Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
Abstract
:1. Introduction
1.1. Prior Works
1.2. Motivation and Contributions
- (1)
- We present a UAV cluster system that leverages RIS mounted on UAVs to establish LoS channels between BS and UAVs, and enhance wireless connectivity in obstructed environments. Additionally, we propose designing the RIS coefficients based on the position information of UAVs to minimize the channel estimation overhead.
- (2)
- This study demonstrates that by designing RIS with appropriate coefficients, it is possible to estimate the interference between UAV clusters based on the positions of the UAVs. We introduce a UAV interference matrix to describe the interference relationship between UAV clusters. In the context of periodical scheduling, we derive the degree of interference between UAV clusters by analyzing the scheduling period and initial scheduling time slot of each UAV. Subsequently, we formulate the UAV scheduling problem as an integer optimization problem.
- (3)
- The time slot scheduling problem for UAVs is approached as a multi-stage decision problem, which can be equivalently modeled as an optimal path search problem through the establishment of a UAV scheduling graph. To tackle this issue, we introduce a novel local interference minimization (LIM) scheme that aims to minimize interference arising from newly scheduled UAVs. Additionally, we propose a rollout interference minimization (RIM) algorithm that builds upon the LIM scheme by utilizing its calculation results to determine optimal decisions for subsequent nodes.
- (4)
- The performance of the proposed LIM and RIM schemes is evaluated through simulations and compared to that of the basic sequential scheduling scheme. The results show that the proposed schemes effectively reduce the interference level between UAV clusters while satisfying the constraints of given scheduling periods and a limited number of RISs.
1.3. Organization
2. System Models and Problem Formulations
3. UAV Scheduling to Avoid Interference
3.1. UAV Scheduling Graph
3.2. Local Interference Minimization Scheme
Algorithm 1: Local interference minimization (LIM). |
3.3. LIM-Based Rollout Scheme
Algorithm 2: Rollout interference minimization (RIM). |
3.4. Complexity Analysis
4. Simulation Results and Discussion
5. Future Directions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Notations and Abbreviations
Notations | |
The UAV interference matrix between UAV cluster i and UAV cluster | |
The wireless channel between the BS and the RIS | |
The wireless channel between the RIS array and UAVs | |
The steering vector with directional angle | |
The received SIR of UAV interfered by beam associated with UAV | |
The cycle to schedule UAV j in UAV cluster i | |
L | The number of RIS arrays on the RIS-UAV |
M | The number of UAVs in a UAV cluster |
The system interference level | |
The initial time slot of UAV j in cluster i | |
A vector of length N that elements specified by indices equal 1 | |
All zeros vector of length N | |
⊗ | The Kronecker product |
vec{} | The vectorization operation |
The modulo operation | |
The ceiling function | |
The greatest common divisor of integer numbers a and b | |
The empty set | |
Abbreviations | |
