# Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications

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## Abstract

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## 1. Introduction

- The uplink NOMA-based transmission system utilizing the UAV as a base station is modeled. Then, the resource allocation problem is formulated with the aim of maximizing the sum rate, taking into consideration the different delay requirements to serve URLLC applications.
- The proposed scheduler allocates resources jointly in both time domain and frequency domain based on the IoT devices parameters. Delay limits and priority are used by time domain; then the buffer status report (BSR) and channel quality indicator (CQI) control the frequency domain scheduler decision to allocate resources. In addition, a power allocation scheme is proposed to achieve fairness between the users allocated the same RB regardless of the different channel conditions.
- Unlike the previous works, the novelty of the presented algorithm lies in its ability to consider both the strict delay requirements of IoT devices and the system throughput while ensuring high reliability and fairness, where simulations are performed to evaluate the proposed scheduling algorithm performance. The results demonstrate the effectiveness of the proposed algorithm to serve URLLC traffic with restricted delay limits, due to the significant enhancement in delay, reliability and fairness, in addition to maximizing the sum data rate and spectral efficiency while achieving the same system complexity when compared to the maximum channel quality indicator (max CQI) algorithm.

## 2. Related Works

## 3. System Model

_{UAV}, y

_{UAV}, h

_{UAV}); assume that (${x}_{UAV}=0$, ${y}_{UAV}=0$, ${h}_{UAV}$); i.e., the UAV is at the center of the cell with altitude h

_{UAV}; such that (h

_{min}≤ h

_{UAV}≤ h

_{max}) where h

_{min}and h

_{max}are the minimum and maximum allowable heights of the UAV. Assume that there are N ground IoT devices; each device is equipped with a single antenna. These devices are distributed randomly in the cell covered by the UAV; each device is located at (x

_{i}, y

_{i}) where i = {1, 2, …, N}. Each IoT device has a packet arrival rate ${\lambda}_{i}$ and transmits minimum rate R

_{i}with delay limit D

_{i}. Each IoT device has a transmitted power P

_{i}(P

_{min,i}≤ P

_{i}≤ P

_{max,i}) where P

_{min,i}and P

_{max,i}are the minimum and maximum transmitted power of IoT device i respectively. Path loss between IoT device i to the UAV expresses the large-scale fading component which is given by

_{i}, y

_{i}) and the UAV located at (${x}_{UAV}$, ${y}_{UAV}$, ${h}_{UAV}$) is

_{k,i}(t) is the subcarriers assignment index, where b

_{k,i}(t) = 1 means that subcarrier k is allocated to device i, otherwise b

_{k,i}(t) = 0. There are maximum L IoT devices allowed to be scheduled over a single subcarrier at the same time and each IoT device gets exactly one subcarrier for simplicity. IoT devices can give preferences for the resource blocks, and their preferences are considered based on their channel quality indicator (CQI) [22] which depends on the channel quality between the UAV and IoT devices. The signal to noise plus interference ratio (SNIR) of IoT device i on subcarrier k is modeled as

_{i}packets per second. Both ${\lambda}_{i}$ and μ

_{i}are statistically identical and independent distributed. The queuing system model of the IoT device is shown in Figure 2. Then, the average delay ${d}_{av,i}$, which is based on the M/M/1 queuing model [25], can be given by the following formula:

_{i}and ${R}_{av,i}$ are the packet size and the average rate of IoT device i respectively.

## 4. Problem Formulation

_{max}is the maximum available transmission power of the IoT device. C1 is the minimum required data rate for all users to ensure QoS. C2 means that any IoT device transmit power cannot exceed P

_{max}. C3 and C4 are the constraints of the assignment matrix b

_{k,i}and power p

_{k,i}. C5 mentions that one subcarrier cannot be allocated to more than L users at the same time. C6 is the delay constraint, states that the average delay of the device i (${d}_{av,i}$) should not exceed the delay limit requirement D

_{i}. C7 is the UAV height constraint between the minimum and maximum allowable altitudes.

## 5. Proposed Algorithm

#### 5.1. Time Domain Packet Scheduling (TDPS)

_{max}= KL devices to be scheduled in the next TTI, since there are L devices can be scheduled over the same resource block according to constraint C5 (22). Consider N_sel to be the number of chosen IoT devices to be scheduled. The rejected users send scheduling requests in the next TTI.

