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Unmanned aerial vehicle (UAV) communication can be used in overcrowded areas and either during or postdisaster situations as an evolving technology to provide ubiquitous connections for wireless devices due to its flexibility, mobility, and good condition of the line of sight channels. In this paper, a single UAV is used as an aerial relay node to provide connectivity to wireless devices because of the considerable distance between wireless devices and the ground base station. Specifically, two path loss models have been utilized; a cellular-to-UAV path loss for a backhaul connection and an air-to-ground path loss model for a downlink connection scenario. Then, the tradeoff introduced by these models is discussed. The problem of efficient placement of an aerial relay node is formulated as an optimization problem, where the objective is to maximize the total throughput of wireless devices. To find an appropriate location for a relay aerial node that maximizes the overall throughput, we first use the particle swarm optimization algorithm to find the drone location; then, we use three different approaches, namely, (1) the equal power allocation approach, (2) water filling approach, and (3) modified water filling approach to maximize the total users’ throughput. The results show that the modified water filling outperforms the other two approaches in terms of the average sum rate of all users and the total number of served users. More specifically, in the best-case scenario, it was observed that the average sum rate of the modified water filling is better than the equal power allocation and ensuring 100% coverage. In contrast, the water filling provides a very close average sum rate to the modified water filling, but it only provides a 28% user coverage.

While most UAV research works in wireless communication demonstrate UAV uses as an aerial base station [

Classification of the deployment of UAVs as relay node based on the objective function.

UAV can be deployed as a relay node when the infrastructure of the terrestrial base station is damaged due to natural disasters or overloaded during crowded events [

On the one hand, many works utilize UAV as a relay node in wireless communication. For example, the study in [

On the other hand, Zhan et al. in [

Moreover, in [

Inspired by the aforementioned studies on relay’s UAV network, this work proposed to use a single UAV acts as a relay node between a remote terrestrial base station and the ground users, where the objective is to find the efficient placement of the UAV such that the overall throughput of the network is maximized. The cellular-to-UAV path loss model presented in [

We summarize the main contributions of this work as follows:

We utilize a cellular-to-UAV path loss model for the backhaul link [

We formulate the problem of efficient placement of a single UAV, where the objective is to maximize ground users’ total throughput

We propose to use the particle swarm optimization algorithm [

The rest of this paper is structured as follows. Section

In the literature, many researchers consider the deployment strategies of UAVs as relay nodes by optimizing different objective functions such as (1) minimizing its transmit power and maximizing energy efficiency [

The authors in [

Many researchers utilized the path loss model in [

The study in [

In [

In [

In [

Works from previous studies use a drone as a relay node under the assumption of free space propagation to increase the throughput of wireless devices. This condition may not be realistic for urban environments in particular. Also, they study the optimal 3D placement of an aerial relay node that can enhance multiple objective functions. In this paper, we plan to use realistic path loss models for an aerial relay node to improve the throughput of wireless devices where the data rates between the ground base station and users are low due to the considerable distance.

Let

System model.

Most of the air-ground channel measurements focus on large-scale statistics such as path loss exponent and shadow fading [

The path loss model between the aerial relay node and a ground user

Note that there is a critical trade-off in the path loss models when the horizontal distance between the aerial relay node and the wireless device changes. When this distance increases, the path loss between the drone and ground wireless device (i.e.,

Path loss model tradeoff.

Consider a wireless communication between the ground base station and a ground user via the aerial relay node. The data rate of the link that connected the ground base station with the aerial relay node is given by:

where

where

where

We aim to find an efficient placement of the aerial relay node and the power allocation such that the total throughput of ground users is maximized. The optimization problem is given by:

Here, the first constraint set ensures that the data rate of the link that connected the aerial relay node with a ground user is less than or equal to the data rate of the link that connected the ground base station with the aerial relay node. The second constraint set guarantees that the signal-to-noise ratio of each ground user

Finding the optimal 3D placement of the aerial relay node and the power allocation is generally difficult because the optimization problem is nonconvex. Therefore, in the next section, to find an efficient solution for the optimization problem, we present the particle swarm optimization algorithm and water-filling algorithm.

The aerial relay node placement is first optimized using the particle swarm optimization algorithm [

First, we apply the particle swarm optimization algorithm at each 2D plane (

The algorithm for particle swarm optimization, shown in Algorithm

where

The time complexity of the PSO algorithm will depend on the number of iterations (

1: Input:

2: The lower and upper bounds of devision variable

3:

4:

5:

6:

7: Each particle

8: Each particle

9: Find the cost of particle

10: Let the best location of particle

11: Let the best cost of particle

12:

13:

14:

15:

16:

17:

18:

19: Find the velocity, position and cost for particle

20:

21:

22:

23:

24: Find an efficient placement of a UAV:

25:

26:

27:

28:

29:

Consider a coverage subarea denoted as

Scenario description: the disaster affected subarea,

In this work, the particle swarm optimization algorithm is first used to find an efficient 3D placement of the aerial relay node (the UAV), such that the total path loss of the ground users is minimized. Then, the equal power distribution, water filling, and modified water filling approaches are used to distribute the transmission power of the UAV

In this work, we consider using a single UAV in a disaster situation, where the objective is to serve all ground users such that the sum data rate is maximized.

