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Future wireless communication, especially the densified 5G network using millimeter-Wave (mmWave) will bring numerous innovations to the current telecommunication industry. In such scenario, the use of Unmanned Aerial Vehicle (UAV) as Base Station (BS) becomes one of the viable options for providing 5G services. The focus of this study is to invest...
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... high gain due to multiple antenna systems which can offer a transmission range that exceeds around 130 m in one direction [5], and in some specific scenarios coverage can be reached up to 200 m [9]. Nevertheless, the critical challenges of using mmWave in the wireless cellular networks are propagation loss and sensitivity to blockage, depicted in Fig. ...
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... this set of simulation, we deploy UAV at a fixed height of 30 m (same height as the BS) and move UAV in a horizontal direction (away) from the BS. We calculate the received power by the UEs via the Amplify-and-Forward relay (UAV) and results are depicted in Fig. 10. We observe that as the distance between the UAV and BS increases, the received ...
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... we perform the analysis by varying both the UAV elevation and the UAV horizontal distance. We deploy UAV at a horizontal distance of 20 m and 60 m from the BS, respectively. For each horizontal distance, we elevate the UAV from 10 to 100 m. We calculate the received power by UEs via the Amplify-and-Forward relay (UAV) and results are shown in Fig. 11. We observe that as the UAV height increases, the received power by the UEs decreases and the greatest number of UEs are receiving good coverage when the UAV height is at 30 m (which is the same height of the BS). Likewise, we notice that the edge user's coverage is optimum at this ...
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... this purpose, we consider the scenario, depicted in Fig. 12a, where terrestrial BS is communicating with UAV, which is providing coverage to the ground UEs. We use 28 GHz frequency band for both links (Access and Backhaul). Furthermore, an isotropic antenna is deployed both at BS and UAV and we feed BS with a transmit power of 43 dBm. We deploy UAV at a horizontal distance of 60 m from the BS, ...
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... high gain due to multiple antenna systems which can offer a transmission range that exceeds around 130 m in one direction [5], and in some specific scenarios coverage can be reached up to 200 m [9]. Nevertheless, the critical challenges of using mmWave in the wireless cellular networks are propagation loss and sensitivity to blockage, depicted in Fig. ...
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... this set of simulation, we deploy UAV at a fixed height of 30 m (same height as the BS) and move UAV in a horizontal direction (away) from the BS. We calculate the received power by the UEs via the Amplify-and-Forward relay (UAV) and results are depicted in Fig. 10. We observe that as the distance between the UAV and BS increases, the received ...
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... we perform the analysis by varying both the UAV elevation and the UAV horizontal distance. We deploy UAV at a horizontal distance of 20 m and 60 m from the BS, respectively. For each horizontal distance, we elevate the UAV from 10 to 100 m. We calculate the received power by UEs via the Amplify-and-Forward relay (UAV) and results are shown in Fig. 11. We observe that as the UAV height increases, the received power by the UEs decreases and the greatest number of UEs are receiving good coverage when the UAV height is at 30 m (which is the same height of the BS). Likewise, we notice that the edge user's coverage is optimum at this ...
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... this purpose, we consider the scenario, depicted in Fig. 12a, where terrestrial BS is communicating with UAV, which is providing coverage to the ground UEs. We use 28 GHz frequency band for both links (Access and Backhaul). Furthermore, an isotropic antenna is deployed both at BS and UAV and we feed BS with a transmit power of 43 dBm. We deploy UAV at a horizontal distance of 60 m from the BS, ...
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... In this regard, SINR and throughput are measured without considering other key factors or metrics. The distance is also investigated in [22] and [23] to determine the received power and path loss, respectively. Unfortunately, there are some drawbacks as other performance metrics and factors are ignored. ...
The feature-rich nature of 5G introduces complexities that make its performance highly conditional and dependent on a broad range of key factors, each with unique values and characteristics that further complicate 5G deployments. To address the complexities, this work develops a new modular model based on machine learning on both architecture and service factors (5GPA) that actively contribute to variations in 5G network performance. The objectives are to address the complications during the design and planning phases according to the requirements before 5G deployment, simplify the whole feature-selection process for different deployments, and optimize 5G network performance. The model is implemented and the results are utilized to determine the correlation between the 5GPA factors and the overall performance. Additionally, a simulated 5G dataset is generated and utilized to make predictions on 5G performance based on unseen factors and values of interest. The reliability of the model is validated by comparing the predicted and actual results in the context of quality of service requirements. The results represent a high level of accuracy, with an average of 95%, and low error rates in terms of mean absolute error, mean squared error, and root mean squared error, averaging 7.60e−03, 1.18e−04, and 8.77e−03, respectively.
