Coexistence of eMBB and URLLC in Open Radio Access Networks: A Distributed Learning Framework
- Resource Type
- Conference
- Authors
- Alsenwi, Madyan; Lagunas, Eva; Chatzinotas, Symeon
- Source
- GLOBECOM 2022 - 2022 IEEE Global Communications Conference Global Communications Conference(48099), GLOBECOM 2022 - 2022 IEEE. :4601-4606 Dec, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Engineering Profession
General Topics for Engineers
Power, Energy and Industry Applications
Signal Processing and Analysis
Computer aided instruction
Distance learning
Simulation
Wireless networks
Quality of service
Ultra reliable low latency communication
Resource management
O-RAN
distributed learning
DRL
network slicing
eMBB
URLLC
5G NR
- Language
This paper proposes a distributed learning framework for network slicing in multi-cell open radio access networks providing two services: Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). In particular, a resource allocation optimization problem is formulated with an objective to maximize the average eMBB data rate while considering URLLC constraints and the data rate variance among eMBB users. A multi-agent Deep Reinforcement Learning (DRL) based algorithm is developed to solve the formulated problem, where network components collaboratively train a global machine learning model and then share learning parameters for distributed executions at network edges. Specifically, DRL agents are installed at Near-Real-Time Radio access network Intelligent Controllers (Near-RT RICs) located in the network edge servers to provide online resource allocation decisions while the training process is performed offline at the Non-Real-Time RIC (Non-RT RIC) located in a regional cloud server. The achieved simulation results show that the proposed algorithm can ensure the required URLLC reliability while keeping the Quality-of-Service (QoS) requirements of the eMBB service.