A Novel Deployment Method for UAV-mounted Mobile Base Stations

Unmanned aerial vehicles (UAVs) have a great potential for improving the performance of wireless communication systems due to their high mobility. In this correspondence, we study a UAV-enabled data collection system, where a UAV is dispatched to collect a given amount of data from a ground terminal (GT) at fixed location. Intuitively, if the UAV flies closer to the GT, the uplink transmission energy of the GT required to send the target data can be more reduced. However, such UAV movement may consume more propulsion energy of the UAV, which needs to be properly controlled to save its limited on-board energy. As a result, the transmission energy reduction of the GT is generally at the cost of higher propulsion energy consumption of the UAV, which leads to a new fundamental energy tradeoff in ground-to-UAV wireless communication. To characterize this tradeoff, we consider two practical UAV trajectories, namely circular flight and straight flight. In each case, we first derive the energy consumption expressions of the UAV and GT and then find the optimal GT transmit power and UAV trajectory that achieve different Pareto optimal tradeoffs between them. Numerical results are provided to corroborate our study.

Nowadays, in an Unmanned Aerial Vehicle (UAV)- enabled Integrated Sensing and Communication (ISAC) System, a UAV can provide on-demand communication services to ground terminals (GTs) by cooperating with infrastructure networks. Furthermore, with radio frequency (RF) sensing capabilities enabled by the ISAC signal, UAVs can track the locations of GTs and thereby provide better communication services. However, due to the high mobility and SWAP (size, weight, and power) constraints of UAVs, as well as the complex working environment, it is difficult to achieve high-accuracy GT location sensing. As a result, UAVs cannot continuously support highdata-rate communication services for GTs. To address the above issues, we adopt Federated Learning (FL) between UAVs and a central server to improve the accuracy of GT location sensing, and jointly optimize UAV trajectory and resource allocation to ensure the Quality of Service(QoS) of communication services. FL is employed to perform distributed learning for the maximum likelihood estimation (MLE) of GT locations. We design a gametheoretic method to enhance UAV operations under the FL framework. This method ensures that each UAV contributes effectively to location sensing. On the other hand, the formulated joint trajectory and resource optimization problem is complex and subject to multiple non-convex constraints. We apply the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method to solve the problem and obtain a sub-optimal solution. Numerical results validate that, with our proposed solution, UAVs can deliver communication services with enhanced QoS in terms of higher data rates, while also operating more energy-efficiently in the considered system compared to benchmark solutions.

As the mobile networks become even denser and the traffic demand is increasing drastically, it is becoming more heterogeneous. Recently, it is intensive to investigate the Cell-Free Massive multiple-input multiple-output Massive (MIMO) system, where a large number of access points (APs) simultaneously serve a much smaller number of users, and the APs and users are clustered to provide good service for all users. This paper designs a mobile base station (MBS)-assisted Cell-Free Massive MIMO system: Utilizing an MBS deployed on the unmanned aerial vehicle (UAV) to form an air-ground heterogeneous system with the Cell-Free Massive MIMO system and offload part of traffic to the MBS to further improve the system performance. To provide better service to all users, this paper design an MBS flight trajectory optimal method to improve the wireless coverage performance and user experience, and proposed a joint power allocation algorithm based on the consideration of the fairness of the service quality. The simulation results indicate that the performance of the system designed in this paper has a significant improvement compared with the normal Cell-Free Massive MIMO system.

Aiming at the problem of insufficient ground terminal (GT) energy in unmanned aerial vehicle (UAV) aided wireless powered communication networks (WPCN) using radio frequency energy harvesting, the minimum throughput maximization resource allocation strategy based on magnetic coupling resonant wireless power transfer is studied in this paper. In the original UAV-aided WPCN, a power transfer UAV equipped with magnetic coupling resonance device is introduced. At the same time, each GT installs a receiving coil. Through the magnetic coupling resonance, the power transfer UAV provides sufficient energy for the GT in turn. In order to maximize the minimum throughput of all GTs, the UAV trajectory for receiving information, GT transfer power and timeslot allocation proportion are jointly optimized. Since the optimization problem is nonconvex, the appropriate convex bound of nonconvex constraint is derived by concave-convex process, and the suboptimal solution of the original problem is obtained by iterative optimization algorithm. The simulation results show that the proposed method has a certain degree of improvement in the minimum throughput compared with the method of fixed UAV trajectory and fixed timeslot allocation ratio.