... Using Victoria fire frequency statistics over a ten-year period and particle swarm optimization [3], the optimal position of UAV is determined for various fires. UAV-aided communication consists of two types of channels, the UAV-ground channel [4][5][6][7][8][9][10] and the UAV-UAV channel [11,12]. UAV-assisted wireless communication operates in three modes: UAV-aided ubiquitous coverage, UAV-aided relaying and UAV-aided information dissemination and data collection. ...
... Two scenarios in which this can be examined are rapid service recovery and base station (BS) offloading in extremely crowded areas. UAV-aided relaying has been considered in [4,9] to maximize reliability for user equipment (UE) without direct communication. ...
... There have been significant works focused on UAV-aided relay communication for the UAV-ground channel [4][5][6][7][8][9][10] and the UAV-UAV channel [11,12]. The authors highlighted the characteristics of mmWave propagation for 5G in [4]. ...
With the integration of unmanned aerial vehicles (UAVs) into fifth generation (5G) networks, UAVs are used in many applications since they enhance coverage and capacity. To increase wireless communication resources, it is crucial to study the trajectory of UAV-assisted relay. In this paper, an energy-efficient UAV trajectory for uplink communication is studied, where a UAV serves as a mobile relay to maintain the communication between ground user equipment (UE) and a macro base station. This paper proposes a UAV Trajectory Optimization (UAV-TO) scheme for load balancing based on Reinforcement Learning (RL). The proposed scheme utilizes load balancing to maximize energy efficiency for multiple UEs in order to increase network resource utilization. To deal with nonconvex optimization, the RL framework is used to optimize the trajectory UAV. Both model-based and model-free approaches of RL are utilized to solve the optimization problem, considering line of sight and non-line of sight channel models. In addition, the network load distribution is calculated. The simulation results demonstrate the effectiveness of the proposed scheme under different path losses and different flight durations. The results show a significant improvement in performance compared to the existing methods.
... This creates several challenges for the network providers to build a mobile network architecture that can handle network data traffic. The 5g mobile network aims to provide a higher number of connected devices, and faster speed to overcome such challenges (Rost et al., 2017;Khan et al., 2020;Alsokkar et al., 2023). ...
The current mobile network core is built based on a centralized architecture, including the S-GW and P-GW entities to serve as mobility anchors. Nevertheless, this architecture causes non-optimal routing and latency for control messages. In contrast, the fifth generation (5G) network will redesign the network service architecture to improve changeover management and deliver clients a better Quality-of-Experience (QoE). To enhance the design of the existing network, a distributed 5G core architecture is introduced in this study. The control and data planes are distinct, and the core network also combines IP functionality anchored in a multi-session gateway design. We also suggest a control node that will fully implement the control plane and result in a flat network design. Its architecture, therefore, improves data delivery, mobility, and attachment speed. The performance of the proposed architecture is validated by improved NS3 simulation to run several simulations, including attachment and inter-and intra-handover. According to experimental data, the suggested network is superior in terms of initial attachment, network delay, and changeover management.
... Designing an intelligent SPPL architecture requires intensive work, which depends on an AI-aided computer vision algorithm and signal power estimation to achieve optimum transmitter locations. As it is known, the communication environment has many obstacles such as trees, buildings, and some other man-made structure that affects the network's quality [48,49]. Thus, as many obstructions as possible should be taken into account throughout propagation planning. ...
The traditional wireless communication systems deployment models require expensive and time-consuming procedures, including environment selection (rural, urban, and suburban), drive test data collection, and analysis of the raw data. These procedures mainly utilize stochastic and deterministic approaches for signal strength prediction to locate the optimum cellular tower (eNodeB) position for 4G and 5G systems. Since environment selection is limited by urban, suburban, and rural areas, they do not cover complex macro and micro variations, especially buildings and tree canopies having a higher impact on signal fading due to scattering and absorption. Therefore, they usually end up with high prediction errors. This article proposes an efficient architecture for the deployment of communication systems. The proposed method determines the effect of the environment via extracting tree and building properties by using a classified 3D map and You Only Look Once (YOLO) V5, which is one of the most efficient deep learning algorithms. According to the results, the mean average precision (mAP) 0.5% and mAP 0.95% accuracies are obtained as 0.96 and 0.45, and image color classification (ICC) findings indicate 77.6% accuracy on vegetation detection, especially for tree canopies. Thus, the obtained results significantly improved signal strength prediction with a 3.96% Mean Absolute Percentage Error (MAPE) rate, while other empirical models’ prediction errors fall in the range of 6.07–15.26%.
