Cell-Free Satellite-UAV Networks for 6G Wide-Area Internet of Things

In fifth generation (5G) and beyond Internet of Things (IoT), it becomes increasingly important to serve a massive number of IoT devices outside the coverage of terrestrial cellular networks. Due to their own limitations, unmanned aerial vehicles (UAVs) and satellites need to coordinate with each other in the coverage holes of 5G, leading to a cognitive satellite-UAV network (CSUN). In this paper, we investigate multi-domain resource allocation for CSUNs consisting of a satellite and a swarm of UAVs, so as to improve the efficiency of massive access in wide areas. Particularly, the cell-free on-demand coverage is established to overcome the cost-ineffectiveness of conventional cellular architecture. Opportunistic spectrum sharing is also implemented to cope with the spectrum scarcity problem. To this end, a process-oriented optimization framework is proposed for jointly allocating subchannels, transmit power and hovering times, which considers the whole flight process of UAVs and uses only the slowly-varying large-scale channel state information (CSI). Under the on-board energy constraints of UAVs and interference temperature constraints from UAV swarm to satellite users, we present iterative multi-domain resource allocation algorithms to improve network efficiency with guaranteed user fairness. Simulation results demonstrate the superiority of the proposed algorithms. Moreover, the adaptive cell-free coverage pattern is observed, which implies a promising way to efficiently serve wide-area IoT devices in the upcoming sixth generation (6G) era.

[1]  W. Murray,et al.  A Projected Lagrangian Algorithm for Nonlinear Minimax Optimization , 1980 .

[2]  Sang-Jo Yoo,et al.  Optimal UAV Path Planning: Sensing Data Acquisition Over IoT Sensor Networks Using Multi-Objective Bio-Inspired Algorithms , 2018, IEEE Access.

[3]  Youngnam Han,et al.  Energy-Efficient UAV Routing for Wireless Sensor Networks , 2020, IEEE Transactions on Vehicular Technology.

[4]  Jeffrey G. Andrews,et al.  Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints , 2005, IEEE Transactions on Wireless Communications.

[5]  Cheng-Xiang Wang,et al.  A Survey of 5G Channel Measurements and Models , 2018, IEEE Communications Surveys & Tutorials.

[6]  Andrea Zanella,et al.  Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios , 2015, IEEE Wireless Communications.

[7]  Junaid Qadir,et al.  Will 5G See its Blind Side? Evolving 5G for Universal Internet Access , 2016, GAIA@SIGCOMM.

[8]  Walid Saad,et al.  Optimized Path Planning for Inspection by Unmanned Aerial Vehicles Swarm with Energy Constraints , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[9]  Rui Zhang,et al.  UAV-Aided Offloading for Cellular Hotspot , 2017, IEEE Transactions on Wireless Communications.

[10]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[11]  Youzheng Wang,et al.  Outage performance of non-orthogonal multiple access based unmanned aerial vehicles satellite networks , 2018, China Communications.

[12]  Mohamed-Slim Alouini,et al.  Aeronautical Data Aggregation and Field Estimation in IoT Networks: Hovering and Traveling Time Dilemma of UAVs , 2018, IEEE Transactions on Wireless Communications.

[13]  Muhammad Ali Imran,et al.  Semi-Adaptive Beamforming for OFDM Based Hybrid Terrestrial-Satellite Mobile System , 2012, IEEE Transactions on Wireless Communications.

[14]  Mahesh Sooriyabandara,et al.  Low Power Wide Area Networks: An Overview , 2016, IEEE Communications Surveys & Tutorials.

[15]  Huaiyu Dai,et al.  Optimal Resource Allocation in Random Access Cooperative Cognitive Radio Networks , 2015, IEEE Transactions on Mobile Computing.

[16]  Jue Wang,et al.  Aerial Small Cells Using Coordinated Multiple UAVs: An Energy Efficiency Optimization Perspective , 2019, IEEE Access.