Unmanned aerial vehicles (UAVs) have played an important role in air-ground integration network. Especially in Internet of Things (IoT) services, UAV equipped with communication equipments is widely adopted as a mobile base station (BS) for data collection from IoT devices on the ground. In this paper, we consider an air-ground network in which the UAV flies straightly to collect information from the IoT devices in a 2-D plane based on the CSMA/CA protocol. Due to UAV's continuous mobility, the communication durations of devices in different locations with UAV are not only time-limited, but also vary from each other. To analyze the throughput performance of uplink multiple access control (MAC) protocol, we propose a new analysis model to deal with the communications heterogeneity in the network. Firstly, we divide the devices in the coverage into different clusters according to their communication durations. Then, a quitting probability indicating the probability that a device quits the UAV's coverage at each time slot is clarified. A modified three-dimensional Markov chain model adopting the quitting probability and cluster division is developed for the performance analysis. Besides, we also propose a modified CSMA/CA protocol which fully considers the heterogeneity of the access time and adaptively allocates the time resource among the devices in different clusters. Finally, the effects of retry limit, initial contention window size, the density of the devices, UAV's speed and coverage area are discussed in the simulation section.

Unmanned Aerial Vehicles (UAVs) are considered an important element in wireless communication networks due to their agility, mobility, and ability to be deployed as mobile base stations (BSs) in the network to improve the communication quality and coverage area. UAVs can be used to provide communication services for ground users in different scenarios, such as transportation systems, disaster situations, emergency cases, and surveillance. However, covering a specific area under a dynamic environment for a long time using UAV technology is quite challenging due to its limited energy resources, short communication range, and flying regulations and rules. Hence, a distributed solution is needed to overcome these limitations and to handle the interactions among UAVs, which leads to a large state space. In this paper, we introduced a novel distributed control solution to place a group of UAVs in the candidate area in order to improve the coverage score with minimum energy consumption and a high fairness value. The new algorithm is called the state-based game with actor–critic (SBG-AC). To simplify the complex interactions in the problem, we model SBG-AC using a state-based potential game. Then, we merge SBG-AC with an actor–critic algorithm to assure the convergence of the model, to control each UAV in a distributed way, and to have learning capabilities in case of dynamic environments. Simulation results show that the SBG-AC outperforms the distributed DRL and the DRL-EC3 in terms of fairness, coverage score, and energy consumption.

Due to their portability and ability to be deployed at low altitudes, unmanned aerial vehicles (UAVs) are a viable option for usage as mobile base stations in the air. The channels between UAVs and ground users pose a threat of substantial co-channel interference in the network with the deployment of UAVs due to line-of-sight links. This research develops coordinated multi-point (CoMP) technique based on joint transmission approach in a UAV-enabled cellular system to handle this problem, coupled with the proper positioning of UAVs. Although CoMP has the potential to improve network performance, its processing time and overhead make it challenging to adopt across the network. Therefore, a user-centric clustering strategy is used to randomly disperse the network’s nodes into overlapping groups. By optimizing CoMP clusters, beamforming vectors, and UAV flight paths, we aim to keep communication energy usage to a minimum. The optimization problem is formulated, considering the limitations imposed by the UAVs’ mobility and maximum transmit power. An effective method is proposed to deal with the nonconvexity of the formulated problem. First, a circular filling strategy is used to determine the flight trajectories of the UAVs. Then, we reformulate the non-convex constrained problem into an equivalent convex optimization problem and optimize the CoMP clusters and the beamforming vectors jointly. The effectiveness of the proposed solution has been numerically validated.

