# Efficient Resource Allocation for Backhaul-Aware Unmanned Air Vehicles-to-Everything (U2X)

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## Abstract

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## 1. Introduction

#### Our Contributions

- A backhaul-aware U2X scenario with the support of multi-layer UAVs is presented along with a resource allocation problem with the constraints on the number of transfers.
- Mutual agreement reward theory is applied to understand the problem of resource allocation in U2X.
- The entire problem is resolved using coordinated resource allocation, which is accounted for using a reward-jump mechanism.
- Hazard tracking and ownership procedures are used to decide the control over the network and reshuffling amongst layers.
- Monte-Carlo simulations are used to evaluate the proposed approach by generating scenarios with various numbers of failures for different numbers of UAVs.

## 2. Related Works

## 3. Proposed Approach

#### 3.1. System Model and Problem Formulation

#### 3.2. Coordinated Resource Allocation

- The proposed approach aims at maximizing the resources handled by UAVs while decreasing the exchange or limiting the shifting users across the UAVs, except for the scenarios wherein UAVs fail in batches. Irrespective of such failures, the proposed approach controls the network and offloads the traffic with limiting iterations.
- For multiple tiers scenarios, the UAVs which support the backhaul links are the ones which make a decision on the load sharing capabilities of fronthaul UAVs by considering $\mathcal{U}$, $\mathcal{M}$, and $\mathcal{N}$, whereas for scenarios with single tier UAVs, the decision on load sharing is only taken based on the mutual agreement reward theory only between $\mathcal{U}$ and $\mathcal{M}$. It is to be considered that the former offers better evaluation in case of failures, while the latter is effective in low-overheads with less resistance to failures.

- Let ${\mathcal{J}}_{R}$ be an incremental reward function with an initial value at 0, which increases as the number of times the network obeys the set constraints. This increment is the actual value assigned to ${\mathcal{J}}_{R}$, and it may vary for different scenarios. In certain cases, it may follow an increment or decrement value depending on the failure rate of the network. Additionally, the reward function may vary for the types of entities and their connectivity as shown in Figure 2.
- Once ${\mathcal{J}}_{R}$ is defined, a threshold is set for each of the categories and the network is evaluated against it to allow a decision on load balancing/resource allocation/offloading.
- The value of ${\mathcal{J}}_{R}$ is also used to take a critical decision of the entity, which will be dominating the decision on resource allocation.

#### 3.3. Backhaul Coordination

#### 3.4. U2X Coordination

#### 3.5. Hazard Tracking and Ownership

## 4. Performance Evaluation

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**An exemplary illustration of reward-jump system used for mutual agreement between the entities to ensure smooth and low-overhead offloading.

**Figure 3.**Flow chart representing all of the procedures and work flow for energy efficient resource allocation in U2X.

Articles | Key Contribution | R1 | R2 | U2X |
---|---|---|---|---|

[26] | Energy-efficient resource allocation scheme | Yes | No | No |

[27] | Distributed tasks allocation scheme | Yes | No | No |

[24] | Resources allocation mechanism for minimize packet transmission delay | Yes | No | No |

[28] | Node placement and communication resource allocation scheme | Yes | No | No |

[29] | Real-time optimal resource allocation | Yes | No | No |

[30] | Resource allocation for a three-UAV network | Yes | No | No |

[31] | Energy-efficient optimization scheme | Yes | No | No |

[11] | Secure resource allocation scheme in ATCNs | Yes | No | No |

[32] | Energy efficient resource allocation | Yes | No | No |

[33] | Stochastic geometry model of backhaul | No | Yes | No |

[34] | In-band integrated access and backhaul management | Yes | Yes | No |

[35] | Security management scheme for backhaul-aware C-V2X | No | Yes | No |

[36] | Model for wireless backhaul to UAVs | No | Yes | No |

[37] | UAV-assisted backhaul scheme for 5G mmWave Cellular Networks | No | Yes | No |

[39] | Optimized cellular UAV-to-X communications | Yes | No | Yes |

[40] | Resource allocation and trajectory design | Yes | No | Yes |

Parameter | Values | Description |
---|---|---|

N | 2 | Number of tiers |

M | 100–1000 | Number of devices/entities |

U | 10–50 | Number of UAVs |

${E}_{M}$ | 1.08× ${10}^{6}$ J | Maneuvering energy of UAVs [47] |

${E}_{N}$ | 0.32 J + 0.25 J | Link establishment |

${E}_{T}$ | 0.32 J | Transmission energy |

${E}_{S}$ | 1 J | Scheduling energy |

$\tau $ | 1000 s | Total operational time |

${L}_{T}$ | 20–40 ms | Transmission latency [48] |

${L}_{D}$ | 0.5 s | Decision latency |

${L}_{R}$ | 0.5 s | Replacement latency |

$\psi $ | 5–50% of U | Hazardous constant |

A | 2500 sq.m | Area under evaluation |

W | 100 MHz | Bandwidth |

${\mathcal{O}}_{R}$ | 200 Mbps | Offered rate |

$\mathit{SINR}$ | 100 | SINR |

${U}_{H}^{\left(C\right)}$ | 200 kbps–200 Mbps | per UAV handling capacity |

$\omega $ | 10 | Step interval |

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**MDPI and ACS Style**

Gupta, T.; Arena, F.; You, I.
Efficient Resource Allocation for Backhaul-Aware Unmanned Air Vehicles-to-Everything (U2X). *Sensors* **2020**, *20*, 2994.
https://doi.org/10.3390/s20102994

**AMA Style**

Gupta T, Arena F, You I.
Efficient Resource Allocation for Backhaul-Aware Unmanned Air Vehicles-to-Everything (U2X). *Sensors*. 2020; 20(10):2994.
https://doi.org/10.3390/s20102994

**Chicago/Turabian Style**

Gupta, Takshi, Fabio Arena, and Ilsun You.
2020. "Efficient Resource Allocation for Backhaul-Aware Unmanned Air Vehicles-to-Everything (U2X)" *Sensors* 20, no. 10: 2994.
https://doi.org/10.3390/s20102994