Machine Learning-Based 3D Channel Modeling for U2V mmWave Communications

Unmanned aerial vehicle (UAV) millimeter wave (mmWave) technologies can provide flexible link and high data rate for future communication networks. By considering the new features of three-dimensional (3D) scattering space, 3D velocity, 3D antenna array, and especially 3D rotations, a machine learning (ML) integrated UAV-to-Vehicle (U2V) mmWave channel model is proposed. Meanwhile, a ML-based network for channel parameter calculation and generation is developed. The deterministic parameters are calculated based on the simplified geometry information, while the random ones are generated by the back propagation based neural network (BPNN) and generative adversarial network (GAN), where the training data set is obtained from massive ray-tracing (RT) simulations. Moreover, theoretical expressions of channel statistical properties, i.e., power delay profile (PDP), autocorrelation function (ACF), Doppler power spectrum density (DPSD), and cross-correlation function (CCF) are derived and analyzed. Finally, the U2V mmWave channel is generated under a typical urban scenario at 28 GHz. The generated PDP and DPSD show good agreement with RT-based results, which validates the effectiveness of proposed method. Moreover, the impact of 3D rotations, which has rarely been reported in previous works, can be observed in the generated CCF and ACF, which are also consistent with the theoretical and measurement results.

[1]  Robert W. Heath,et al.  Measurements of the 60 GHz UE to eNB Channel for Small Cell Deployments , 2017, IEEE Wireless Communications Letters.

[2]  Henrik Lehrmann Christiansen,et al.  Model-Aided Deep Learning Method for Path Loss Prediction in Mobile Communication Systems at 2.6 GHz , 2020, IEEE Access.

[3]  Bo Ai,et al.  Towards Connected Unmanned Aerial System: A Channel Modeling Perspective. , 2020, 2012.06707.

[4]  Xiaofei Zhang,et al.  Direction of Departure (DOD) and Direction of Arrival (DOA) Estimation in MIMO Radar with Reduced-Dimension MUSIC , 2010, IEEE Communications Letters.

[5]  Jianjiang Zhou,et al.  MmWave beamforming for UAV communications with unstable beam pointing , 2019, China Communications.

[6]  Sergey Andreev,et al.  Line-of-Sight Probability for mmWave-Based UAV Communications in 3D Urban Grid Deployments , 2021, IEEE Transactions on Wireless Communications.

[7]  Fengchun Zhang,et al.  Near-Field Ultra-Wideband mmWave Channel Characterization Using Successive Cancellation Beamspace UCA Algorithm , 2019, IEEE Transactions on Vehicular Technology.

[8]  Leonardo Mostarda,et al.  Cognition in UAV-Aided 5G and Beyond Communications: A Survey , 2020, IEEE Transactions on Cognitive Communications and Networking.

[9]  Theodore S. Rappaport,et al.  Indoor Wireless Channel Properties at Millimeter Wave and Sub-Terahertz Frequencies , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[10]  Theodore S. Rappaport,et al.  Millimeter Wave and Sub-Terahertz Spatial Statistical Channel Model for an Indoor Office Building , 2021, IEEE Journal on Selected Areas in Communications.

[11]  Yan Zhang,et al.  Machine‐learning‐based prediction methods for path loss and delay spread in air‐to‐ground millimetre‐wave channels , 2019, IET Microwaves, Antennas & Propagation.

[12]  Haralabos C. Papadopoulos,et al.  Predicting Wireless Channel Features Using Neural Networks , 2018, 2018 IEEE International Conference on Communications (ICC).

[13]  Angel Lozano,et al.  Lightweight UAV-based Measurement System for Air-to-Ground Channels at 28 GHz , 2021, 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

[14]  Cheng-Xiang Wang,et al.  A Novel 3D Non-Stationary Wireless MIMO Channel Simulator and Hardware Emulator , 2018, IEEE Transactions on Communications.

[15]  Athanasios G. Kanatas,et al.  Three-Dimensional Modeling of mmWave Doubly Massive MIMO Aerial Fading Channels , 2020, IEEE Transactions on Vehicular Technology.

[16]  Angel Lozano,et al.  Millimeter Wave Channel Modeling via Generative Neural Networks. , 2020 .

[17]  Jianhua Zhang,et al.  The way to apply machine learning to IoT driven wireless network from channel perspective , 2019, China Communications.

[18]  Qihui Wu,et al.  An Amateur Drone Surveillance System Based on the Cognitive Internet of Things , 2017, IEEE Communications Magazine.

[19]  Liuqing Yang,et al.  Playback of 5G and Beyond Measured MIMO Channels by an ANN-Based Modeling and Simulation Framework , 2020, IEEE Journal on Selected Areas in Communications.

[20]  Kim Olesen,et al.  Measured wideband characteristics of indoor channels at centimetric and millimetric bands , 2016, EURASIP J. Wirel. Commun. Netw..

[21]  Xiaomin Chen,et al.  3D non‐stationary geometry‐based multi‐input multi‐output channel model for UAV‐ground communication systems , 2019, IET Microwaves, Antennas & Propagation.

[22]  Xi Zhang,et al.  Three-dimensional non-stationary geometry-based stochastic model for UAV-MIMO Ricean fading channels , 2019, IET Commun..

[23]  Baigen Cai,et al.  A DNN-Based Channel Model for Network Planning in Train Control Systems , 2022, IEEE Transactions on Intelligent Transportation Systems.

[24]  David W. Matolak,et al.  Air–Ground Channel Characterization for Unmanned Aircraft Systems Part II: Hilly and Mountainous Settings , 2017, IEEE Transactions on Vehicular Technology.