UAV | Unmanned aerial vehicle |
RIS | Reconfigurable intelligent surface |
BS | Base station |
UIM | UAV interference matrix |
SIR | Signal-to-noise ratio |
LIM | Local interference minimization |
RIM | Rollout interference minimization |
References
- Zeng, Y.; Zhang, R.; Lim, T.J. Wireless communications with unmanned aerial vehicles: Opportunities and challenges. IEEE Commun. Mag. 2016, 54, 36–42. [Google Scholar] [CrossRef]
- Varsier, N.; Dufrène, L.-A.; Dumay, M.; Lampin, Q.; Schwoerer, J. A 5G New Radio for Balanced and Mixed IoT Use Cases: Challenges and Key Enablers in FR1 Band. IEEE Commun. Mag. 2021, 59, 82–87. [Google Scholar] [CrossRef]
- Zhang, S.; Pöhlmann, R.; Wiedemann, T.; Dammann, A.; Wymeersch, H.; Hoeher, P.A. Self-aware swarm navigation in autonomous exploration missions. Proc. IEEE 2020, 108, 1168–1195. [Google Scholar] [CrossRef]
- Zeng, Y.; Wu, Q.; Zhang, R. Accessing from the sky: A tutorial on UAV communications for 5G and beyond. Proc. IEEE 2019, 107, 2327–2375. [Google Scholar] [CrossRef]
- Chen, W.; Liu, J.; Guo, H.; Kato, N. Toward robust and intelligent drone swarm: Challenges and future directions. IEEE Netw. 2020, 34, 278–283. [Google Scholar] [CrossRef]
- Wu, Q.; Xu, J.; Zeng, Y.; Ng, D.W.K.; Al-Dhahir, N.; Schober, R.; Swindlehurst, A.L. A comprehensive overview on 5G-and-beyond networks with UAVs: From communications to sensing and intelligence. IEEE J. Sel. Areas Commun. 2021, 39, 2912–2945. [Google Scholar] [CrossRef]
- Zhang, S.; Zhang, R. Radio map-based 3D path planning for cellular-connected UAV. IEEE Trans. Wirel. Commun. 2020, 20, 1975–1989. [Google Scholar] [CrossRef]
- Lyu, J.; Zhang, R. Network-connected UAV: 3-D system modeling and coverage performance analysis. IEEE Internet Things J. 2019, 6, 7048–7060. [Google Scholar] [CrossRef]
- Zhan, C.; Zeng, Y. Energy-efficient data uploading for cellular-connected UAV systems. IEEE Trans. Wirel. Commun. 2020, 19, 7279–7292. [Google Scholar] [CrossRef]
- Chandhar, P.; Danev, D.; Larsson, E.G. Massive MIMO for communications with drone swarms. IEEE Trans. Wirel. Commun. 2017, 17, 1604–1629. [Google Scholar] [CrossRef]
- Ouamri, M.A.; Singh, D.; Muthanna, M.A.; Bounceur, A.; Li, X. Performance analysis of UAV multiple antenna-assisted small cell network with clustered users. Wirel. Netw. 2023, 29, 1859–1872. [Google Scholar] [CrossRef]
- Ouamri, M.A.; Alkanhel, R.; Gueguen, C.; Alohali, M.A.; Ghoneim, S.S. Modeling and analysis of uav-assisted mobile network with imperfect beam alignment. CMC-Comput. Mater. Contin. 2023, 74, 453–467. [Google Scholar] [CrossRef]
- Mohamed, Z.; Aïssa, S. Leveraging UAVs with Intelligent Reflecting Surfaces for Energy-Efficient Communications with Cell-Edge Users. In Proceedings of the 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 7–11 June 2020; pp. 1–6. [Google Scholar]
- Pang, X.; Sheng, M.; Zhao, N.; Tang, J.; Niyato, D.; Wong, K.K. When UAV Meets IRS: Expanding Air-Ground Networks via Passive Reflection. IEEE Wirel. Commun. 2021, 28, 164–170. [Google Scholar] [CrossRef]
- Li, Y.; Zhang, H.; Long, K.; Nallanathan, A. Exploring Sum Rate Maximization in UAV-Based Multi-IRS Networks: IRS Association, UAV Altitude, and Phase Shift Design. IEEE Trans. Commun. 2022, 70, 7764–7774. [Google Scholar] [CrossRef]
