# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 2 Related Work
### Radio Technologies and 5G NR
Results show that their model can achieve a sub-meter level positioning accuracy and a nano-second level network synchronization using a dedicated edge localization server. Higher propagation losses at mmWave frequencies can degrade the localization accuracy in 5G NR networks. [PERSON] et al. [31] derived the Cramer-Rao Lower Bound (CRLB) for position and orientation errors. The model consists of a single BS with multiple antennae at a height of 10 m and a UE with multiple antennae operating at a carrier frequency of 38 GHz, rotated by 10 degree interval step. The Fisher Information Matrix (FIM) is computed for the Angle of Arrival (AoA), Angle of Departure (AoD), and ToA of multi-path signals, from which the error bounds for position and orientation are then computed. Results show that the Up-Link (UL) and DL signals are not identical. UL positioning is sensitive to UE orientation, whereas DL is not. In UL, the UE produces narrow beams travelling towards the BS. However, these beams might not even reach the BS because of the UE orientation at any given time, thereby decreasing the localization accuracy. They also showed that the mmWave systems can estimate the state of UE with a sub-meter position error and sub-degree orientation error for a BS-UE separation distances of up to 50 m.  
A model to investigate the localization accuracy achieved by 5G NR is described in [32] for a Connected Autonomous Vehicle (CAV) use case, in which the CAV is not assumed to be time synchronized with the BS. A Two-Way Localization (TWL) method is proposed for this scenario based on two protocols. In the first protocol, Round-Trip Localization Protocol (RLP), the BS sends a signal to the CAV, which in turn responds with another signal after a pre-agreed time interval. Positioning is performed by considering only the DL signal. In the second protocol, Collaborative Localization Protocol (CLP), the CAV sends back the response to the BS and localization is achieved with both the transmitted and received signals by the BS. Thereafter, the Position Error Bound (PEB) and Orientation Error Bound (OEB) for both protocols are determined. Numerical results show that the CLP outperforms RLP in terms of error bounds. Additionally, the authors prove that having more antennae on the BS is more beneficial than having more antennae on the UE. In [33; 34; 35], a model to estimate the speed, direction, and position of a moving UE based on a time reversal technique of focused beams and the effect of the transmitted power is proposed. Based on the focusing beam of a 5G MIMO system and the received signal by the UE, the Auto Correlation Function (ACF) is computed. Thereafter, the localization algorithm computes the UE speed, direction, and position by taking the ACF of the received signal and considering the geometrical positions of BS as inputs. A positioning accuracy of 0.2 m and orientation accuracy of 1.8\({}^{\circ}\) is achieved through simulations. In 5G networks, as with general radio-based mapping approaches, it is possible to create a _map of the environment_ in order to assess the signal propagation and coverage of the cellular network. A radio map represents the signal quality between the BS and how a receiver perceives the network capabilities. An appropriate representation of radio maps, that has been a topic of research over the years in the fields of geodesy and telecommunnications, are REMs [36], which can provide insights on how the radio signals travel in a given geographical area and how the obstacles limit coverage. REMs are built using either direct approaches, e.g., via interpolation methods [37; 38; 39; 40] or indirect approaches without requiring the knowledge of the actual transmitter locations, e.g., LiVE [41], Signal to Noise Ratio (SNR)-aided methods [42], and Hybrid schemes [43]. Building a radio map with spatial coherence is an important and resource-intensive task, similarly to other robotic mapping processes based on visual and LiDAR features, such as varying image intensities or edge and plane information from laser ranging.