# Predictive Network Control in Multi-Connectivity Mobility for URLLC Services

## TL;DR

This paper introduces a centralized predictive flow controller leveraging channel and buffer state information to optimize multi-connectivity in URLLC services, enhancing throughput and reliability in 5G scenarios.

## Contribution

It extends CSI availability to a PDCP controller and develops a novel mathematical model for finite trajectory optimization using a linear program.

## Key findings

- Performance improvements in multi-layer small cell mobility scenarios
- Enhanced end-to-end throughput with the proposed controller
- Validated results through 5G NR system level simulations

## Abstract

This paper proposes a centralized predictive flow controller to handle multi-connectivity for ultra-reliable low latency communication (URLLC) services. The prediction is based on channel state information (CSI) and buffer state reports from the system nodes. For this, we extend CSI availability to a packet data convergence protocol (PDCP) controller. The controller captures CSI in a discrete time Markov chain (DTMC). The DTMC is used to predict forwarding decisions over a finite time horizon. The novel mathematical model optimizes over finite trajectories based on a linear program. The results show performance improvements in a multi-layer small cell mobility scenario in terms of end-to-end (E2E) throughput. Furthermore, 5G new radio (NR) complaint system level simulations (SLS) and results are shown for dual connectivity as well as for the general multi-connectivity case.