Energy Efficient Transmission Design for NOMA Backscatter-Aided UAV Networks with Imperfect CSI
Abstract
:1. Introduction
1.1. Recent Works on Backscatter Communications
1.2. Motivation and Contributions
- A NOMA ABC-aided UAV network is considered in which multiple UAVs transmit superimposed signals to the IoT in their coverage areas using a NOMA protocol. Meanwhile, BSTs also receive UAV signals, add valuable data, and reflect it towards the IoT. On the receiver side, the IoT with strong channel conditions applies SIC to decode the signal received from the UAV and the BST by subtracting the information of other IoT with poor channel conditions. However, the last IoT cannot apply SIC and decode the received signal from the UAV and the BST directly by treating the signal of the first IoT as noise. This work aims to minimize the power consumption of the NOMA ABC-aided UAV network by efficiently allocating the available system resources under the assumption of imperfect channel information.
- The formulated power minimization problem is not convex due to inter-UAV interference among UAVs, NOMA interference, and interference of imperfect CSI. Thus, solving the problem directly and obtaining the optimal solution is challenging and complex. Therefore, we transform it and then adopt a sub-gradient approach for an efficient solution. According to this method, all the optimization variables and multipliers are updated in each iteration until convergence. For comparison, we proposed an optimization framework for the traditional NOMA UAV network without involving ABC.
- To validate our proposed framework, we obtain numerical results using Monte Carlo simulations for the NOMA ABC-aided UAV network and the benchmark NOMA UAV network without ABC. Results demonstrate the benefits of the ABC-aided NOMA UAV network compared to the benchmark network. Moreover, results also show the effect of imperfect channel information on the overall system’s achievable energy efficiency. Furthermore, the impact of other optimization parameters, such as minimum quality of services of individual IoT, transmit power of each UAV, and the number of UAVs, is also depicted. The results reveal that using ABC in NOMA UAV networks can significantly improve the total achievable energy efficiency of the system.
1.3. Paper Organization
2. System Model and Problem Formulation
3. Proposed Optimization Solution
4. Numerical Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ref. | Scenario | Objective | UAV/Backscatter | Technique | CSI |
---|---|---|---|---|---|
[14] | Single cell | Energy efficiency | Single/single | Dinkelbach and dual theory | Perfect |
[15] | Single cell | Efficient capacity | None/multiple | Reinforcement learning | Perfect |
[16] | Single cell | Spectral efficiency | None/single | Dual theory and KKT conditions | Perfect |
[17] | Single cell | Throughput | None/multiple | Block coordinated descent and successive convex optimization | Perfect |
[18] | Single cell | Max-min throughput | None/multiple | Block coordinated descent and successive convex optimization | Perfect |
[19] | Single cell | Secrecy rate | None/single | Dual theory | Perfect |
[20] | Single | Throughput | None/multiple | Lagrangian method | Perfect |
[21] | Single cell | Energy efficiency | None/multiple | Alternating optimization and Dual theory | Perfect |
[22] | Single cell | Spectral efficiency | None/single | Dual theory and KKT conditions | Perfect |
[23] | Single cell | Outage probability and intercept probability | None/single | Asymptotic expressions and approximate expressions | Perfect |
[24] | Multi cell | Energy efficiency | None/multiple | Dinkelbach and dual theory | Perfect |
[25] | Multi cell | Utility | None/multiple | Reinforcement learning and supervised deep learning | Perfect |
[26] | Multi cell | Interference management | None/multiple | Reinforcement learning | Perfect |
[27] | Single cell | Throughput | Single/single | Block coordinated descent and successive convex optimization | Perfect |
[28] | Single cell | Throughput | Single/multiple | Exhaustive search | Perfect |
[29] | Single cell | Throughput | Single/single | Block coordinated descent and successive convex optimization | Perfect |
[30] | Single cell | BER and Energy efficiency | multiple/multiple | Closed-form solution for BER | Perfect |
[31] | Single cell | Secrecy rate | Single/multiple | Block coordinate descent | Perfect |
[32] | Single cell | Energy efficiency | Single/multiple | Golden section | Perfect |
[33] | Single cell | Energy efficiency | Single/multiple | Block coordinated decent | Perfect |
[Our] | Multi cell | Energy efficiency | Multiple/multiple | Sub-gradient and alternating optimization | Imperfect |
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AlJubayrin, S.; Al-Wesabi, F.N.; Alsolai, H.; Duhayyim, M.A.; Nour, M.K.; Khan, W.U.; Mahmood, A.; Rabie, K.; Shongwe, T. Energy Efficient Transmission Design for NOMA Backscatter-Aided UAV Networks with Imperfect CSI. Drones 2022, 6, 190. https://doi.org/10.3390/drones6080190
AlJubayrin S, Al-Wesabi FN, Alsolai H, Duhayyim MA, Nour MK, Khan WU, Mahmood A, Rabie K, Shongwe T. Energy Efficient Transmission Design for NOMA Backscatter-Aided UAV Networks with Imperfect CSI. Drones. 2022; 6(8):190. https://doi.org/10.3390/drones6080190
Chicago/Turabian StyleAlJubayrin, Saad, Fahd N. Al-Wesabi, Hadeel Alsolai, Mesfer Al Duhayyim, Mohamed K. Nour, Wali Ullah Khan, Asad Mahmood, Khaled Rabie, and Thokozani Shongwe. 2022. "Energy Efficient Transmission Design for NOMA Backscatter-Aided UAV Networks with Imperfect CSI" Drones 6, no. 8: 190. https://doi.org/10.3390/drones6080190