In this paper, we consider a ground terminal (GT) to an unmanned aerial vehicle (UAV) wireless communication system where data from GTs are collected by an unmanned aerial vehicle. We propose to use the ground terminal-UAV (G-U) region for the energy consumption model. In particular, to fulfill the data collection task with a minimum energy both of the GTs and UAV, an algorithm that combines optimal trajectory design and resource allocation scheme is proposed which is supposed to solve the optimization problem approximately. We initialize the UAV’s trajectory firstly. Then, the optimal UAV trajectory and GT’s resource allocation are obtained by using the successive convex optimization and Lagrange duality. Moreover, we come up with an efficient algorithm aimed to find an approximate solution by jointly optimizing trajectory and resource allocation. Numerical results show that the proposed solution is efficient. Compared with the benchmark scheme which did not adopt optimizing trajectory, the solution we propose engenders significant performance in energy efficiency.

This paper presents measurement and evaluation of radiation power density emitted from mobile cellular base stations in Abuja and it’s environ. Forty (40) mobile base stations with ten (10) each from AIRTEL, ETISALAT, GLO and MTN located in areas with high population were randomly examined across Abuja. Broadband analysis method was employed in the measurement of the radiation power density. The results obtained shows that most of the base stations recorded lower radiation power density below the safety standard value of 4.5watt/m2 , 22.5 watt/m2 for 900MHz frequency though few base stations recorded above standard value: GLO-BS02, GLO-BS03 and MTN-BS02 recorded 59.02 mW/m2, 39.76 mW/m2 and 46.70 mW/m2 respectively. The result also revealed that as distance increases, the mean power density reduces. This is to said that human exposure to radio frequency radiation have less or no health hazard at distance far away from the mobile cellular base station.

In traditional cellular networks with fixed base stations, the channel reuse pattern is static and deterministic. When the cell layout is dynamic, due to the mobility of base stations, the cluster of cells within cochannel interference range changes with time. Consequently, the channel reuse pattern is highly dynamic. Moreover, base stations also need wireless channels to communicate amongst themselves. A communication session between a pair of nodes may have to switch channels due to the movement of other nodes into the neighborhood. None of the existing dynamic channel allocation algorithms for cellular networks works in such a system. Hence, there is a need for new wireless channel allocation algorithms for virtual cellular networks with mobile base stations. In this paper, principles of mutual exclusion pertaining to distributed computing systems are employed to develop such an algorithm. The inter-base-station wireless links are referred to as backbone links, while the base station to mobile node links are referred to as short-hop links. The proposed algorithm is distributed, dynamic, and deadlock-free. Disjoint sets of channels are used for backbone and short-hop links. The distributed nature of the channel allocation scheme leads to scalability and robustness, as the responsibility is no longer centralized at the mobile telecommunications switching office (MTSO). Instead, it is shared among all the mobile base stations. In addition, the issue of channel rearrangement is addressed. Channel rearrangement is the switching of channels, performed to prevent cochannel interference, when mobile base stations using the same channels, hitherto not in interference range, come within the range of each other. If multiple channels are available to support a communication session, the channel selection policy can have a significant impact on performance. A random selection from the set of available channels yields better performance at low to moderate channel demand. This is in contrast to ordered channel selection (from one end of the spectrum), which always yields the best performance for cellular networks. Results obtained by simulating the algorithm are consistent with the theoretically obtained values.

Wireless network coverage can be disrupted by unexpected events. Temporary connectivity can be necessary in regions that are outside of the wireless communication infrastructure. One solution to that is the use of Unmanned Aerial Vehicles (UAV) as mobile base stations. UAVs can act like temporary “range extenders” in unexpected or temporary events. Low Altitude Platforms (LAPs), like drones, are specific types of UAVs that can be used for range extension tasks. As the drones have limited energy for their operation, their deployment should be optimized to get maximum possible coverage of the desired region with the constraints related to the capability of the overall system. This study proposed a novel framework for optimum coverage of the desired region with given drones by using heuristic optimization methods. Multi-objective optimization considers minimizing overlapping regions between drones, overflowing regions for the drones (coverage outside of the desired region), and flight distance of the drones from/to the base station. The trade-offs among constraints are resolved by using priority based optimization in which by setting weights the “user” can prioritize one or more constraints over the others.

