Orientation-Assisted Beam Management for Beyond 5G Systems

Finding the optimal transmit and receive beam pair for reliable communication can be challenging, especially in highly dynamic environments. Side-information from on-board sensors at the user equipment (UE) can be used to aid the beam management (BM) process. In this work, we use the orientation information coming from inertial measurement unit (IMU) for effective BM. Specifically, we use particle filter (PF) to fuse the reference signal received power (RSRP) information with orientation information. We perform extensive simulations using realistic ray-tracing channels, practical beam patterns, and various UE movement and rotation speeds. Simulation results show the proposed strategy can greatly improve the beam prediction accuracy and reduce the power loss caused by sub-optimal beam-selection.

[1]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[2]  Boon Loong Ng,et al.  Beam Codebook Design for 5G mmWave Terminals , 2019, IEEE Access.

[3]  Shi Jin,et al.  3D Scene-Based Beam Selection for mmWave Communications , 2020, IEEE Wireless Communications Letters.

[4]  Jeffrey G. Andrews,et al.  Hand Grip Impact on 5G mmWave Mobile Devices , 2019, IEEE Access.

[5]  Anum Ali,et al.  Passive Radar at the Roadside Unit to Configure Millimeter Wave Vehicle-to-Infrastructure Links , 2019, IEEE Transactions on Vehicular Technology.

[6]  Umberto Spagnolini,et al.  Inertial Sensor Aided mmWave Beam Tracking to Support Cooperative Autonomous Driving , 2019, 2019 IEEE International Conference on Communications Workshops (ICC Workshops).

[7]  Muhammad Alrabeiah,et al.  Millimeter Wave Base Stations with Cameras: Vision-Aided Beam and Blockage Prediction , 2019, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).

[8]  Robert W. Heath,et al.  Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information , 2017, IEEE Transactions on Wireless Communications.

[9]  Zhouyue Pi,et al.  An introduction to millimeter-wave mobile broadband systems , 2011, IEEE Communications Magazine.

[10]  Robert W. Heath,et al.  LIDAR Data for Deep Learning-Based mmWave Beam-Selection , 2019, IEEE Wireless Communications Letters.

[11]  Mohammad Dehghani Soltani,et al.  Terminal Orientation in OFDM-Based LiFi Systems , 2018, IEEE Transactions on Wireless Communications.

[12]  Matthew A. Nicely,et al.  Improved Parallel Resampling Methods for Particle Filtering , 2019, IEEE Access.

[13]  Amitava Ghosh,et al.  Millimeter wave V2I beam-training using base-station mounted radar , 2019, 2019 IEEE Radar Conference (RadarConf).

[14]  Robert W. Heath,et al.  Position-aided millimeter wave V2I beam alignment: A learning-to-rank approach , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[15]  Yong Soo Cho,et al.  Application of Motion Sensors for Beam-Tracking of Mobile Stations in mmWave Communication Systems , 2014, Sensors.

[16]  Mohammad Dehghani Soltani,et al.  Modeling the Random Orientation of Mobile Devices: Measurement, Analysis and LiFi Use Case , 2018, IEEE Transactions on Communications.

[17]  Mohammad Dehghani Soltani,et al.  An Orientation-Based Random Waypoint Model for User Mobility in Wireless Networks , 2020, 2020 IEEE International Conference on Communications Workshops (ICC Workshops).

[18]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[19]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[20]  Sundeep Rangan,et al.  Towards 6G Networks: Use Cases and Technologies , 2019, ArXiv.

[21]  Thomas B. Schön,et al.  Using Inertial Sensors for Position and Orientation Estimation , 2017, Found. Trends Signal Process..

[22]  Theodore S. Rappaport,et al.  Millimeter Wave Mobile Communications for 5G Cellular: It Will Work! , 2013, IEEE Access.

[23]  Bernard H. Fleury,et al.  First- and second-order characterization of direction dispersion and space selectivity in the radio channel , 2000, IEEE Trans. Inf. Theory.

[24]  Robert W. Heath,et al.  MmWave Vehicular Beam Selection With Situational Awareness Using Machine Learning , 2019, IEEE Access.

[25]  R. Swinbank,et al.  Fibonacci grids: A novel approach to global modelling , 2006 .

[26]  Sunwoo Kim,et al.  Robust Beam-Tracking for mmWave Mobile Communications , 2017, IEEE Communications Letters.

[27]  Mazen O. Hasna,et al.  Bidirectional Optical Spatial Modulation for Mobile Users: Toward a Practical Design for LiFi Systems , 2019, IEEE Journal on Selected Areas in Communications.

[28]  Robert W. Heath,et al.  Beam tracking for mobile millimeter wave communication systems , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[29]  Wei Liu,et al.  Three-dimensional millimetre-wave beam tracking based on smart phone sensor measurements and direction of arrival/time of arrival estimation for 5G networks , 2018 .

[30]  Robert W. Heath,et al.  Spatial Covariance Estimation for Millimeter Wave Hybrid Systems Using Out-of-Band Information , 2018, IEEE Transactions on Wireless Communications.

[31]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[32]  Muhammad Alrabeiah,et al.  Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks , 2020, 2021 IEEE International Conference on Communications Workshops (ICC Workshops).

[33]  Arnab Roy,et al.  A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies , 2018, IEEE Communications Surveys & Tutorials.

[34]  Erik G. Ström,et al.  Location-aided mm-wave channel estimation for vehicular communication , 2016, 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).