Rate Splitting on Mobile Edge Computing for UAV-Aided IoT Systems

In the Internet of Things (IoT), numerous low complexity and energy constrained devices are employed to collect and transmit data simultaneously, where the unmanned aerial vehicle (UAV) is an efficient means to relay the signals. Considering the limited power and computational capability of UAV, mobile edge computing (MEC) is carried out to enhance the usage of UAV-aided IoT networks. In order to develop robust UAV-aided MEC systems, energy efficient transmission schemes and low-latency computational resource allocation become crucial to cope with the energy limitation of UAV and computing delay of MEC. In this paper, we develop UAV-aided MEC systems, where UAVs collect data from IoT devices and then transmit to MEC-based access points for computation. In order to minimize the energy consumption of a UAV in the centralized and distributed MEC computation modes under the constraints of transmission rate and computational time, respectively, a joint rate splitting problem is formulated to optimally allocate rates for transmission links between two antenna arrays on the UAV. In addition, the altitude of UAV is analyzed and designed. From simulation results, it shows the proposed architecture is able to provide robust and high quality transmission rate for the UAV-aided MEC systems.

[1]  Mohamed-Slim Alouini,et al.  Data-Oriented Transmission in Future Wireless Systems: Toward Trustworthy Support of Advanced Internet of Things , 2019, IEEE Vehicular Technology Magazine.

[2]  Bo Ai,et al.  Mobility Model-Based Non-Stationary Mobile-to-Mobile Channel Modeling , 2018, IEEE Transactions on Wireless Communications.

[3]  Xuemin Shen,et al.  Cloud assisted HetNets toward 5G wireless networks , 2015, IEEE Communications Magazine.

[4]  Rui Zhang,et al.  Wireless communications with unmanned aerial vehicles: opportunities and challenges , 2016, IEEE Communications Magazine.

[5]  Jie Xu,et al.  Mobile Edge Computing for Cellular-Connected UAV: Computation Offloading and Trajectory Optimization , 2018, 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[6]  Changchuan Yin,et al.  Optimized Trajectory Design in UAV Based Cellular Networks for 3D Users: A Double Q-Learning Approach , 2019, J. Commun. Inf. Networks.

[7]  Jie Zhang,et al.  Mobile-Edge Computation Offloading for Ultradense IoT Networks , 2018, IEEE Internet of Things Journal.

[8]  Jinho Choi,et al.  Cooperative Multiuser Beamforming in mmWave Distributed Antenna Systems , 2018, IEEE Transactions on Vehicular Technology.

[9]  Kyungwhoon Cheun,et al.  Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results , 2014, IEEE Communications Magazine.

[10]  Miao Pan,et al.  IoT Enabled UAV: Network Architecture and Routing Algorithm , 2019, IEEE Internet of Things Journal.

[11]  Yong Ren,et al.  Energy-Efficient Computation Offloading for Secure UAV-Edge-Computing Systems , 2019, IEEE Transactions on Vehicular Technology.

[12]  Zhu Han,et al.  Response Delay Optimization in Mobile Edge Computing Enabled UAV Swarm , 2020, IEEE Transactions on Vehicular Technology.

[13]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[14]  Fuhui Zhou,et al.  Computation-Efficient Offloading and Trajectory Scheduling for Multi-UAV Assisted Mobile Edge Computing , 2020, IEEE Transactions on Vehicular Technology.

[15]  Kezhi Wang,et al.  Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks , 2019, IEEE Transactions on Wireless Communications.

[16]  Ying Chen,et al.  TOFFEE: Task Offloading and Frequency Scaling for Energy Efficiency of Mobile Devices in Mobile Edge Computing , 2019, IEEE Transactions on Cloud Computing.

[17]  Dario Sabella,et al.  Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things , 2016, IEEE Consumer Electronics Magazine.

[18]  Alexander Gluhak,et al.  A survey on facilities for experimental internet of things research , 2011, IEEE Communications Magazine.

[19]  Theodore S. Rappaport,et al.  Wireless Communications: Principles and Practice (2nd Edition) by , 2012 .

[20]  Xiao Ma,et al.  Cost-Efficient Resource Provisioning in Cloud Assisted Mobile Edge Computing , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[21]  Haipeng Yao,et al.  Multi-UAV-Enabled Load-Balance Mobile-Edge Computing for IoT Networks , 2020, IEEE Internet of Things Journal.

[22]  Rui Han,et al.  Blockchain-Based GNSS Spoofing Detection for Multiple UAV Systems , 2019, J. Commun. Inf. Networks.

[23]  Sarmad Ullah Khan,et al.  Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges , 2012, 2012 10th International Conference on Frontiers of Information Technology.

[24]  Jinho Choi,et al.  Air-to-Ground Wireless Links for High-Speed UAVs , 2020, IEEE Journal on Selected Areas in Communications.

[25]  Jinho Choi,et al.  Low Complexity MIMO Detection , 2012 .

[26]  Lin Bai,et al.  Unmanned Aerial Vehicle Base Station (UAV-BS) Deployment With Millimeter-Wave Beamforming , 2020, IEEE Internet of Things Journal.

[27]  Ning Zhang,et al.  Joint Admission Control and Resource Allocation in Edge Computing for Internet of Things , 2018, IEEE Network.

[28]  Wei Zhang,et al.  Multi-Satellite Relay Transmission in 5G: Concepts, Techniques, and Challenges , 2018, IEEE Network.

[29]  Geoffrey Ye Li,et al.  Collaborative Cloud and Edge Computing for Latency Minimization , 2019, IEEE Transactions on Vehicular Technology.

[30]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[31]  Zhe Yu,et al.  Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing , 2020, IEEE Internet of Things Journal.

[32]  Jinho Choi,et al.  Low-Complexity Multiuser Detection in Millimeter-Wave Systems Based on Opportunistic Hybrid Beamforming , 2018, IEEE Transactions on Vehicular Technology.

[33]  Feng Shu,et al.  User Association and Path Planning for UAV-Aided Mobile Edge Computing With Energy Restriction , 2019, IEEE Wireless Communications Letters.

[34]  Markus Rupp,et al.  5G Key Technologies for Smart Railways , 2020, Proceedings of the IEEE.

[35]  Jun Zhang,et al.  Joint Beamforming and Power Allocation for UAV-Enabled Full-Duplex Relay , 2019, IEEE Transactions on Vehicular Technology.

[36]  Jinho Choi,et al.  Low Complexity MIMO Receivers , 2014 .

[37]  Yan Zhang,et al.  Mobile Edge Computing: A Survey , 2018, IEEE Internet of Things Journal.

[38]  Jennifer Urner,et al.  Antenna Theory And Design , 2016 .

[39]  Sneha A. Dalvi,et al.  Internet of Things for Smart Cities , 2017 .

[40]  Ying Chen,et al.  Dynamic Computation Offloading in Edge Computing for Internet of Things , 2019, IEEE Internet of Things Journal.