Channel Estimation in C-V2X using Deep Learning

Sattiraju, Raja; Weinand, Andreas; Schotten, Hans D.

doi:10.48550/arxiv.2003.02617

“…[211][212][213] optimized the channel estimation using DL where offline training is implemented for reducing the training time and handling huge data. However, efficiency fall may happen as the considered channels are not fully similar to the actual channels [214]. Hence, the way of efficient channel approximation in extremely dynamic networks requires more investigation.…”

Section: ML For New Physical Layermentioning

confidence: 99%

Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges

Das¹,

Benkhelifa

²

,

Sun

³

et al. 2023

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“…[207][208][209] optimized the channel estimation using DL where offline training is implemented for reducing the training time and handling huge data. However, efficiency fall may happen as the considered channels are not fully similar to the actual channels [210]. Hence, the way of efficient channel approximation in extremely dynamic networks requires more investigation.…”

Section: ML For New Physical Layermentioning

confidence: 99%

Comprehensive Review on ML-based RIS-enhanced IoT Systems: Basics, Research Progress and Future Challenges

Das¹,

Benkhelifa²,

Sun³

et al. 2023

Preprint

1

0

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Sixth generation (6G) internet of things (IoT) networks will modernize the applications and satisfy user demands through implementing smart and automated systems. Intelligence-based infrastructure, also called reconfigurable intelligent surfaces (RISs), have been introduced as a potential technology striving to improve system performance in terms of data rate, latency, reliability, availability, and connectivity. A huge amount of cost-effective passive components are included in RISs to interact with the impinging electromagnetic waves in a smart way. However, there are still some challenges in RIS system, such as finding the optimal configurations for a large number of RIS components. In this paper, we first provide a complete outline of the advancement of RISs along with machine learning (ML) algorithms and overview the working regulations as well as spectrum allocation in intelligent IoT systems. Also, we discuss the integration of different ML techniques in the context of RIS, including deep reinforcement learning (DRL), federated learning (FL), and FL-deep deterministic policy gradient (FL-DDPG) techniques which are utilized to design the radio propagation atmosphere without using pilot signals or channel state information (CSI). Additionally, in dynamic intelligent IoT networks, the application of existing integrated ML solutions to technical issues like user movement and random variations of wireless channels are surveyed. Finally, we present the main challenges and future directions in integrating RISs and other prominent methods to be applied in upcoming IoT networks. <br>

show abstract

Comprehensive Review on ML-based RIS-enhanced IoT Systems: Basics, Research Progress and Future Challenges

Das¹,

Benkhelifa²,

Sun³

et al. 2022

Preprint

View full text Add to dashboard Cite

Sixth generation (6G) internet of things (IoT) networks will modernize the applications and satisfy user demands through implementing smart and automated systems. Intelligence-based infrastructure, also called reconfigurable intelligent surfaces (RISs), have been introduced as a potential technology striving to improve system performance in terms of data rate, latency, reliability, availability, and connectivity. A huge amount of cost-effective passive components are included in RISs to interact with the impinging electromagnetic waves in a smart way. However, there are still some challenges in RIS system, such as finding the optimal configurations for a large number of RIS components. In this paper, we first provide a complete outline of the advancement of RISs along with machine learning (ML) algorithms and overview the working regulations as well as spectrum allocation in intelligent IoT systems. Also, we discuss the integration of different ML techniques in the context of RIS, including deep reinforcement learning (DRL), federated learning (FL), and FL-deep deterministic policy gradient (FL-DDPG) techniques which are utilized to design the radio propagation atmosphere without using pilot signals or channel state information (CSI). Additionally, in dynamic intelligent IoT networks, the application of existing integrated ML solutions to technical issues like user movement and random variations of wireless channels are surveyed. Finally, we present the main challenges and future directions in integrating RISs and other prominent methods to be applied in upcoming IoT networks. <br>

show abstract

Channel Estimation in C-V2X using Deep Learning

Cited by 4 publications

References 11 publications

Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges

Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges

Comprehensive Review on ML-based RIS-enhanced IoT Systems: Basics, Research Progress and Future Challenges

Comprehensive Review on ML-based RIS-enhanced IoT Systems: Basics, Research Progress and Future Challenges

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