## 5 Beyond 5G
New latency solutions being considered for 6G include: allowing inter-layer interactions at the Radio Access Network (such inter-layer interactions would allow the RAN nodes to directly access LTE positioning protocol messages, which could drastically reduce the overall latency budget of positioning sessions), shortening the distance between the location server and the NG-RAN (which could be crucial for latency-intolerant 6G applications), and UE-based positioning, which could transform cellular positioning toward UE-based calculation in a network-controlled manner [25]. Additionally, artificial intelligence and machine learning are expected to play an important role in the cellular network of the future [25; 69; 71; 73; 70], especially in conjunction with traditional positioning solutions. AI is expected to have an impact on mobile positioning for Non-Line-Of-Sight (NLoS) multi-paths and possibly on achieving ultra-low latency [70]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Major Factors** & **6G** & **5G** \\ \hline Peak data rate & \textgreater{}100 Gb/s & 10[20] Gb/s \\ \hline User experience data rate & \textgreater{}10 Gb/s & 1 Gb/s \\ \hline Traffic density & \textgreater{}100 Tb/s/km\({}^{2}\) & 10 Tb/s/km\({}^{2}\) \\ \hline Connection density & \textgreater{}10 million/km\({}^{2}\) & 1 million/km\({}^{2}\) \\ \hline Delay & \textless{}1 ms & ms level \\ \hline Mobility & \textgreater{}1000 km/h & 350 km/h \\ \hline Spectrum efficiency & \textgreater{}3x relative to 5G & 3–5x relative to 4G \\ \hline Energy efficiency & \textgreater{}10x relative to 5G & 1000x relative to 4G \\ \hline Coverage percent & \textgreater{}99\% & \textgreater{}70\% \\ \hline Reliability & \textgreater{}99.999\% & \textgreater{}99.9\% \\ \hline Positioning precision & Centimeter level & Meter level \\ \hline Receiver sensitivity & \textless{}130 dBm & About –120 dBm \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The possible capabilities of 6G as opposed to 5G [74]. It is predicted to be especially crucial in data-rich and complex localization applications (e.g., poor GNSS channel conditions indoor and outdoor urban environments) where we have a large number of multi-modal, indirect, and noisy observations and the physical properties of the system's nonlinear signal characteristics may be unknown or difficult to model [69]. Another application of AI and ML methods could be semantic localization, as suggested in [73]. Semantic localization means that the position of the target is not given with numerical coordinates, but with a semantic description, such as \"put this equipment on the table or in the recycle bin\". In this case, the AI algorithm needs to interpret the connection between the physical coordinates and semantic locations. Possible further novelties expected in networks beyond 5G include lower instantaneous PRS bandwidth for positioning (to reduce complexity), sidelink solutions to enable localization in areas with partial coverage or no cellular coverage, decentralized architecture, device-free localization, the inclusion of location functionalities in the RAN, as well as intelligent environments and Intelligent Reflective Surfaces (IRS) [25; 70; 72]. Among these, a lot of attention has recently been given to intelligent reflecting surfaces in particular, which comprise an array of IRS units, each of which can independently incur some change to the incident signal (such as amplitude, frequency, or even polarization, but most often a phase shift) [72; 78]. IRS are meant to intelligently configure the wireless environment to help the transmissions between the sender and receiver in case direct communications have bad qualities [78], and they offer a cost-effective solution to link blockage problems in mmWave communications [79].