# Intelligent UAV Deployment for a Disaster-Resilient Wireless Network

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## Abstract

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## 1. Introduction

- A multi-UAV and multi-UE system, where UEs are randomly distributed in a disaster struck area is considered.
- Algorithms are proposed to position the UAV ABSs and allocate UEs for each ABS, to maximize the sum spectral efficiency of the network, while maintaining a minimum QoS level for all UEs.
- The proposed scheme is centralized and has a low level of complexity, as only the statistical CSI, locations of the UEs, and the initialized locations of the ABSs are required as inputs.
- The proposed scheme allows the ABSs to directly move from their initial position to the optimal position with a single maneuver, making it a quick and energy efficient approach.
- The available energy levels in the batteries of the ABSs are taken into consideration in the deployment.

## 2. System Model

#### 2.1. Spatial Model

#### 2.2. Channel Model

#### 2.3. Signal-to-Interference-plus-Noise Ratio (SINR)

## 3. Optimal ABS Placement and User Association

#### 3.1. 2D Deployment of the ABSs and the UE Assignment

#### 3.2. ABS Altitude Selection

Algorithm 1: Clustering and matching algorithm with exhaustive search. |

**Block-A**of Algorithm 1. Then, the objective function, which is the STSE, is evaluated for each particle considering the current UE and the position assignments. Their initial position is the LB for all the particles and is also based on these calculated values; the GB position vector of the swarm will be updated. This ends the initialization stage.

**Block-A**of Algorithm 1. The objective function is computed again for the newly updated positions, and the LB and GB positions are updated consequently.

Algorithm 2: Clustering and matching algorithm with PSO |

## 4. Simulation Results and Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**(

**a**) User equipment (UE) distribution in $\mathbb{A}$ and (

**b**) UE distribution in $\mathbb{B}$ (disaster region).

**Figure 2.**System model illustration of the information and interference signals for ${N}_{\mathrm{UAV}}=3$ and ${N}_{\mathrm{UE}}=3$.

**Figure 3.**Illustration of ABS placement and UE association obtained using the approach in [14], where ${R}_{B}$ = 2000 m, ${\alpha}_{N}=2.5$, ${\alpha}_{L}=2$, ${\lambda}_{U}=2\times {10}^{-4}$/m${}^{2}$, $\delta =0$, ${N}_{\mathrm{UAV}}=3$, ${H}^{*}$= 300 m, ${N}_{T}=70$. The position of the ABS is represented using

**X**. The three colors differentiate the UE clusters at a particular stage. (

**a**–

**h**) illustrate the $1\mathrm{st}$, …, $5\mathrm{th}$, $7\mathrm{th}$, $9\mathrm{th}$ and $11\mathrm{th}$ adaptive stages, respectively

**Figure 4.**Illustration of the movement of the aerial base stations (ABSs) in the 2D plane for suburban environment. The position of the ABS is represented using

**x**. The three colors differentiate the UE clusters of the respective ABSs. (

**a**) Initial 2D position of the ABSS. (

**b**) Movement of the ABSs to the computed position. The solid arrow represents the actual ABS movement. The doted lines represent the adaptive process (does not represent the movement) performed at the CC. ${R}_{B}$ = 2000 m, ${\alpha}_{N}=2.5$, ${\alpha}_{L}=2$, ${\lambda}_{U}$ = 2 × 10${}^{-4}$/m${}^{2}$, $\delta =0$, ${N}_{\mathrm{UAV}}=3$, ${H}^{*}$ = 300 m, ${N}_{T}=70$.

**Figure 5.**(

**a**) Global best, local best, position, and the velocity in the ($n-1$)th iteration. (

**b**) Velocity in the $n\mathrm{th}$ iteration as a weighted vector addition of previous velocity components and the position in the $n\mathrm{th}$ iteration.

**Figure 6.**Total spectral efficiency vs. altitude of the ABS (comparison between Algorithm 1-based deployment and random deployment).

**Figure 7.**Total spectral efficiency vs. altitude of the ABS (comparing Algorithm 1-based deployment, random deployment, and equidistant deployment).

**Figure 8.**Average coverage probability vs. Signal-to-Interference-Plus-Noise Ratio (SINR) threshold (comparison between Algorithm 1-based deployment and random deployment).

**Figure 10.**(

**a**) Maximum achievable total spectral efficiency (TSE) vs. user intensity (

**b**). Energy consumption for maneuvering vs. user intensity.

Notation | Description |
---|---|

${x}_{j},{y}_{j}$ | 2D- Coordinates of the $j\mathrm{th}$ ABS |

${B}_{i}$ | 2D- Coordinates of the $i\mathrm{th}$ UE |

${\lambda}_{U}$ | Intensity of the UE distribution |

${R}_{B}$ | Radius of the isolated region |

${N}_{\mathrm{UAV}}$ | Required Number of UAVs |

${N}_{\mathrm{UE}}$ | Number of UEs in the isolated region |

${N}_{T}$ | Maximum number of UE that can be supported by an ABS |

$d(j,i)$ | 2D euclidean distance from $j\mathrm{th}$ ABS to $i\mathrm{th}$ UE |

${\alpha}_{q}$ | Large-scale path loss exponent |

${g}_{q}$ | Small-scale fading amplitude |

${p}_{t}^{j}$ | Transmission power of the $j\mathrm{th}$ ABS |

${P}_{r}(j,i)$ | Received signal power at $i\mathrm{th}$ UE from the $j\mathrm{th}$ ABS |