- Li, K.; Zhao, K.; Khan, M.F.; Ho, P.H.; Peng, L. UAV-mounted Intelligent Reflecting Surface (IRS) MISO Communications. In Proceedings of the 2022 International Conference on Networking and Network Applications (NaNA), Urumqi, China, 3–5 December 2022; pp. 62–66. [Google Scholar]
- Shafique, T.; Tabassum, H.; Hossain, E. Optimization of Wireless Relaying with Flexible UAV-Borne Reflecting Surfaces. IEEE Trans. Commun. 2021, 69, 309–325. [Google Scholar] [CrossRef]
- Mu, X.; Liu, Y.; Guo, L.; Lin, J.; Schober, R. Joint deployment and multiple access design for intelligent reflecting surface assisted networks. IEEE Trans. Wirel. Commun. 2021, 20, 6648–6664. [Google Scholar] [CrossRef]
- Zhou, L.; Chen, X.; Hong, M.; Jin, S.; Shi, Q. Efficient resource allocation for multi-UAV communication against adjacent and co-channel interference. IEEE Trans. Veh. Technol. 2021, 70, 10222–10235. [Google Scholar] [CrossRef]
- Mei, W.; Zhang, R. Cooperative downlink interference transmission and cancellation for cellular-connected UAV: A divide-and-conquer approach. IEEE Trans. Commun. 2019, 68, 1297–1311. [Google Scholar] [CrossRef]
- Liu, L.; Zhang, S.; Zhang, R. Multi-beam UAV communication in cellular uplink: Cooperative interference cancellation and sum-rate maximization. IEEE Trans. Wirel. Commun. 2019, 18, 4679–4691. [Google Scholar] [CrossRef]
- Li, X.; Liu, Z.; Qin, N.; Jin, S. FFR based joint 3D beamforming interference coordination for multi-cell FD-MIMO downlink transmission systems. IEEE Trans. Veh. Technol. 2020, 69, 3105–3118. [Google Scholar] [CrossRef]
- Sha, Z.; Wang, Z. Least pair-wise collision beam schedule for mmWave inter-cell interference suppression. IEEE Trans. Wirel. Commun. 2019, 18, 4436–4449. [Google Scholar] [CrossRef]
- Sha, Z.; Wang, Z.; Chen, S.; Hanzo, L. Graph theory based beam scheduling for inter-cell interference avoidance in mmWave cellular networks. IEEE Trans. Veh. Technol. 2020, 69, 3929–3942. [Google Scholar] [CrossRef]
- Mishra, D.; Vegni, A.M.; Loscrí, V.; Natalizio, E. Drone networking in the 6 g era: A technology overview. IEEE Commun. Stand. Mag. 2021, 5, 88–95. [Google Scholar] [CrossRef]
- Gu, B.; Chen, W.; Alazab, M.; Tan, X.; Guizani, M. Multiagent Reinforcement Learning-Based Semi-Persistent Scheduling Scheme in C-V2X Mode 4. IEEE Trans. Veh. Technol. 2022, 71, 12044–12056. [Google Scholar] [CrossRef]
- Jiang, R.; Fei, Z.; Huang, S.; Wang, X.; Wu, Q.; Ren, S. Bivariate Pilot Optimization for Compressed Channel Estimation in RIS-Assisted Multiuser MISO-OFDM Systems. IEEE Trans. Veh. Technol. 2023, 72, 9115–9130. [Google Scholar] [CrossRef]
- Chen, J.; Liang, Y.C.; Cheng, H.V.; Yu, W. Channel estimation for reconfigurable intelligent surface aided multi-user mmWave MIMO systems. IEEE Trans. Wirel. Commun. 2023, 22, 6853–6869. [Google Scholar] [CrossRef]
- binti Burhanuddin, L.A.; Liu, X.; Deng, Y.; Challita, U.; Zahemszky, A. QoE optimization for live video streaming in UAV-to-UAV communications via deep reinforcement learning. IEEE Trans. Veh. Technol. 2022, 71, 5358–5370. [Google Scholar] [CrossRef]
- Bhandari, S.; Wang, X.; Lee, R. Mobility and Location-Aware Stable Clustering Scheme for UAV Networks. IEEE Access 2020, 8, 106364–106372. [Google Scholar] [CrossRef]
