On enabling 5G dynamic TDD by leveraging deep reinforcement learning and O-RAN

Dynamic Time Duplex Division (D-TDD) is a promising solution to accommodate the new emerging 5G and 6G services characterised by asymmetric and dynamic Uplink

(UL) and Downlink (DL) traffic demands. D-TDD dynamically changes the TDD configuration of a cell without interrupting users’ connectivity, hence balancing the bandwidth for UL or DL communication according to the traffic pattern. However,

3GPP standard does not specify algorithms or solutions to derive the TDD configuration, i.e., the number of slots to dedicate to UL and DL. In [1], we have proposed a Machine Learning (ML)-based solution relaying on Deep Reinforcement Learning (DRL) to allow the base station (or gNB) to self-adapt to the traffic pattern of the cell by periodically adapting the number of slots dedicated to UL and DL. In this work, we implemented the DRL algorithm on top of an open-source gNB based on

OpenAirInterface (OAI) [2] to demonstrate its efficiency. To this end, we relied on the O-RAN architecture [3], where the proposed DRL algorithm is deployed as xApp at the Near Real-time RAN Intelligent Controller (RIC) and communicates with the base

station using O-RAN E2 interface. We developed xTDD Service Model (SM) following the E2SM standard [3], allowing the DRL solution to monitor DL and UL buffers from the gNB to deduce the optimal TDD configuration that accommodates the current traffic. Then, the decision (i.e., TDD configuration) is pushed to the base station. We implemented the solution on top of the OAI 5G StandAlone (SA) platform and Flexric RIC [4]. To the best of our knowledge, this is the first demonstration of a ML-based D-TDD on top of a real 5G network, showing the advantage of O-RAN architecture to building Self Organized Network (SON) function for dynamic configuration of D-TDD.