Deep Learning for Channel Tracking in IRS-Assisted UAV Communication Systems

Yu, Jiadong; Liu, Xiaolan; Gao, Yue; Zhang, Chiya; Zhang, Wei

doi:10.1109/twc.2022.3160517

Cited by 33 publications

(15 citation statements)

References 51 publications

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“…Additionally, the delay in both centralized learning and processing time to optimize algorithm is also longer for practical applications. In [ 7 ], researchers introduced proposed a deep deterministic policy gradient (DDPG) algorithm to investigate DRL applications to IRS-UAV NOMA downlink system. The proposed technique is utilized to optimize the IRS phase shift, horizontal position of UAV and power allocation of BS.…”

Section: Emerging Technologiesmentioning

confidence: 99%

“…The most crucial concerns are stringent size, weight, and power (SWAP) constraints. Additionally, LoS link blockage due to high rise buildings [ 7 ] and high power consumption to ensure airborne condition and high mobility also pose critical limits. Similarly, security and privacy issues of legitimate network entities can suffer from potential eavesdroppers.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Reflecting Surfaces Assisted UAV Communications for Massive Networks: Current Trends, Challenges, and Research Directions

Mohsan

Khan

Alsharif

et al. 2022

Sensors

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A intelligent reflecting surface (IRS) can intelligently configure wavefronts such as amplitude, frequency, phase, and even polarization through passive reflections and without requiring any radio frequency (RF) chains. It is predicted to be a revolutionizing technology with the capability to alter wireless communication to enhance both spectrum and energy efficiencies with low expenditure and low energy consumption. Similarly, unmanned aerial vehicle (UAV) communication has attained a significant interest by research fraternity due to high mobility, flexible deployment, and easy integration with other technologies. However, UAV communication can face obstructions and eavesdropping in real-time scenarios. Recently, it is envisaged that IRS and UAV can combine together to achieve unparalleled opportunities in difficult environments. Both technologies can achieve enhanced performance by proactively altering the wireless propagation through maneuver control and smart signal reflections in three-dimensional space. This study briefly discusses IRS-assisted UAV communications. We survey the existing literature on this emerging research topic for both ground and airborne scenarios. We highlight several emerging technologies and application scenarios for future wireless networks. This study goes one step further to elaborate research opportunities to design and optimize wireless systems with low energy footprint and at low cost. Finally, we shed some light on open challenges and future research directions for IRS-assisted UAV communication.

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Section: Emerging Technologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Reflecting Surfaces Assisted UAV Communications for Massive Networks: Current Trends, Challenges, and Research Directions

Mohsan

Khan

Alsharif

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The authors developed a DQN-based distributed algorithm to optimize the trajectory and speed of UAVs, phase shift of IRS, subcarrier allocation, and active beamforming at BSs. The authors of [99] proposed a DL-based channel-tracking mechanism to track the time-varying channel. The proposed algorithm consists of two modules: channel pre-estimation and channel tracking.…”

Section: Algorithmsmentioning

confidence: 99%

A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications

2022

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Both the unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) are attracting growing attention as enabling technologies for future wireless networks. In particular, IRS-assisted UAV communication, which incorporates IRSs into UAV communications, is emerging to overcome the limitations and problems of UAV communications and improve the system performance. This article aims to provide a comprehensive survey on IRS-assisted UAV communications. We first present six representative scenarios that integrate IRSs and UAVs according to the installation point of IRSs and the role of UAVs. Then, we introduce and discuss the technical features of the state-of-the-art relevant works on IRS-assisted UAV communications systems from the perspective of the main performance criteria, i.e., spectral efficiency, energy efficiency, security, etc. We also introduce machine learning algorithms adopted in the previous works. Finally, we highlight technical issues and research challenges that need to be addressed to realize IRS-assisted UAV communications systems.

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“…Accurate channel estimation is critical in highly mobile IRS-assisted non-terrestrial communication [97], [99].…”

Section: Sacmentioning

confidence: 99%

IRS-Empowered 6G Networks: Deployment Strategies, Performance Optimization, and Future Research Directions

et al. 2022

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The performance of the envisioned 6G network is fundamentally constrained by the uncontrollable and random wireless communication channel. Intelligent reflecting surfaces (IRSs) have emerged as one of the potential solutions to overcome this challenge by smartly controlling the incident signal to enhance the energy efficiency and spectrum efficiency of the 6G network. In addition, the future 6G network will incorporate several enabling technologies, including artificial intelligence and machine learning (AI/ML), integrated terrestrial and non-terrestrial (TNT) networks, multi-access edge computing (MEC), non-orthogonal multiple access (NOMA), and terahertz/millimeter wave (THz/mmWave) communication techniques. Therefore, this paper provides a contemporary and comprehensive overview of the envisioned IRS-empowered 6G networks from the perspective of its architecture, deployment strategy, integration of IRS technology with other 6G-enabling technologies, and physical layer security (PLS). Finally, we highlight design challenges and future research directions aimed at improving 6G network performance.INDEX TERMS Intelligent reflecting surfaces, Terahertz communication, Terrestrial and non-terrestrial (TNT) networks, Unmanned Aerial Vehicles, Reinforcement learning, Ultra-reliable and Low-latency communications.

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Deep Learning for Channel Tracking in IRS-Assisted UAV Communication Systems

Cited by 33 publications

References 51 publications

Intelligent Reflecting Surfaces Assisted UAV Communications for Massive Networks: Current Trends, Challenges, and Research Directions

Intelligent Reflecting Surfaces Assisted UAV Communications for Massive Networks: Current Trends, Challenges, and Research Directions

A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications

IRS-Empowered 6G Networks: Deployment Strategies, Performance Optimization, and Future Research Directions

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