... Thus, the factor of non-stationarity should be considered in the U2V mmWave channel model as well. To better understand the characteristics of propagation channel in realistic world and efficiently design, optimize, and evaluate communication technologies, it is vital to accurately model and reproduce the U2V mmWave channels in the laboratory [9]- [12]. ...
Unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) technologies have a promising prospect in the future wireless communication networks. By considering the factors of three-dimensional (3D) scattering space, 3D trajectory, and 3D antenna array, a non-stationary ray-based channel model for UAV-to-vehicle (U2V) mmWave communications is proposed. The computation and generation methods of channel parameters including inter-path and intra-path are developed and illustrated in detail. The inter-path parameters are calculated in a deterministic way based on the given geometric information. The parameters of intra-path rays are generated in a stochastic way based on the statistical properties, which can be obtained in advance by applying a Gaussian mixture model (GMM) on the massive ray tracing (RT) data of different typical scenarios. Meanwhile, a modified method of equal areas (MMEA) is developed to generate the random intra-path variables. To speed up the RT algorithm, the 3D propagation scenario is reconstructed based on the user-defined digital map. Moreover, the theoretical statistical properties of the proposed channel model, i.e., power delay profile (PDP), autocorrelation function (ACF), Doppler power spectrum density (DPSD), cumulative distribution function (CDF), and level crossing rate (LCR) are derived. Finally, a typical U2V channel under urban scenario at 28 GHz is generated and validated. Simulation results demonstrate that the PDP and DPSD of the proposed channel model are accorded well with the theoretical ones, and the ACF, CDF, and LCR are consistent with the measured ones as well.
... Simulationbased analysis of the probability of outage and rate for drone-assisted cooperative communication is discussed in [28] by considering AF relaying at drone and hybrid channel environment. In [29], UAV is considered as an ariel base station for improving the reliability of 5G. For evaluating the network performance in the absence of direct communication Content courtesy of Springer Nature, terms of use apply. ...
This paper consider a Drone Assisted Network Coded Cooperation (DA-NCC) scenario for Line of Sight (LoS) channel environments. For analysing the performance of DA-NCC, Decode-and-Forward (DF) protocol is used at the drone and Selection Combining (SC) is performed at the destination node. An analytical closed-form formulation of the outage probability is devised and proven through simulations to assess network performance of the DA-NCC system. In order to have a better understanding of deterministic networks, a discussion on capacity and a comparison of alternative rectangular designs for deterministic networks are also presented. Insightful results on the relation among drone height, DNC-noise and network geometry may play an important role during the performance analysis of the DA-NCC system. Using closed-form expressions of performance measures, system designers can quickly examine the effects of various parameters on the DA-NCC network’s performance.
... In recent times, unmanned aerial vehicles (UAVs) have been proposed in cellular networks for public safety (PS) scenarios. The UAVs can be used as a relay to connect the emergency zone with the nearest BS [8]. Recently, UAVs have been deployed as a portable BS to provide cellular connectivity to crowded events. ...
Device-to-device (D2D) enables direct communication between two-user equipment (UEs) with or without the involvement of a base station (BS). D2D communication is a vital paradigm to design a reliable public safety network (PSN) and support several services in the sixth generation (6G) systems such as target monitoring, emergency search and rescue, etc. In this experimental study, we demonstrate a cooperative D2D communication system in an emergency scenario to disseminate important information (e.g., the number of people, their IDs and current location) from an affected zone to a deployed command centre in the absence of a BS. We suggest context-aware proximity services-based direct discovery along with unmanned aerial vehicles (UAVs) as a possible solution to implement the future PSN in 6G. Furthermore, we characterize the performance of direct discovery in terms of connectivity reliability and latency in emergency scenarios. Our results show that the discovery ratio is always higher than 90% for SNR values above 20 dB and reaches 100% for SNR values of 23 dB. The end-to-end delay is a low as 18 ms when there is no relay node between two UEs, and increases linearly with the number of hops. Under specific emergency scenarios, the impact of this work is that it is possible to deploy the equipment, establish connectivity, and pass information from the affected zone to deployed command centre in approximately one minute and forty seconds in a real-time lab environment, and four minutes and thirteen seconds in the tested real-life outdoor scenario.
