1,035 research outputs found
Distributed drone base station positioning for emergency cellular networks using reinforcement learning
Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network
Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m2 as well as in a large area of 10,000 m2. Results show that the drone turnover rate is related to both users’ sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14–16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.Junta de AndalucĂa Beca 2020/00000172UniĂłn Europea FEDER 2014-202
Wireless Mesh Networks Based on MBPSO Algorithm to Improvement Throughput
Wireless Mesh Networks can be regarded as a type of communication technology in mesh topology in which wireless nodes interconnect with one another. Wireless Mesh Networks depending on the semi-static configuration in different paths among nodes such as PDR, E2E delay and throughput. This study summarized different types of previous heuristic algorithms in order to adapt with proper algorithm that could solve the issue. Therefore, the main objective of this study is to determine the proper methods, approaches or algorithms that should be adapted to improve the throughput. A Modified Binary Particle Swarm Optimization (MBPSO) approach was adapted to improvements the throughput. Finally, the finding shows that throughput increased by 5.79% from the previous study
A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches
Wireless communication networks have been witnessing an unprecedented demand
due to the increasing number of connected devices and emerging bandwidth-hungry
applications. Albeit many competent technologies for capacity enhancement
purposes, such as millimeter wave communications and network densification,
there is still room and need for further capacity enhancement in wireless
communication networks, especially for the cases of unusual people gatherings,
such as sport competitions, musical concerts, etc. Unmanned aerial vehicles
(UAVs) have been identified as one of the promising options to enhance the
capacity due to their easy implementation, pop up fashion operation, and
cost-effective nature. The main idea is to deploy base stations on UAVs and
operate them as flying base stations, thereby bringing additional capacity to
where it is needed. However, because the UAVs mostly have limited energy
storage, their energy consumption must be optimized to increase flight time. In
this survey, we investigate different energy optimization techniques with a
top-level classification in terms of the optimization algorithm employed;
conventional and machine learning (ML). Such classification helps understand
the state of the art and the current trend in terms of methodology. In this
regard, various optimization techniques are identified from the related
literature, and they are presented under the above mentioned classes of
employed optimization methods. In addition, for the purpose of completeness, we
include a brief tutorial on the optimization methods and power supply and
charging mechanisms of UAVs. Moreover, novel concepts, such as reflective
intelligent surfaces and landing spot optimization, are also covered to capture
the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of
Communications Society (OJ-COMS
Three Dimensional UAV Positioning for Dynamic UAV-to-Car Communications
[EN] In areas with limited infrastructure, Unmanned Aerial Vehicles (UAVs) can come in handy
as relays for car-to-car communications. Since UAVs are able to fully explore a three-dimensional
environment while flying, communications that involve them can be affected by the irregularity of the
terrains, that in turn can cause path loss by acting as obstacles. Accounting for this phenomenon, we
propose a UAV positioning technique that relies on optimization algorithms to improve the support
for vehicular communications. Simulation results show that the best position of the UAV can be
timely determined considering the dynamic movement of the cars. Our technique takes into account
the current flight altitude, the position of the cars on the ground, and the existing flight restrictions.This work was partially supported by the Ministerio de Ciencia, InnovaciĂłn y Universidades, Programa
Estatal de InvestigaciĂłn, Desarrollo e InnovaciĂłn Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018 ,
Spain, under Grant RTI2018-096384-B-I00, and grant BES-2015-075988, Ayudas para contratos predoctorales 2015.Hadiwardoyo, SA.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Krinkin, K.; Klionskiy, D.; Hernández-Orallo, E.; Manzoni, P. (2020). Three Dimensional UAV Positioning for Dynamic UAV-to-Car Communications. Sensors. 20(2):1-18. https://doi.org/10.3390/s20020356S11820
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
QoS-Aware 3D Coverage Deployment of UAVs for Internet of Vehicles in Intelligent Transportation
It is a challenging problem to characterize the air-to-ground (A2G) channel
and identify the best deployment location for 3D UAVs with the QoS awareness.
To address this problem, we propose a QoS-aware UAV 3D coverage deployment
algorithm, which simulates the three-dimensional urban road scenario, considers
the UAV communication resource capacity and vehicle communication QoS
requirements comprehensively, and then obtains the optimal UAV deployment
position by improving the genetic algorithm. Specifically, the K-means
clustering algorithm is used to cluster the vehicles, and the center locations
of these clusters serve as the initial UAV positions to generate the initial
population. Subsequently, we employ the K-means initialized grey wolf
optimization (KIGWO) algorithm to achieve the UAV location with an optimal
fitness value by performing an optimal search within the grey wolf population.
To enhance the algorithm's diversity and global search capability, we randomly
substitute this optimal location with one of the individual locations from the
initial population. The fitness value is determined by the total number of
vehicles covered by UAVs in the system, while the allocation scheme's
feasibility is evaluated based on the corresponding QoS requirements.
Competitive selection operations are conducted to retain individuals with
higher fitness values, while crossover and mutation operations are employed to
maintain the diversity of solutions. Finally, the individual with the highest
fitness, which represents the UAV deployment position that covers the maximum
number of vehicles in the entire system, is selected as the optimal solution.
Extensive experimental results demonstrate that the proposed algorithm can
effectively enhance the reliability and vehicle communication QoS
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