[17]  Mohsen Guizani,et al.  Optimal Preamble Design in Spatial Group-Based Random Access for Satellite-M2M Communications , 2019, IEEE Wireless Communications Letters.

[18]  Bamidele Adebisi,et al.  Low-Power Wide Area Network Technologies for Internet-of-Things: A Comparative Review , 2019, IEEE Internet of Things Journal.

[19]  Cheng-Xiang Wang,et al.  Optimal Beamforming for Hybrid Satellite Terrestrial Networks With Nonlinear PA and Imperfect CSIT , 2020, IEEE Wireless Communications Letters.

[20]  Sumei Sun,et al.  Energy-Efficient, Large-Scale Distributed-Antenna System (L-DAS) for Multiple Users , 2013, IEEE Journal of Selected Topics in Signal Processing.

[21]  Ning Ge,et al.  Exploiting the Shipping Lane Information for Energy-Efficient Maritime Communications , 2019, IEEE Transactions on Vehicular Technology.

[22]  Qingqing Wu,et al.  Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks , 2017, IEEE Transactions on Wireless Communications.

[23]  Symeon Chatzinotas,et al.  Cognitive spectrum utilization in Ka band multibeam satellite communications , 2015, IEEE Communications Magazine.

[24]  Xiaofeng Tao,et al.  Data Aggregation in Massive Machine Type Communication: Challenges and Solutions , 2019, IEEE Access.

[25]  Derrick Wing Kwan Ng,et al.  Robust Beamforming for NOMA-Based Cellular Massive IoT With SWIPT , 2020, IEEE Transactions on Signal Processing.

[26]  Yongming Huang,et al.  Joint CoMP Transmission for UAV-Aided Cognitive Satellite Terrestrial Networks , 2019, IEEE Access.

[27]  Ning Ge,et al.  Virtual MIMO in Multi-Cell Distributed Antenna Systems: Coordinated Transmissions with Large-Scale CSIT , 2013, IEEE Journal on Selected Areas in Communications.

[28]  Igor Bisio,et al.  Satellite Communications Supporting Internet of Remote Things , 2016, IEEE Internet of Things Journal.

[29]  Ning Ge,et al.  UAV Swarm-Enabled Aerial CoMP: A Physical Layer Security Perspective , 2019, IEEE Access.

[30]  Louis J. Ippolito,et al.  Attenuation by Atmospheric Gases , 1986 .

[31]  Yunfei Chen,et al.  Optimum Placement of UAV as Relays , 2018, IEEE Communications Letters.

[32]  Hai Lin,et al.  Massive Beam-Division Multiple Access for B5G Cellular Internet of Things , 2020, IEEE Internet of Things Journal.

[33]  Prabhu Babu,et al.  Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning , 2017, IEEE Transactions on Signal Processing.

[34]  Gan Zheng,et al.  Optimum Deployment of Multiple UAVs for Coverage Area Maximization in the Presence of Co-Channel Interference , 2019, IEEE Access.

[35]  Ana I. Pérez-Neira,et al.  Hybrid Analog–Digital Transmit Beamforming for Spectrum Sharing Backhaul Networks , 2018, IEEE Transactions on Signal Processing.

[36]  Qingqing Wu,et al.  Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond , 2019, Proceedings of the IEEE.

[37]  Danijela Cabric,et al.  Energy-Efficient Massive IoT Shared Spectrum Access Over UAV-Enabled Cellular Networks , 2018, IEEE Transactions on Communications.

[38]  Sanjeev Jain,et al.  A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges , 2019, IEEE Access.

[39]  Jianhua Lu,et al.  UAV-Aided MIMO Communications for 5G Internet of Things , 2019, IEEE Internet of Things Journal.

[40]  Qiwei Wang,et al.  A Framework of Non-Orthogonal Slotted Aloha (NOSA) Protocol for TDMA-Based Random Multiple Access in IoT-Oriented Satellite Networks , 2018, IEEE Access.