Study of Reinforcement Learning for NB-IoT Random Access and Resource Allocation

• Main Authors: Ji-Zheng You, 游基正
• Other Authors: Yen-Wen Chen
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/95yjq7

碩士 === 國立中央大學 === 通訊工程學系 === 107 === 3rd Generation Partnership Project (3GPP) proposed NarrowBand Internet of Things (NB-IoT) based on Long Term Evolution (LTE) for IoT application in Release 13. It modifies and simplifies the LTE specification to let it be compatible with IoT devices and can coexist with existing LTE systems. In Release 14, the random access procedure can be supported in non-anchor carriers, which alleviate the problem that network congestion may occurs if UE can only random access via anchor carrier. The non-anchor carriers in uplink are used for data transmission. However, if eNB schedules non-anchor carrier for Narrowband Physical Random Access Channel (NPRACH) then it also compresses the Narrowband Physical Uplink Shared Channel (NPUSCH) resource. When NPRACH resource is insufficient, which leads to network congestion, UEs might not be able to complete radio resource control (RRC) connection. So how to configure non-anchor carriers to support random access procedure without causing waste of resources is an importance issue. In this thesis, we propose a Prediction based Random Access Resource Allocation scheme (PRARA), which firstly predicts the number of required resources based on reinforcement learning, and secondly, dynamically allocates the number of secondary contention resources according to the number of collided subcarriers. We aim to increase the performance and resource efficiency of random access in condition of limited resource.