A layered grouping random access scheme based on dynamic preamble selection for massive machine type communications

Massive machine type communication (mMTC) has been identified as an important use case in Beyond 5G networks and future massive Internet of Things (IoT). However, for the multiple access mMTC, there is a serious preamble collision problem if conventional 4-step random (RA) scheme employed. Consequently, range grantfree (GF) RA schemes were proposed. Nevertheless, number cellular users (devices) significantly increases, both energy spectrum efficiency existing GF still rapidly degrade owing to much longer preambles required. In order overcome this dilemma, layered grouping strategy proposed, where are firstly divided into clusters based on their geographical locations, then same cluster autonomously join different groups by using optimum consumption (Opt-EC) K-means algorithm. With new architecture, process load estimation phase active group detection phase. Based state evolution theory approximated message passing algorithm, tight lower bound minimum length achieving certain accuracy derived. Benefiting from estimation, dynamic selection (DPS) invoked second phase, resulting required with length. As evidenced our simulation results, two-phase DPS aided results significant performance improvement