## 8 5G Potentials and Promises for Robot Applications
### Localisation Based on 5G  
As the 5G roll-out is in the early stages, few works have investigated 5G localisation, especially for robotics applications and in a sensor fusion framework. Besides, most are solely based on simulation results, neglecting many practical aspects. For example, the impact of synchronisation error in time-based positioning or simplification of channel models is considered. In the following, we review the current state-of-the-art 5G-based localisation. In 5G NR, pilot signals are included for positioning purposes, including Positioning Reference Signal (PRS) in the down-link and Sounded Reference Signal (SRS) in the up-link [15]. In addition to network centre frequency and bandwidth, PRS and SRS configuration also play roles in localisation accuracy. In [104], for different combinations of centre frequency, sub-carrier spacing and PRS comb-size, localisation accuracy for simple scenarios is compared in terms of Root Mean Square Error (RMSE) using simulation. The authors of [105] simulate the roadside 5G network implementation for assisted driving, showing accuracy below 20-25 cm for 50-100 MHz bandwidth. Localisation is performed based on TOA extracted as the first correlation peak between PRS and the received signal. The channel is modelled based on path loss and the TDL channel model. The impact of the geometrical placement of roadside 5G base stations on the localisation based on EKF, and how the distance from BS affects EKF linearisation error is investigated in [106]. In this paper, EKF for location estimation is also presented, in which the covariance matrix is tuned dynamically, and improvement is shown through simulation. There are several attempts at localisation in the 5G network based on CIR and CFR. The work of [107] ad dresses localisation in the up-link side based on 5G SRS information, in which, based on the received signal, the channel frequency response (CFR) is estimated. TOA and Direction of Arrival (DOA) are then evaluated using the well-known 2D multiple signal classification (MUSIC) algorithms. Localisation is finally performed, and an indoor experimental setup achieves accuracy of less than 1 m. The authors of [108] generated a fingerprint dataset of AOA and its corresponding amplitude based on the CSI matrix in a 5G network. Deep Neural network (DNN) was trained and used as a regressor for online estimation. Quasi Deterministic Radio channel Generator (QuaDRiGa) [109] was exploited for channel modelling. Approximately 1-meter accuracy for NLOS and 0.1 m for LOS was reported. Based on the SRS symbol, in an up-link, CFR is estimated for each base station in [110], and subsequently, TOA and AOA are jointly estimated for localisation. Accuracy below 1 m is acquired. Localisation under the fingerprinting framework is explored in [111] based on CSI, where the transfer learning concept is leveraged to reduce the real-world training effort. QuaDRiGa is leveraged to obtain synthetic CSI to pre-train the CNN model. In [112], angle-based fingerprint localisation is conducted. The fingerprints include the angles (zenith and azimuth) along with their corresponding power for all observed paths. To validate the results, simulation is performed by recreating 3D outdoor environments, including building geometry. AOA-based position estimation in a 5G network was experimentally reported in [113], where EKF was used at edge cloud for localisation. The research of [114; 115] is related to receiver localisation harnessing AOA under 5G MIMO System and beam-formed RSS, respectively. While TOA, TDOA, AOA, and RSS of the LOS path could be directly related to the relative positions of the transmitter and receiver, there is no explicit connection between the NLOS path and close distance. Thus, the localisation performance will be degraded noticeably, only relying on those measurements. In literature, NLOS error mitigation techniques and ray tracing-based approaches are carried out to compensate for the NLOS (see [116] and the references therein).