The ever-increasing number of mobile telecommunication base station as a result of increasing demand for broadband applications has raised a growing concern and worry over the health implications and safety of the radiations from these base stations by the resident of Ekiti State and Nigeria in Nigeria. Measurement of radio frequency was conducted in this research in order to study the electromagnetic field radiation level in Ekiti State Nigeria. Investigation was conducted with the four available mobile operators with the three sub-frequency band viz 900 MHz, 1800 MHz and 2100 MHz. The power density of radio frequency radiation was estimated through measurement with the aid of A 3-Axis RF Radiation Strength Meter TM-196 and Handheld Spectrum Analyzer Model NA-773, 144/430. The peak and average power density was computed using the method of theoretical calculation and the safety distance from the antennas were estimated. The measured and the calculated values were compared with the international commission on non-ionizing radiation protection (ICNIRP) standard for public and occupational exposure level. Results show that the radiations from the base stations adheres to the standard provided by ICNIRP

Optimal deployment and movement of multiple unmanned aerial vehicles (UAVs) is studied. The considered scenario consists of several ground terminals (GTs) communicating with the UAVs using variable transmission power and fixed data rate. First, the static case of a fixed geographical GT density is analyzed. Using high resolution quantization theory, the corresponding best achievable performance (measured in terms of the average GT transmission power) is determined in the asymptotic regime of a large number of UAVs. Next, the dynamic case where the GT density is allowed to vary periodically through time is considered. For one-dimensional networks, an accurate formula for the total UAV movement that guarantees the best time-averaged performance is determined. In general, the tradeoff between the total UAV movement and the achievable performance is obtained through a Lagrangian approach. A corresponding trajectory optimization algorithm is introduced and shown to guarantee a convergent Lagrangian. Numerical simulations confirm the analytical findings. Extensions to different system models and performance measures are also discussed.

This paper proposes a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration in mobile access applications where the UAVs work as mobile base stations. The primary objective of the proposed algorithm is to establish reliable mobile access networks for vehicle-to-everything (V2X) communications. This paper jointly considers energy-efficient UAV operation and reliable wireless communication services for realizing robust mobile access services. For the energy-efficient UAV operation, the reward function formulation of our proposed MADRL algorithm contains the features for UAV energy consumption models in order to realize efficient operations. Furthermore, for the reliable wireless communication services, the quality of service (QoS) requirements of individual users are considered as a part of reward function. Furthermore, this paper considers 60, GHz millimeter-wave (mmWave) mobile access for utilizing the benefits of i) ultra-wide-bandwidth for multi-Gbps high-speed communications and ii) high-directional communications for spatial reuse that is obviously good for avoiding interference among densely deployed users. Lastly, the comprehensive and data-intensive performance evaluation of the proposed MADRL-based algorithm for multi-UAV positioning is conducted. The results of these evaluations demonstrate that the proposed algorithm outperforms other existing algorithms.

Wireless Multihop Infrastructures (WMIs) are promising network platforms for the applications such as smart cities, emergency responses, and outdoor events. On the other hand, the low cost maintenance and large coverage of WMIs are subject to the network dis-connectivity, which can be caused by node damage or energy depletion, especially in the emergency situations and off-grid locations. The end to end reach ability is essential for the WMI to provide the network service to users. To enable the sustainable network reach ability in the disconnected WMI, this paper studies local maintaining and recovery of network infrastructure by utilizing autonomous mobile base stations (MBSs). The proposed MBS approach, MBS-Fit, is a distributed mechanism that addresses pause control problem in terms of pause length and fitful pause positions such that MBS can provide reach ability for the disconnected WMIs. The network reach ability is mainly measured by the amount of packets arrived from users to the gateway through the WMI. We adopt packet level evaluation to study the automatic interactions of MBS with the static WMI objects for local recovery of network infrastructure. Simulation results reveal that the proposed MBS-Fit approach achieves up to 30 percent improvement of network reach ability in term of packet delivery compared with conventional MBS. MBS-Fit mechanism also enables up to 80 percent improvement in terms of packet forwarding number at MBS.