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

中文翻译：

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大规模机器类型通信的基于动态前导码选择的分层分组随机接入方案

大规模机器类型通信（mMTC）已被确定为超越5G网络和未来大规模物联网（IoT）的重要用例。但是，对于mMTC中的大规模多路访问，如果采用常规的4步随机访问（RA）方案，则存在严重的访问前导冲突问题。因此，提出了一系列无赠款（GF）RA方案。然而，如果蜂窝用户（设备）的数量显着增加，由于所需的前导码要长得多，现有GF方案的能量和频谱效率仍将迅速下降。为了克服这一难题，提出了一种分层分组策略，其中首先根据蜂窝用户的地理位置将其分为多个集群，然后使用基于最佳能耗（Opt-EC）的K-means算法，同一集群的用户可以自动加入不同的组。通过这种新的分层蜂窝体系结构，RA过程分为集群负载估计阶段和活动组检测阶段。基于近似消息传递算法的状态演化理论，推导了最小前同步码长度的严格下限，以达到一定的检测精度。得益于集群负载估计，第二阶段将调用动态前导码选择（DPS）策略，从而以最小的长度生成所需的前导码。正如我们的仿真结果所证明的那样，这种两阶段DPS辅助的RA策略可显着改善性能 通过这种新的分层蜂窝体系结构，RA过程分为集群负载估计阶段和活动组检测阶段。基于近似消息传递算法的状态演化理论，推导了最小前同步码长度的严格下限，以达到一定的检测精度。得益于集群负载估计，第二阶段将调用动态前导码选择（DPS）策略，从而以最小的长度生成所需的前导码。正如我们的仿真结果所证明的那样，这种两阶段DPS辅助的RA策略可显着改善性能 通过这种新的分层蜂窝体系结构，RA过程分为集群负载估计阶段和活动组检测阶段。基于近似消息传递算法的状态演化理论，推导了最小前同步码长度的严格下限，以达到一定的检测精度。得益于集群负载估计，第二阶段将调用动态前导码选择（DPS）策略，从而以最小的长度生成所需的前导码。正如我们的仿真结果所证明的那样，这种两阶段DPS辅助的RA策略可显着改善性能 得出最小前同步码长度的严格下限，以实现一定的检测精度。得益于集群负载估计，第二阶段将调用动态前导码选择（DPS）策略，从而以最小的长度生成所需的前导码。正如我们的仿真结果所证明的那样，这种两阶段DPS辅助的RA策略可显着改善性能 得出最小前同步码长度的严格下限，以实现一定的检测精度。得益于集群负载估计，第二阶段将调用动态前导码选择（DPS）策略，从而以最小的长度生成所需的前导码。正如我们的仿真结果所证明的那样，这种两阶段DPS辅助的RA策略可显着改善性能