... 1 3 capable structures, UAVs can easily be deployed by controlling remotely or autonomously [3]. They can be utilized in different applications such as disaster management, search and rescue operations, smart agriculture operations, traffic monitoring, relaying for ad hoc networks [4][5][6]. All of these applications require low-cost, scalable, survivable, faster, and reliable protocols to provide an efficient data delivery and channel utilization [7]. ...
... We also demonstrate the impact of non-uniform deployments in Sect. 6. We analyze the proposed model by using the random waypoint mobility model in Sect. ...
Unmanned aerial vehicles have been widely used in many areas of life. They communicate with each other or infrastructure to provide ubiquitous coverage or assist cellular and sensor networks. They construct flying ad hoc networks. One of the most significant problems in such networks is communication among them over a shared medium. Using random channel access techniques is a useful solution. Another important problem is that the variations in the density of these networks impact the quality of service and introduce many challenges. This paper presents a novel density-aware technique for flying ad hoc networks. We propose Density-aware Slotted ALOHA Protocol that utilizes slotted ALOHA with a dynamic random access probability determined using network density in a distributed fashion. Compared to the literature, this paper concentrates on proposing a three-dimensional, easily traceable model and stabilize the channel utilization performance of slotted ALOHA with an optimized channel access probability to its maximum theoretical level, 1/e, where e is the Euler’s number. Monte-Carlo simulation results validate the proposed approach leveraging aggregate interference density estimator under the simple path-loss model. We compare our protocol with two existing protocols, which are Slotted ALOHA and Stabilized Slotted ALOHA. Comparison results show that the proposed protocol has 36.78% channel utilization performance; on the other hand, the other protocols have 24.74% and 30.32% channel utilization performances, respectively. Considering the stable results and accuracy, this model is practicable in highly dynamic networks even if the network is sparse or dense under higher mobility and reasonable non-uniform deployments.
... Designing an intelligent SPPL architecture requires intensive work, which depends on an AI-aided computer vision algorithm and signal power estimation to achieve optimum transmitter locations. As it is known, the communication environment has many obstacles such as trees, buildings, and some other man-made structure that affects the network's quality [46,47]. Thus, as many obstructions as possible should be taken into account throughout propagation planning. ...
The traditional wireless communication systems deployment models require expensive and time-consuming procedures, including environment selection(rural, urban, and suburban), drive test data collection, and analysis of the raw data. These procedures mainly utilize stochastic and deterministic approaches for signal strength prediction to locate the optimum cellular tower(eNodeB) position for 4G and 5G systems. Since environment selection is limited by urban, suburban, and rural areas, they do not cover complex macro and micro variations, especially buildings and tree canopies having a higher impact on signal fading due to scattering and absorption. Therefore, they usually end up with high prediction errors. This article proposes an efficient architecture for the deployment of communication systems. The proposed method determines the effect of the environment via extracting tree and building properties by using a classified 3D map and You Only Look Once (YOLO) V5, which is one of the most efficient deep learning algorithms. According to the results, the Mean Average Precision(mAP) 0.5% and mAP 0.95% accuracies are obtained as 0.96 and 0.45, and Image Color Classification(ICC) findings indicate 77.6% accuracy on vegetation detection, especially for tree canopies. Thus, the obtained results significantly improved signal strength prediction with a 3.96% Mean Absolute Percentage Error(MAPE) rate, while other empirical models’ prediction errors fall in the range of 6.07%-15.26%.
... This creates numerous new challenges for future mobile communication technology operators to handle such data traffic and enhance network capacity. Next-generation (5G) mobile networks aim to provide faster speed and a higher number of connected users to overcome such challenges [7,8]. ...
Reaching a flat network is the main target of future evolved packet core for the 5G mobile networks. The current 4th generation core network is centralized architecture, including Serving Gateway and Packet-data-network Gateway; both act as mobility and IP anchors. However, this architecture suffers from non-optimal routing and intolerable latency due to many control messages. To overcome these challenges, we propose a partially distributed architecture for 5th generation networks, such that the control plane and data plane are fully decoupled. The proposed architecture is based on including a node Multi-session Gateway to merge the mobility and IP anchor gateway functionality. This work presented a control entity with the full implementation of the control plane to achieve an optimal flat network architecture. The impact of the proposed evolved packet Core structure in attachment, data delivery, and mobility procedures is validated through simulation. Several experiments were carried out by using NS-3 simulation to validate the results of the proposed architecture. The Numerical analysis is evaluated in terms of total transmission delay, inter and intra handover delay, queuing delay, and total attachment time. Simulation results show that the proposed architecture performance-enhanced end-to-end latency over the legacy architecture.
