Uplink resource management for massive machine-type communications in cellular networks

Abstract

With the advent of the fifth generation (5G) mobile communications systems, main usage scenarios for IMT 2020 and beyond are categorized as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low-latency communications (URLLC). Recently, MTC has attracted great attention since it is expected to play a significant role in realizing Internet-of-Things (IoT) which connects a massive number of machine devices in a wide range of applications, e.g., smart metering, surveillance and security, city automation, intelligent transportation systems, industrial management, and e-health. However, accommodating MTC with the current cellular networks may exhibit some challenges, since the current networks are optimized to service human-to-human (H2H) communications but MTC exhibits different features from H2H communications. Thus, the networks should be optimized in a different way in order to enable MTC to be implemented in practical cellular networks. In this dissertation, we propose efficient uplink resource management schemes for supporting the mMTC scenario in cellular networks. We propose various technologies to efficiently utilize each of uplink radio resources, e.g., a physical random access channel (PRACH), a physical uplink control channel (PUCCH), and a physical uplink shared channel (PUSCH). We mathematically analyze the proposed technologies, and evaluate their comprehensive performances through extensive computer simulations. We verify the superiority of our proposed technologies, and emphasize on the necessity of new approaches for efficiently accommodating the mMTC scenario in the next-generation cellular networks.