# Dynamic Packet Duplication for Industrial URLLC
## 1 Introduction
A conservative link adaptation algorithm for URLLC is proposed in [13], where the base station keeps statistics of received CQI reports and calculates the maximum channel quality degradation over a time window. Moreover, in [14] increasing the number of CQI and Modulation Coding Scheme (MCS) values is proposed to reduce the gap between the Signal to Interference plus Noise Ratio (SINR) threshold values and, hence, the radio resource wastage. In [15], two methods for enhancing the efficiency of data duplication over dual connectivity are proposed. In the first method, the duplicates of packets already successfully delivered to the User Equipment (UE) are promptly dropped from the transmission queues of the involved base stations using an uplink indication provided by the UE. In the second method, the duplication is only performed when the primary base station receives a negative acknowledgement (NACK) associated with the transmission. A Deep Reinforcement Learning method was proposed in [16] to decide which secondary legs to use to duplicate and transmit the packet for the UE within the dual connectivity range. The studies above specifically cover Urban Macro (UMa) and Urban Micro (UMi) scenarios, but not the industrial scenario. Although these techniques have shown their effectiveness in reducing latency and packet loss, they come at an additional cost, which derives from a less efficient usage of the network resources. The usage of these techniques does not add any benefit if the baseline network can guarantee the requirements of the services/applications at a given moment. Prediction may allow the base station to decide if a redundant technique is required. This paper proposes a method to predict the End-to-End (E2E) latency as a Service Key Performance Indicator (S-KPI) [17] by observing the network conditions right before a critical transmission in the downlink and, based on that prediction, activate the PD technique (dynamic algorithm) [12; 18] in industrial scenarios. The approach for the prediction model followed in this paper is based on the solution shown in [19], where Radio Access Network (RAN) Key Performance Indicators (KPIs), such as RSRP and Reference Signal Received Quality (RSRQ), are used to predict the S-KPIs using an estimator based on ML. In particular, the authors in [19] applied the estimator for video Key Quality Indicators (KQIs), such as the average buffer size or number of stalls. Nevertheless, the methodology is independent to the final application and ML algorithm used. In this paper, the same concept is adopted for the prediction of the E2E latency S-KPI, assuming that the main contribution of the latency is at the RAN level. As in 5G, it is expected that Mobile Edge Computing (MEC) [20] will play a central role for URLLC, this assumption is justifiable, since the path of the packets will be limited to the RAN. The advantage of this approach is that RAN KPIs can be easily measured by the network terminals and infrastructure elements. In this paper, the precision of the estimator is evaluated. The dynamic PD approach using the predictor was evaluated, comparing the E2E latency performance with single connection and a static PD (that is, always duplicating the packet regardless of network conditions) techniques and the resource consumption when using a static vs. a dynamic PD approach (based on the proposed latency predictor). The remainder of this paper is organized as follows. In Section 2, the materials are described: first, a brief description of cellular networks in Industry 4.0 and critical applications is given in Section 2.1; and in Section 2.2, multi-connectivity in 5G is presented. Then, in Section 3, the proposed system is described. In Section 4, the simulated implementation details are described. The results are shown in Section 5. Finally, our conclusions are drawn in Section 6.