive Human-Robot Interactions for Multiple Unmanned Aerial Vehicles

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Yixiang Lim ◽  
Nichakorn Pongsarkornsathien ◽  
Alessandro Gardi ◽  
Roberto Sabatini ◽  
Trevor Kistan ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Simulation Environment ◽  
Adaptive Automation ◽  
Cognitive States ◽  
Aerial Vehicles ◽  
Machine Interface ◽  
Multiple Unmanned Aerial Vehicles ◽  
Online Adaptation ◽  
Adaptation Phase

Advances in unmanned aircraft systems (UAS) have paved the way for progressively higher levels of intelligence and autonomy, supporting new modes of operation, such as the one-to-many (OTM) concept, where a single human operator is responsible for monitoring and coordinating the tasks of multiple unmanned aerial vehicles (UAVs). This paper presents the development and evaluation of cognitive human-machine interfaces and interactions (CHMI2) supporting adaptive automation in OTM applications. A CHMI2 system comprises a network of neurophysiological sensors and machine-learning based models for inferring user cognitive states, as well as the adaptation engine containing a set of transition logics for control/display functions and discrete autonomy levels. Models of the user’s cognitive states are trained on past performance and neurophysiological data during an offline calibration phase, and subsequently used in the online adaptation phase for real-time inference of these cognitive states. To investigate adaptive automation in OTM applications, a scenario involving bushfire detection was developed where a single human operator is responsible for tasking multiple UAV platforms to search for and localize bushfires over a wide area. We present the architecture and design of the UAS simulation environment that was developed, together with various human-machine interface (HMI) formats and functions, to evaluate the CHMI2 system’s feasibility through human-in-the-loop (HITL) experiments. The CHMI2 module was subsequently integrated into the simulation environment, providing the sensing, inference, and adaptation capabilities needed to realise adaptive automation. HITL experiments were performed to verify the CHMI2 module’s functionalities in the offline calibration and online adaptation phases. In particular, results from the online adaptation phase showed that the system was able to support real-time inference and human-machine interface and interaction (HMI2) adaptation. However, the accuracy of the inferred workload was variable across the different participants (with a root mean squared error (RMSE) ranging from 0.2 to 0.6), partly due to the reduced number of neurophysiological features available as real-time inputs and also due to limited training stages in the offline calibration phase. To improve the performance of the system, future work will investigate the use of alternative machine learning techniques, additional neurophysiological input features, and a more extensive training stage.

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