${I}_{\mathrm{Agg}}(i)$ | Aggregated interference experienced by the $i\mathrm{th}$ UE |

${E}_{\mathrm{mob}}^{j}$ | Required energy for mobility of the $j\mathrm{th}$ ABS |

${E}_{j}$ | Available energy for mobility at the $j\mathrm{th}$ ABS |

${\eta}_{h}$, ${\eta}_{v}$ | Energy consumption per unit distance to horizontal and vertical movement respectively |

${\varphi}_{j}$ | Assigned user list of the $j\mathrm{th}$ ABS |

$P(LOS,{\theta}_{j}^{i})$ | probability of line of sight from $j\mathrm{th}$ ABS to the $i\mathrm{th}$ UE |

$a\phantom{\rule{0.222222em}{0ex}},\phantom{\rule{0.222222em}{0ex}}b$ | Constants which reflects environmental characteristics |

${h}_{q}(j,i)$ | Channel gain from the $j\mathrm{th}$ ABS to the $i\mathrm{th}$ UE |

$SIN{R}_{\mathrm{min}}$ | Minimum SINR threshold which reflects the minimum QoS requirement |

${G}_{L}$ | Gain achieved comparing to the previous step |

${H}_{j}$ | Altitude of the $j\mathrm{th}$ ABS |

${H}^{*}$ | Common optimal altitude |

${N}_{H}$ | Number of discrete altitude levels considered in Algorithm 1 |

${H}_{j}^{*}$ | Optimal altitude of the $j\mathrm{th}$ ABS |

$\delta $, $\tilde{\delta}$ | Minimum gain expected in Algorithm 1 and Algorithm 2 |

${h}_{\mathrm{min}}$,${h}_{\mathrm{max}}$ | Minimum and maximum altitude allowed to hover an ABS |

${W}_{k}(n)$ | Position of the ${k}^{\mathrm{th}}$ particle at $n\mathrm{th}$ iteration in PSO space |

${W}^{\mathrm{Gb}}(n)$ | Global best position at the $n\mathrm{th}$ iteration in PSO space |

${W}_{k}^{\mathrm{Lb}}(n)$ | Local best position of the ${k}^{\mathrm{th}}$ particle at $n\mathrm{th}$ iteration in PSO space |

${V}_{k}(n)$ | Velocity of $k\mathrm{th}$ particle at $n\mathrm{th}$ iteration in PSO space |

${J}_{k}(n)$ | Objective function value of the $k\mathrm{th}$ particle at $n\mathrm{th}$ iteration in PSO space |

${c}_{1},\phantom{\rule{0.222222em}{0ex}}{c}_{2}$ | Local learning coefficient and swarm learning coefficient respectively |

$\xi $ | Inertia weight of the swarm particle |

${N}_{\mathrm{pop}}$ | Number of particles in the swarm population |

${G}_{P}$ | Spectral efficiency gain achieved comparing to the previous iteration in PSO |

${N}_{G}$ | Number of continuous iterations without a gain in the spectral efficiency |

$\mathsf{\Gamma}$ | Threshold to exit the PSO algorithm |

Parameter | Value | Parameter | Value | Parameter | Value |
---|---|---|---|---|---|

${\lambda}_{u}$ | $4\times {10}^{-4}$/m${}^{2}$ | ${R}_{B}$ | 2000 m | r | 15 Mbps |

${N}_{T}$ | 40 | ${E}_{T}$ | 1 kJ | ${\eta}_{h}$ | 0.1 J/m |

${\alpha}_{L}$ | 2 | ${\alpha}_{N}$ | 2.5 | ${\eta}_{v}$ | 1 J/m |

${p}_{t}^{j}$ | 30 dBm | $\delta $,$\tilde{\delta}$ | 0 | ${N}_{\mathrm{pop}}$ | 20 |

$\mathsf{\Gamma}$ | 4 | ${N}_{0}$ | −80 dBm | ${\phi}_{1},{\phi}_{2}$ | random in [0, 1] |

a | 4.8800 (Suburban) | b | 0.4290 (Suburban) | $SIN{R}_{\mathrm{min}}$ | −30 dB |

9.6117 (Urban) | 0.1581 (Urban) | ${h}_{\mathrm{min}}$ | 50 m | ||

12.0810 (Dense urban) | 0.1140 (Dense urban) | ${h}_{\mathrm{max}}$ | 3000 m | ||

24.5960 (High-rise urban) | 0.1248 (High-rise urban) | $\xi $ | 0.5175 |

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**MDPI and ACS Style**

Hydher, H.; Jayakody, D.N.K.; Hemachandra, K.T.; Samarasinghe, T.
Intelligent UAV Deployment for a Disaster-Resilient Wireless Network. *Sensors* **2020**, *20*, 6140.
https://doi.org/10.3390/s20216140

**AMA Style**

Hydher H, Jayakody DNK, Hemachandra KT, Samarasinghe T.
Intelligent UAV Deployment for a Disaster-Resilient Wireless Network. *Sensors*. 2020; 20(21):6140.
https://doi.org/10.3390/s20216140

**Chicago/Turabian Style**

Hydher, Hassaan, Dushantha Nalin K. Jayakody, Kasun T. Hemachandra, and Tharaka Samarasinghe.
2020. "Intelligent UAV Deployment for a Disaster-Resilient Wireless Network" *Sensors* 20, no. 21: 6140.
https://doi.org/10.3390/s20216140