#### 5.2. Frequency Domain Packet Scheduling (FDPS)

Algorithm 1. Joint Delay-Rate Optimization Scheduler |

Input: T, K, N, $\lambda $, ${P}_{min}{P}_{max}$, j, L |

Output: $\left\{s\right\}{b}_{k,i}$ |

1: Initialize: N_{max} = L * K |

2: Step 1: chooses the N_{max} devices with the highest metrics in TDPS. |

3: Arrange IoT devices in j groups according to the application type. |

4: For T_{s} = 1 to T_{s} = T do |

5: R = IoT devices send scheduling requests |

6: If R =< N_{max} go to Label |

7: else, do |

8: For i = 1 to i = R do |

9: Compute the weight of device i (27): |

${M}_{i}={\alpha}_{i}{w}_{i,j}{d}_{av,i}$ |

10: End for |

11: Arrange IoT devices of each group in descending order of the weight (${M}_{i}$) |

12: Choose the N_max nodes which have the maximum weight to be scheduled in this TTI. |

13: Label: |

Choose the R nodes to be scheduled in this TTI. |

14: End If |

15: N_sel = the selected nodes to be scheduled |

16: Step 2: assign each IoT device a resource block |

17: For i = 1 to i = N_sel do |

18: Form the preference matrix for each device i to all available RBs based on the CQI value. |

19: Form the objective function which is the weighted preference matrix ${f}_{obj}$. |

20: Initialize $\widehat{k}$ = 1, correspondingly set ${f}_{max}={f}_{obj}$. |

21: if a solution $\stackrel{`}{k}\u03f5\left\{k\u03f5K\left|{f}_{obj}\right.\u232a{f}_{max}\right\}$ can be found: update $\widehat{k}$ = $\stackrel{`}{k}$, and ${f}_{max}={f}_{obj}$. |

22: End for |

23: Delay of the scheduled nodes is cleared, but that of waiting nodes is incremented. |

24: Rejected nodes send scheduling requests in the next TTI. |

25: End For |

#### 5.3. Power Allocation Algorithm

## 6. Simulation Results

_{max}in every TTI achieving almost the same sum rate. It can be noticed that the sum rate in case of 25 RBs outperforms that in case of 6 RBs and 10 RBs, while it is the least in the case of 6 RBs. It is observed that the saturation point is shifted to the right as the number of RBs increases, meaning that as the number of RBs increases, the sum rate saturates at larger number of users.

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Illustration of the elevation angle in case of static UAV [21].

**Figure 2.**Queuing system model of the IoT device [26].

Environment | ${\mathit{\theta}}_{\mathit{o}\mathit{p}\mathit{t}\mathit{i}\mathit{m}\mathit{u}\mathit{m}}$ |
---|---|

Suburban | 20.34° |

Urban | 42.44° |

Dense urban | 54.62° |

High-rise urban | 75.52° |

Symbol | Description | Value |
---|---|---|

a | Environment Constant | 4.88 |

b | Environment Constant | 0.43 |

${\eta}_{LOS}$ | Line of sight Environment Constant | 0.1 |

${\eta}_{NLOS}$ | Non-Line of sight Environment Constant | 21 |

${\theta}_{optimum}$ | Optimal elevation angle | $20.34\xb0$ |

${\sigma}^{2}$ | Noise power density | −174 |

U | Number of UAVs | 1 |

${P}_{min}$ | IoT device minimum power | 100 mW–20 dBm |

${P}_{max}$ | IoT device maximum power | 500 mW–27 dBm |

${R}_{c}$ | Radius of the cell | 1 km |

TTI | Time slot | 1 ms |

Simulation time | 1 s | |

${D}_{limit}$ | Maximum delay limit | {10,20,30,40} ms |

$\lambda $ | Arrival rate per group | {100,250,600,400} (packets/s) |

N | Total number of devices | 300 |

D | Packet size | 100 bits |

m | Channel blocklength | 100 symbols |

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**MDPI and ACS Style**

Karem, R.; Ahmed, M.; Newagy, F.
Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications. *Sensors* **2022**, *22*, 1566.
https://doi.org/10.3390/s22041566

**AMA Style**

Karem R, Ahmed M, Newagy F.
Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications. *Sensors*. 2022; 22(4):1566.
https://doi.org/10.3390/s22041566

**Chicago/Turabian Style**

Karem, Rana, Mehaseb Ahmed, and Fatma Newagy.
2022. "Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications" *Sensors* 22, no. 4: 1566.
https://doi.org/10.3390/s22041566