| dc.description.abstract | With the rapid development of mobile communications, the 3rd Generation Partnership Project
(3GPP) in Release 17 specification has aimed to accommodate numerous User Equipments (UE) in
5G New Radio (NR) network. Considering the future mobile communication systems (beyond 5G or
6G), an even larger number of devices should be supported. Furthermore, the transmission behavior
is gradually shifting from downlink-dominant (DL dominant) to uplink-dominant (UL dominant).
This shift necessitates more frequency resources, namely bandwidth (BW), for UL data transmission.
Higher frequency bands, such as FR2, can fulfill the bandwidth requirements. However, the
characteristics of higher frequency bands require larger Sub-Carrier Spacing (SCS) to counteract
phase noise (PN). Increasing the SCS also leads to shorter slot duration, which further shortens the
computational time budget of scheduler. If the base station (gNB) fails to accomplish scheduling in
time, it will result in resource waste consequently.
For uplink transmission, this study proposes a novel uplink scheduler named the Deep
Reinforcement Learning-based Lightweight Scheduler (DRL-LS). The DRL-LS determines the
priority of UL data in the queue by its characteristic, such as the Remaining Packet Delay Budget (RPDB)
and the Quality of Service (QoS). Due to the limited time budget for scheduling, the scheduler
only schedules data of high priority in a contention-free manner. For those unscheduled UL data, a
contention-based (CB) UL transmission protocol is proposed, allowing eligible uplink data (i.e., less
urgent data) to access CB resources in a contention manner. Data packets that experience contention
failure will be retransmitted in subsequent UL resources. To meet the packet delay budget, the priority
of queueing UL data increases over time. Additionally, the DRL-LS would learn how to interact with
the environment to achieve a balance between the maximum throughput and media access protocol.
Finally, to validate the performance of the proposed DRL-LS, this study deploys the agent in the
RAN Intelligence Controller (RIC), which is built over the open platform including the
OpenAirInterface5G (OAI) and Mosaic5G FlexRIC open source software. | en_US |