Mobile Localisation of 5G and Beyond 5G Cellular Networks

Boda Liu
2021
Mobile localisation is one of the main functions of the fifth generation (5G) and beyond 5G (B5G) cellular networks, enabling high quality location services (LCSs). In this thesis, a number of challenging problems on localisation are addressed for 5G and B5G networks under different use cases, including device-to-device (D2D) connectivity, unmanned aerial vehicle (UAV) mounted base station (BS), massive multiple-input multiple-output (MIMO) antenna, and intelligent surface (IS) array. The
more » ... S) array. The detailed contributions are shown as following items. In the first contribution, I propose a cooperative localisation technique based on timeof-arrival (TOA), angle-of-arrival (AOA) and angle-of-departure (AOD) observed at BSs, and received-signal-strength (RSS) collected from collaborative mobile stations (MSs) in single-bounce multipath environment, named as CLTAAR, to mitigate non-line-of-sight (NLOS) error due to single-bounce scattering. This scheme is further improved by a proposed weight function of variance of measurements. Then, a grouping strategy is integrated with the proposed work to reduce the running time of estimation progress, named as eCLTAAR. The system performance is verified by simulations and Cramer-Rao Lower Bound (CRLB). It is shown that the proposed techniques outperform existing approaches in terms of localisation accuracy and running time. In the second contribution, unmanned aerial vehicle (UAV)-base stations (BSs) assisted and received signal strength (RSS) based mobile station (MS) localisation is investigated. A practical air-to-ground path loss model is utilised, where the path loss exponent (PLE) varies with the elevation angle and altitude of UAV, and the accurate PLE estimate is often difficult to obtain. With unknown and unequal PLEs for different UAVs, the UAVs assisted localisation problem becomes nonlinear and non-convex, which cannot be solved by the existing methods. Two localisation approaches are proposed to solve the problem with known transmit power, unknown and unequal PLEs, and one approach with estimated ii Mobile Localisation of 5G and Beyond 5G Cellular Networks Boda Liu transmit power is proposed for the scenario with all the parameters unknown. Simulation results show a much higher accuracy achieved by the proposed schemes than the existing approaches with perfect knowledge of either one or all the parameters. In addition, an anti-intuitive finding verifies the analytical higher accuracy of localisation and ranging distance obtained with estimated parameters than that of the real parameters. Thus, it suggests to apply the estimated parameters rather than real parameters, even if the perfect knowledge is available. The effectiveness of proposed approaches are also verified by the Cramer-Rao lower bound (CRLB) derived. In the last but not least contribution, we investigate an intelligent surface (IS) assisted massive MIMO based localisation. Large IS array (Ix) that in near-field regime is divided into multiple non-overlapping sub-arrays with approximated channel parameters allocated. Both approximated Fisher information matrix (aFIM) and exact FIM (eFIM) are derived. To improve performance of localisation, a localisation-aimed IS phase shifter (lo-ISpsf) is first proposed to minimise position error bound and orientation error bound, rather than maximise data rate as done by communication-aimed ISpsf (co-ISpsf) in the existing research. Simulation results show the much higher accuracy of proposed lo-ISpsf than that of existing co-ISpsf in different cases with various number of Ix elements and quantization bits. ISpsf of 1-bit quantizer is the most efficient in most cases. The numerical results also reflect the significant degradation on accuracy caused by absence of knowledge of IS position and orientation. iii This thesis would not have been finished without loads of support and help from the following people. Therefore, I would like to take this opportunity to thank them all.
doi:10.17638/03106793 fatcat:4d5prklskbdj7f7stwg34tmbne