Performance analysis of cache-aided UAV relaying networks

Abstract In this work, we analyze the effect of relaying network where unmanned aerial vehicles (UAVs) act as cache-aided relay nodes. Unmanned aerial vehicle (UAV) has high mobility, which can be flexibly scheduled when unable to communicate so that it can act as a wireless relay to assist communication. It can provide a more flexible deployment mode in different application scenarios to improve the performance of communication. What is more, how to transform the dynamic characteristics of UAVs into channel characteristics is also a challenge. To be more specific, UAVs act as decoded-and-forward (DF) relays to assist the wireless communication between the source and destination, and the speed and flying height remains unchanged. The UAVs are equipped with a wireless cache, which can provide reliable services and expand the channel capacity to reduce the outage probability of the network. Experimental and simulation results verify the effect of UAVs with cache on the outage performance in relaying networks.

[1]  Fumiyuki Adachi,et al.  Multi-Task Learning for Generalized Automatic Modulation Classification Under Non-Gaussian Noise With Varying SNR Conditions , 2021, IEEE Transactions on Wireless Communications.

[2]  George K. Karagiannidis,et al.  Distributed Machine Learning for Multiuser Mobile Edge Computing Systems , 2022, IEEE Journal of Selected Topics in Signal Processing.

[3]  Fusheng Zhu,et al.  An adaptive deep learning-based UAV receiver design for coded MIMO with correlated noise , 2021, Phys. Commun..

[4]  George K. Karagiannidis,et al.  Secure Mobile Edge Computing Networks in the Presence of Multiple Eavesdroppers , 2022, IEEE Transactions on Communications.

[5]  Arumugam Nallanathan,et al.  Dilated Convolution Based CSI Feedback Compression for Massive MIMO Systems , 2021, IEEE Transactions on Vehicular Technology.

[6]  Fasheng Zhou,et al.  Intelligent secure mobile edge computing for beyond 5G wireless networks , 2021, Phys. Commun..

[7]  Zhiguo Ding,et al.  I/Q Imbalance Aware Nonlinear Wireless-Powered Relaying of B5G Networks: Security and Reliability Analysis , 2020, IEEE Transactions on Network Science and Engineering.

[8]  Lisheng Fan,et al.  Efficient and flexible management for industrial Internet of Things: A federated learning approach , 2021, Comput. Networks.

[9]  Fumiyuki Adachi,et al.  CV-3DCNN: Complex-Valued Deep Learning for CSI Prediction in FDD Massive MIMO Systems , 2021, IEEE Wireless Communications Letters.

[10]  Mingquan Lu,et al.  Optimal Two-Way TOA Localization and Synchronization for Moving User Devices With Clock Drift , 2020, IEEE Transactions on Vehicular Technology.

[11]  Yi Qian,et al.  Energy-efficient design for mmWave-enabled NOMA-UAV networks , 2021, Science China Information Sciences.

[12]  Bin Xia,et al.  Analysis on Cache-Enabled Wireless Heterogeneous Networks , 2015, IEEE Transactions on Wireless Communications.

[13]  Shahid Mumtaz,et al.  Secrecy Performance Analysis of UAV Assisted Relay Transmission for Cognitive Network With Energy Harvesting , 2020, IEEE Transactions on Vehicular Technology.

[14]  Caijun Zhong,et al.  Statistical CSI based design for intelligent reflecting surface assisted MISO systems , 2020, Science China Information Sciences.

[15]  Jie Yang,et al.  Flight Delay Prediction Based on Aviation Big Data and Machine Learning , 2020, IEEE Transactions on Vehicular Technology.

[16]  George K. Karagiannidis,et al.  Secure Cache-Aided Multi-Relay Networks in the Presence of Multiple Eavesdroppers , 2019, IEEE Transactions on Communications.

[17]  Junjuan Xia,et al.  Battery-constrained Federated Edge Learning in UAV-enabled IoT for B5G/6G Networks , 2021, Phys. Commun..

[18]  Xutao Li,et al.  Impact of outdated antenna selection on cache-aided UAV relay networks , 2019, Phys. Commun..

[19]  Octavia A. Dobre,et al.  Hardware Impaired Ambient Backscatter NOMA Systems: Reliability and Security , 2020, IEEE Transactions on Communications.

[20]  George K. Karagiannidis,et al.  Opportunistic Access Point Selection for Mobile Edge Computing Networks , 2021, IEEE Transactions on Wireless Communications.

[21]  Rui Zhao,et al.  Deep Reinforcement Learning Based Mobile Edge Computing for Intelligent Internet of Things , 2020, Phys. Commun..

[22]  Lihua Li,et al.  Secrecy Analysis of Ambient Backscatter NOMA Systems Under I/Q Imbalance , 2020, IEEE Transactions on Vehicular Technology.

[23]  Caijun Zhong,et al.  Robust Design for Intelligent Reflecting Surfaces Assisted MISO Systems , 2020, IEEE Communications Letters.

[24]  Yunfei Chen,et al.  UAV-Relaying-Assisted Secure Transmission With Caching , 2019, IEEE Transactions on Communications.

[25]  Caijun Zhong,et al.  Performance Analysis of Intelligent Reflecting Surface Aided Communication Systems , 2020, IEEE Communications Letters.

[26]  Dong Li,et al.  Ultra-reliable MU-MIMO detector based on deep learning for 5G/B5G-enabled IoT , 2020, Phys. Commun..

[27]  Caijun Zhong,et al.  Programmable Metasurface-Based Multicast Systems: Design and Analysis , 2020, IEEE Journal on Selected Areas in Communications.

[28]  Dan Deng,et al.  A Note on Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters , 2020, IEEE Transactions on Broadcasting.

[29]  George K. Karagiannidis,et al.  Learning-Based Signal Detection for MIMO Systems With Unknown Noise Statistics , 2021, IEEE Transactions on Communications.

[30]  Liseng Fan,et al.  Intelligent ubiquitous computing for future UAV-enabled MEC network systems , 2021, Cluster Computing.

[31]  George K. Karagiannidis,et al.  Dynamic Offloading for Multiuser Muti-CAP MEC Networks: A Deep Reinforcement Learning Approach , 2021, IEEE Transactions on Vehicular Technology.

[32]  Lihua Li,et al.  Cooperative Wireless-Powered NOMA Relaying for B5G IoT Networks With Hardware Impairments and Channel Estimation Errors , 2020, IEEE Internet of Things Journal.

[33]  Abbas El Gamal,et al.  Capacity theorems for the relay channel , 1979, IEEE Trans. Inf. Theory.

[34]  Caijun Zhong,et al.  Location Information Aided Multiple Intelligent Reflecting Surface Systems , 2020, IEEE Transactions on Communications.

[35]  Houbing Song,et al.  Cache-enabled in cooperative cognitive radio networks for transmission performance , 2020, Tsinghua Science and Technology.

[36]  MengChu Zhou,et al.  A Dynamic Evolution Method for Autonomous Vehicle Groups in a Highway Scene , 2022, IEEE Internet of Things Journal.

[37]  Guan Gui,et al.  Machine-Learning-Aided Trajectory Prediction and Conflict Detection for Internet of Aerial Vehicles , 2021, IEEE Internet of Things Journal.