Exploiting NOMA for Radio Resource Efficient Traffic Steering Use-case in O-RAN

• In this work, we consider the design of a radio resource management (RRM) solution for traffic steering (TS) use-case in the open radio access network (O-RAN). The O-RAN TS deals with the quality-of-service (QoS)-aware steering of the traffic by connectivity management (e.g., device-to-cell association, radio spectrum, and power allocation) for emerging heterogeneous networks (HetNets) in 5G-and-beyond systems. However, TS in HetNets is a complex problem in terms of efficiently assigning/utilizing the radio resources while satisfying the diverse QoS requirements of especially the cell-edge users due to their poor signal-to-interference-plus-noise ratio (SINR). In this respect, we propose an intelligent non-orthogonal multiple access (NOMA)-based RRM technique for a small cell base station (SBS) within a macro gNB. A Q-learning-assisted algorithm is designed to allocate the transmit power and frequency sub-bands at the O-RAN control layer such that interference from macro gNB to SBS devices is minimized while ensuring the QoS of the maximum number of devices. The numerical results show that the proposed method enhances the overall spectral efficiency of the NOMA-based TS use case without adding to the system's complexity or cost compared to traditional HetNet topologies such as co-channel deployments and dedicated channel deployments. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)

Nyckelord

HetNets

Internet-of-things (IoT)

non-orthogonal multiple access (NOMA)

Open RAN (O-RAN)

Q-learning

spectral efficiency

traffic steering

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)