[25]  Xiaomin Chen,et al.  Effects of Digital Map on the RT-based Channel Model for UAV mmWave Communications , 2020, 2020 International Wireless Communications and Mobile Computing (IWCMC).

[26]  Zeyu Huang,et al.  Air-to-Ground Channel Characterization for Low-Height UAVs in Realistic Network Deployments , 2020, IEEE Transactions on Antennas and Propagation.

[27]  Dheryta Jaisinghani,et al.  Millimeter-Wave Base Stations in the Sky: An Experimental Study of UAV-to-Ground Communications , 2022, IEEE Transactions on Mobile Computing.

[28]  Xiaofei Zhang,et al.  DOA Estimation Based on Combined Unitary ESPRIT for Coprime MIMO Radar , 2017, IEEE Communications Letters.

[29]  Ozgur Ozdemir,et al.  Temporal and Spatial Characteristics of mm Wave Propagation Channels for UAVs , 2018, 2018 11th Global Symposium on Millimeter Waves (GSMM).

[30]  Mengtian Yao,et al.  3GPP TR 38.901 Channel Model , 2021 .

[31]  Pekka Kyosti,et al.  Validation of 5G METIS map-based channel model at mmwave bands in indoor scenarios , 2016, 2016 10th European Conference on Antennas and Propagation (EuCAP).

[32]  R. Geise,et al.  Modulating Features of Field Measurements with a UAV at Millimeter Wave Frequencies , 2018, 2018 IEEE Conference on Antenna Measurements & Applications (CAMA).

[33]  Ming Xiao,et al.  A Learning-Based Spectrum Access Stackelberg Game: Friendly Jammer-Assisted Communication Confrontation , 2021, IEEE Transactions on Vehicular Technology.

[34]  Kwang-Cheng Chen,et al.  Machine Learning for Wireless Communication Channel Modeling: An Overview , 2019, Wireless Personal Communications.

[35]  Tomas Akenine-Möller,et al.  Fast, minimum storage ray/triangle intersection , 1997, J. Graphics, GPU, & Game Tools.

[36]  Troels Pedersen,et al.  Learning Parameters of Stochastic Radio Channel Models From Summaries , 2020, IEEE Open Journal of Antennas and Propagation.

[37]  François Quitin,et al.  Multi-Frequency Air-to-Ground Channel Measurements and Analysis for UAV Communication Systems , 2020, IEEE Access.

[38]  Yang Li,et al.  Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities , 2019, IEEE Communications Magazine.

[39]  Bo Ai,et al.  Wireless Channel Sparsity: Measurement, Analysis, and Exploitation in Estimation , 2021, IEEE Wireless Communications.

[40]  Jian Yu,et al.  A Kernel-Power-Density-Based Algorithm for Channel Multipath Components Clustering , 2017, IEEE Transactions on Wireless Communications.

[41]  Bo Ai,et al.  A Wideband Non-Stationary Air-to-Air Channel Model for UAV Communications , 2020, IEEE Transactions on Vehicular Technology.

[42]  David W. Matolak,et al.  A 3-D Geometry-Based Stochastic Model for Unmanned Aerial Vehicle MIMO Ricean Fading Channels , 2020, IEEE Internet of Things Journal.

[43]  Li Pei,et al.  Machine-Learning-Based Fast Angle-of-Arrival Recognition for Vehicular Communications , 2021, IEEE Transactions on Vehicular Technology.

[44]  Hao Jiang,et al.  Three-Dimensional Non-Stationary Wideband Geometry-Based UAV Channel Model for A2G Communication Environments , 2019, IEEE Access.

[45]  Erik G. Larsson,et al.  Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts , 2020, Science China Information Sciences.

[46]  Cheng-Xiang Wang,et al.  Spatial Correlations of a 3-D Non-Stationary MIMO Channel Model With 3-D Antenna Arrays and 3-D Arbitrary Trajectories , 2019, IEEE Wireless Communications Letters.

[47]  Hongming Zhang,et al.  A Novel 3D UAV Channel Model for A2G Communication Environments Using AoD and AoA Estimation Algorithms , 2020, IEEE Transactions on Communications.

[48]  B. Ai,et al.  Three-Dimensional Modeling of Millimeter-Wave MIMO Channels for UAV-Based Communications , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

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

[50]  Kai Mao,et al.  A Geometry-Based Beamforming Channel Model for UAV mmWave Communications , 2020, Sensors.

[51]  JIACHI ZHANG,et al.  Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning , 2020, IEEE Access.

[52]  Jian Yu,et al.  Clustering Enabled Wireless Channel Modeling Using Big Data Algorithms , 2018, IEEE Communications Magazine.

[53]  5 G Channel Model for bands up to 100 GHz , 2015 .

[54]  Hengtai Chang,et al.  A Novel Nonstationary 6G UAV-to-Ground Wireless Channel Model With 3-D Arbitrary Trajectory Changes , 2021, IEEE Internet of Things Journal.

[55]  Ismail Güvenç,et al.  UAV Air-to-Ground Channel Characterization for mmWave Systems , 2017, 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall).

[56]  Cheng-Xiang Wang,et al.  Modeling and Simulation for UAV Air-to-Ground mmWave Channels , 2020, 2020 14th European Conference on Antennas and Propagation (EuCAP).

[57]  Timothy J. O'Shea,et al.  Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks , 2018, 2019 International Conference on Computing, Networking and Communications (ICNC).

[58]  Wu Qihui,et al.  Channel Estimation Enhancement With Generative Adversarial Networks , 2021, IEEE Transactions on Cognitive Communications and Networking.