scholarly journals 3D placements of drones in a millimeter-wave network to maximize the lifetime of wireless devices

2021 ◽  
Vol 8 (11) ◽  
pp. 119-128
Author(s):  
Malkawi et al. ◽  
Keyword(s):  
Base Station ◽  
Projection Algorithm ◽  
Wireless Devices ◽  
Placement Problem ◽  
Swarm Optimization ◽  
Fifth Generation ◽  
Multiple Uavs ◽  
Multiple Unmanned Aerial Vehicles ◽  
Wave Network ◽  
Day By Day

In the last few years, the use of drones is increasing day by day in wireless networks and the applications of them are rapidly increased on different sides. Now, we can use the drone as an aerial base station (BS) to support cellular networks in emergency cases and in natural disasters. To take the advantage of both drones and fifth-generation (5G) and link between their features, we study an aerial BS considering millimeter waves (mm-waves). In this paper, we optimize the 3D placements for multiple unmanned aerial vehicles (UAVs) in an mm-wave network to achieve maximum time durations of the uplink transmission. First, we present a formulation for the placement problem, where we aim to allocate 3D locations for multiple UAVs to achieve the maximum sum of time durations of uplink transmissions. We propose an efficient algorithm to find the placements of UAVs. We propose an algorithm that starts by grouping the wireless devices into a number of clusters, and each cluster is served by a single UAV. After the clustering process, it applies the gradient projection-based algorithm (GP) or particle swarm optimization (PSO) in each cluster. In the results section, our proposed approach and the center projection algorithm will be compared to prove the efficiency of our approach.

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.

scholarly journals On the performance of non-orthogonal multiple access (NOMA) using FPGA

2020 ◽  
Vol 10 (2) ◽  
pp. 2151
Author(s):  
Mohamad Abdulrahman Ahmed ◽  
Khalid F. Mahmmod ◽  
Mohammed M. Azeez
Keyword(s):  
Gaussian Noise ◽  
Interference Cancellation ◽  
Multiple Access ◽  
Additive White Gaussian Noise ◽  
Base Station ◽  
Rate Performance ◽  
Allocation Mechanism ◽  
Close Match ◽  
Fifth Generation ◽  
Field Programmable

In this paper,  non-orthogonal multiple access (NOMA) is designed and implemented for the fifth generation (5G) of multi-user wireless communication.  Field-programmable gate array (FPGA) is considered for the implementation of this technique for two users. NOMA is applied in downlink phase of the base-station (BS) by applying power allocation mechanism for far and near users, in which one signal contains the superposition of two scaled signals depending on the distance of each user from the BS.  We assume an additive white Gaussian noise (AWGN) channel for each user in the presence of the interference due to the non-orthogonality between the two users’ signals. Therefore, successive-interference cancellation (SIC) is exploited to remove the undesired signal of the other user. The outage probability and the bit-error rate performance are presented over different signal-to-interference-plus-noise ratio (SINR). Furthermore, Monte-Carlo simulations via Matlab are utilized to verify the results obtained by FPGA, which show exact-close match.

scholarly journals Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

2021 ◽  
Vol 21 (1) ◽  
pp. 62-72
Author(s):  
R. B. Madhumala ◽  
Harshvardhan Tiwari ◽  
Verma C. Devaraj
Keyword(s):  
Particle Swarm Optimization ◽  
Virtual Machine ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Virtual Machine Placement ◽  
Placement Problem ◽  
Swarm Optimization ◽  
Cloud Datacenter

Abstract Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.

scholarly journals Internet of Things Energy Efficient Cluster-Based Routing Using Hybrid Particle Swarm Optimization for Wireless Sensor Network

2021 ◽  
Author(s):  
Senthil G A ◽  
Arun Raaza ◽  
N Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Internet Of Things ◽  
Optimal Path ◽  
Particle Swarm ◽  
Base Station ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization

Abstract Specialized transducers in Wireless Sensor Networks (WSNs) that offer sensing services to the Internet of Things (IoT) devices have limited storage and energy resources. One of the most vital issues in WSN design is power usage, as it is nearly impossible to recharge or replace sensor nodes’ batteries. A prominent role in conserving power for energy-constrained networks is served by the clustering algorithm. It is possible to reduce network energy usage and network lifespan prolongation by proper balancing of the network load with Cluster Head (CH) election. The single-hop inter-cluster routing technique, in which there is a direct transfer from CHs to the Base Station (BS), is done by the Low Energy Adaptive Clustering Hierarchy (LEACH). However, for networks with large-regions, this technique is not viable. An optimized Orphan-LEACH (O-LEACH) has been proposed in this work to facilitate the formation of a novel process of clustering, which can result in minimized usage of energy as well as enhanced network longevity. Sufficient energy is possessed by the orphan node, which will attempt to be cover the network. The proposed work’s primary novel contribution is the O-LEACH protocol that supplies the entire network’s coverage with the least number of orphaned nodes and has extremely high connectivity rates. A hybrid optimization utilizing Simulated Annealing (SA) with Lightning Search Algorithm (LSA) (SA-LSA), and Particle Swarm Optimization (PSO) with LSA (PSO-LSA) Algorithm is proposed. These proposed techniques effectivelymanage the CH election achieving optimal path routing and minimization in energy usage, resulting in the enhanced lifespan of the WSN. The proposed technique’s superior performance, when compared with other techniques, is confirmed from the outcomes of the experimentations.

scholarly journals QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks

2021 ◽  
Vol 14 (1) ◽  
pp. 270-280
Author(s):  
Abhijit Halkai ◽  
◽  
Sujatha Terdal ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Large Scale ◽  
Cluster Head ◽  
Particle Swarm ◽  
Base Station ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Localization Model

A sensor network operates wirelessly and transmits detected information to the base station. The sensor is a small sized device, it is battery-powered with some electrical components, and the protocols should operate efficiently in such least resource availability. Here, we propose a novel improved framework in large scale applications where the huge numbers of sensors are distributed over an area. The designed protocol will address the issues that arise during its communication and give a consistent seamless communication system. The process of reasoning and learning in cognitive sensors guarantees data delivery in the network. Localization in Scarce and dense sensor networks is achieved by efficient cluster head election and route selection which are indeed based on cognition, improved Particle Swarm Optimization, and improved Ant Colony Optimization algorithms. Factors such as mobility, use of sensor buffer, power management, and defects in channels have been identified and solutions are presented in this research to build an accurate path based on the network context. The achieved results in extensive simulation prove that the proposed scheme outperforms ESNA, NETCRP, and GAECH algorithms in terms of Delay, Network lifetime, Energy consumption.

Placement of Relay Stations in WiMAX Network Using Glowworm Swarm Optimization

2019 ◽  
Vol 10 (3) ◽  
pp. 39-67
Author(s):  
Sangeetha J ◽  
Keerthiraj Nagaraj ◽  
Ram Prakash Rustagi ◽  
Balasubramanya Murthy K N
Keyword(s):  
Geographic Region ◽  
Base Station ◽  
Optimal Placement ◽  
Network Coverage ◽  
Hardware Complexity ◽  
Swarm Optimization ◽  
Wimax Networks ◽  
Glowworm Swarm Optimization ◽  
The Cost ◽  
The Impact

The Relay Station (RS) deployment problem for WiMAX networks is studied. Unlike Base Station (BS), RS does not need a wire-line backhaul and has much lower hardware complexity. Hence, usage of RSs can significantly minimize the deployment cost and maximize the network coverage of the system. To solve the RS deployment problem, the authors have used a nature inspired technique known as Glowworm Swarm Optimization (GSO). Different cases have been considered for a single fixed BS, to find the feasible number of RSs and its optimal placement in WiMAX networks. Computational experiments are conducted to show the effect of RS deployments in different distribution scenarios. This article also shows the impact of placing RSs at optimal locations to serve given Mobile Stations (MSs) that are distributed arbitrarily in a given geographic region such that the cost is minimized, and the network coverage is maximized. The results obtained from the GSO algorithm are compared with k-means algorithm and it is observed that GSO performs better than k-means algorithm.

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