# Dynamic Packet Duplication for Industrial URLLC
## 3 Proposal
### KPI to S-KPI Mapping  
In cellular networks, performance monitoring (PM) indicators are collected from different points of the network: gNBs, radio interfaces, core network etc. UE traces can also be collected, representing the radio PM indicators collected by the terminals. The PMs are sent to a centralized location where they are analyzed.  
Monitoring the network provides many alternatives for detection of problems, analysis of the performance, among others. Nevertheless, these KPIs contains information from lower layers of the network, but not on the performance at application layer in the UEs. Since the development of 5G is centered on the E2E Quality of Service (QoS), this approach has gained importance in recent years. S-KPIs [17] measure these magnitudes. S-KPIs are specific to the final application; that is, for a video transmission, relevant S-KPIs include the average buffer size or the number of stalling, while, for delay-sensitive applications, the main S-KPI is the E2E latency.  
The main inconvenience of S-KPI is that they are difficult to measure. They require special procedures at the application layer of the device. The E2E latency can only be measured a posteriori, thus, becoming useless to decide whether a special transmission, such as PD can be performed at a given time.  
To address this, a KPI to S-KPI estimator can be used [19]. This technique allows an estimation of the S-KPI based on the available KPIs, which are easy to obtain. Since the KPIs that can be obtained are only a subset of the variables that influence the value of the S-KPI, the estimator will always have a margin for error, and the formula for the mapping will not be trivial. In [19], the approach for creating such estimator is with ML techniques.  
In this paper, the hypothesis is that, through this mapping, the E2E latency can be obtained a priori by monitoring the following KPIs:  
* Signal to Interference plus Noise Ratio (SINR): includes all the usable signals in the computation. It is used by some vendors to better determine the CQI to adapt the modulation.
* Modulation index: indicates the modulation index used from the table of the MCS when performing a packet transmission. A higher index selects a more efficient modulation, with a higher spectral efficiency and code rate. Otherwise, a lower modulation index selects a more robust modulation, with a lower spectral efficiency and code rate.
* Reception Success: indicates if a packet has been decoded successfully at the receiver or not. This is used to determine if a packet has suffered a HARQ retransmission, since the NACK message is indicated by the receiver to the base station.  
The hypothesis assumes that the RAN network is the main contributor to the variable part of the latency. This is due to the jitter being higher at Radio Access Technologies  
Figure 2: Block diagram of the system.  
(RATs) [32] as opposed to the wired networks present in the trunk [33]. When using MEC, the processing is done with the gNB or after a negligible network path, and thus the trunk network component is canceled completely. Latency does not depend directly on the measured KPIs but instead on factors, such as the network load. Nevertheless, in this paper, it is assumed that these KPIs represent the overall status of the RAN connection.  
Since the relations between the measured KPIs and the factors that determine the latency are complex, ML is commonly used to find and exploit such relations. In this paper, ML generates the prediction model that encompasses the complex relations among the cited KPIs and the latency.