- Liu, Z.; Zhou, E.; Cui, J.; Dong, Z.; Fan, P. A Double-Beam Soft Handover Scheme and Its Performance Analysis for Mmwave UAV Communications in Windy Scenarios. IEEE Trans. Veh. Technol. 2022, 72, 893–906. [Google Scholar] [CrossRef]
- Rosen, K.H. Elementary Number Theory; Pearson Education: London, UK, 2011. [Google Scholar]
- Bertsekas, D.P. Rollout algorithms for discrete optimization: A survey. In Handbook of Combinatorial Optimization; Springer: New York, NY, USA, 2013; Volume 5, pp. 2989–3013. [Google Scholar]
Existing Works | Cellular-Connected UAV | UAV-Mounted RIS | Interference Avoidance | Scheduling Optimization | Periodic Scheduling |
---|---|---|---|---|---|
[9] | ✓ | ✓ | |||
[13,14,15] | ✓ | ||||
[16,17,18] | ✓ | ✓ | |||
[19] | ✓ | ✓ | ✓ | ||
[20,21] | ✓ | ✓ | |||
[22,23,24] | ✓ | ✓ | |||
This Paper | ✓ | ✓ | ✓ | ✓ | ✓ |
(a) , , Schedule Periods , . | |||||
---|---|---|---|---|---|
interference probability | 0.2 | 0.4 | 0.6 | 0.8 | 1 |
sequential schedule | 0.132 | 0.237 | 0.351 | 0.491 | 0.604 |
LIM | 0.022 | 0.047 | 0.075 | 0.149 | 0.225 |
RIM | 0 | 0 | 0 | 0 | 0 |
(b) , , Schedule Periods , . | |||||
interference probability | 0.2 | 0.4 | 0.6 | 0.8 | 1 |
sequential schedule | 0.276 | 0.458 | 0.754 | 0.881 | 1.200 |
LIM | 0.033 | 0.089 | 0.126 | 0.205 | 0.298 |
RIM | 0 | 0.001 | 0.003 | 0.009 | 0.016 |
(c) , , Schedule Periods , . | |||||
interference probability | 0.2 | 0.4 | 0.6 | 0.8 | 1 |
sequential schedule | 0.399 | 0.767 | 1.312 | 1.766 | 2.145 |
LIM | 0.056 | 0.162 | 0.262 | 0.405 | 0.548 |
RIM | 0.006 | 0.027 | 0.035 | 0.064 | 0.095 |
(a) , , Schedule Periods , , . | |||||
---|---|---|---|---|---|
interference probability | 0.2 | 0.4 | 0.6 | 0.8 | 1 |
sequential schedule | 0.424 | 0.867 | 1.274 | 1.736 | 2.135 |
LIM | 0.089 | 0.216 | 0.353 | 0.496 | 0.595 |
RIM | 0.004 | 0.013 | 0.026 | 0.037 | 0.047 |
(b) , , Schedule Periods , , . | |||||
interference probability | 0.2 | 0.4 | 0.6 | 0.8 | 1 |
sequential schedule | 0.639 | 1.240 | 1.756 | 2.534 | 3.121 |
LIM | 0.122 | 0.223 | 0.456 | 0.821 | 1.343 |
RIM | 0.007 | 0.029 | 0.063 | 0.106 | 0.129 |
(c) , , Schedule Periods , , . | |||||
interference probability | 0.2 | 0.4 | 0.6 | 0.8 | 1 |
sequential schedule | 1.034 | 2.227 | 3.405 | 4.783 | 6.114 |
LIM | 0.208 | 0.626 | 1.212 | 0.405 | 0.548 |
RIM | 0.065 | 0.191 | 0.442 | 0.665 | 0.881 |
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Share and Cite
Zhou, E.; Liu, Z.; Lan, P.; Xiao, W.; Yang, W.; Niu, X. Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications. Electronics 2023, 12, 4539. https://doi.org/10.3390/electronics12214539
Zhou E, Liu Z, Lan P, Xiao W, Yang W, Niu X. Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications. Electronics. 2023; 12(21):4539. https://doi.org/10.3390/electronics12214539
Chicago/Turabian StyleZhou, Enzhi, Ziyue Liu, Ping Lan, Wei Xiao, Wei Yang, and Xianhua Niu. 2023. "Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications" Electronics 12, no. 21: 4539. https://doi.org/10.3390/electronics12214539
APA StyleZhou, E., Liu, Z., Lan, P., Xiao, W., Yang, W., & Niu, X. (2023). Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications. Electronics, 12(21), 4539. https://doi.org/10.3390/electronics12214539