Consequently, a modified water-filling algorithm is utilized. In this algorithm, we first distribute part of the transmitted power among the users, satisfying that each user has an amount of power that makes the user signal-to-noise ratio

In this scenario, the UAV is used as a relay node immediately after the disaster occurred, where the terrestrial base stations are disrupted and went out of service in the disaster regions, as shown in Figure

Users distribution inside affected subarea,

System and simulation parameters.

Simulation parameters | System and algorithm parameters | ||||
---|---|---|---|---|---|

Subarea | Carrier frequency | 2 GHz | |||

Number of ground users | Noise power | -120 dBm | |||

Max. GBs transmit power | Signal-to-noise threshold | SNR | 35 dB | ||

Max. UAV transmit power | PSO population size | 50 | |||

Total UAV bandwidth | 50 MHz | Max # of iterations of PSO | 50 | ||

Total GBs bandwidth | 100,75 MHz | Environment parameters | 9.6, 0.28 | ||

Base station location | (7000,500) | Environment parameters | 1, 20 |

Figure

Average sum rate vs. total UAV transmission power.

Since we assume the disaster scenario, the aim is to maximize the sum data rate and serve the largest possible users. Figure

Average served users vs. total UAV transmission power.

In this scenario, we consider that 100 wireless devices are nonuniformly distributed inside a square area with

Average sum rate vs. ground base station power at 75 and 100 MHz bandwidth.

Average sum rate vs. BS power at 75 MHz bandwidth

Average sum rate vs. BS power at 100 MHz bandwidth

Figure

Average served users vs. distance (m).

Table

Simulation results for the proposed algorithms within a subarea

Step | Algorithm | Efficient UAV placement | Total path loss UAV to users (dB) | GBs-to-UAV backhaul path loss (dB) | Average sum rate | CtU average rate | Num. of served users |
---|---|---|---|---|---|---|---|

1 | Eq. power distr. | (0, 498.4, 442) | 3751.18 | 102.99 | 424.3 | 957.1 | 100 |

2 | Eq. power distr. | (500, 502.3, 271.9) | 3349.29 | 99.061 | 550.4 | 1089 | 100 |

3 | Eq. power distr. | (1000, 494.8, 429.5) | 3727.80 | 106.22 | 417.5 | 959.8 | 100 |

4 | Eq. power distr. | (1500, 499.1, 706.6) | 4183.68 | 115.25 | 269.2 | 674.5 | 100 |

5 | Eq. power distr. | (2000, 491.7, 997.2) | 4503.21 | 122.67 | 170.5 | 434.7 | 100 |

6 | Eq. power distr. | (3000, 493.5, 1000) | 5281.48 | 123.0 | 27.52 | 408.6 | 100 |

7 | Eq. power distr. | (4000, 500, 1000) | 6227.37 | 123.90 | 0.41 | 418.8 | 27 |

8 | Eq. power distr. | (5000, 500, 1000) | 6801.04 | 125.56 | 0.02 | 519.2 | 0 |

9 | Eq. power distr. | (6000, 499, 1000) | 7123.16 | 124.89 | 0.004 | 801.9 | 0 |

10 | Eq. power distr. | (7000, 496.8, 1000) | 7317.82 | 117.35 | 0.0016 | 1809 | 0 |

The modified water-filling algorithm is more feasible in disaster scenarios since a communication network should be provided to most users in the affected area to give them the ability to request assistance in case of need. In fact, in modified water-filling algorithm, we accomplished two goals; first, we ensure that all users are served even at low

Due to the large distance between the ground base station and the users located inside the coverage region where the natural disaster happened, a UAV was used as an aerial relay node to provide wireless coverage for wireless devices. In this work, a cellular-to-UAV path loss model was utilized for the backhaul connection between the ground base station and the UAV. Moreover, the air-to-ground path loss model was utilized for a downlink connection between the UAV and the ground users. Then, the trade-off introduced by these models was discussed. Specifically, the problem of finding an efficient placement of a UAV was formulated as an optimization problem, where the objective is to maximize wireless devices’ total throughput. The particle swarm optimization and the modified water-filling algorithm were used to find an efficient UAV placement that maximizes the overall throughput. As future work, we propose to study the uplink scenario utilizing the ground-to-air path loss model. Moreover, we propose to extend this work using mm-wave bands, which provide very high bandwidth.

Data are available on request.

The authors declare that they have no conflicts of interest.

The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education – Kingdom of Saudi Arabi for funding this research work through the project number-6864 2020 IF.