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An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things

机译:基于智能负载控制的随机访问方案,用于基于空间的东西

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摘要

Random access is one of the most competitive multiple access schemes for future space-based Internet of Things (S-IoT) due to its support for massive connections and grant-free transmission, as well as its ease of implementation. However, firstly, existing random access schemes are highly sensitive to load: once the load exceeds a certain critical value, the throughput will drop sharply due to the increased probability of data collision. Moreover, due to variable satellite coverage and bursty traffic, the network load of S-IoT changes dynamically; therefore, when existing random access schemes are applied directly to the S-IoT environment, the actual throughput is far below the theoretical maximum. Accordingly, this paper proposes an intelligent load control-based random access scheme based on CRDSA++, which is an enhanced version of the contention resolution diversity slotted ALOHA (CRDSA) and extends the CRDSA concept to more than two replicas. The proposed scheme is dubbed load control-based three-replica contention resolution diversity slotted ALOHA (LC-CRDSA3). LC-CRDSA3 actively controls network load. When the load threatens to exceed the critical value, only certain nodes are allowed to send data, and the load is controlled to be near the critical value, thereby effectively improving the throughput. In order to accurately carry out load control, we innovatively propose a maximum likelihood estimation (MLE)-based load estimation algorithm, which estimates the load value of each received frame by making full use of the number of time slots in different states. On this basis, LC-CRDSA3 adopts computational intelligence-based time series forecasting technology to predict the load values of future frames using the historical load values. We evaluated the performance of LC-CRDSA3 through a series of simulation experiments and compared it with CRDSA++. Our experimental results demonstrate that in S-IoT contexts where the load changes dynamically, LC-CRDSA3 can obtain network throughput that is close to the theoretical maximum across a wide load range through accurate load control.
机译:由于支持大规模连接和赠款传输以及其易于实现,随机访问是未来基于空间的内容(S-IOT)的最具竞争力的多种访问方案之一,以及其易于实现。但是,首先,现有随机接入方案对加载非常敏感:一旦负载超过某个临界值,由于数据冲突的增加率增加,吞吐量将急剧下降。此外,由于变量卫星覆盖范围和突发流量,S-IOT的网络负载动态变化;因此,当现有随机接入方案直接应用于S-IOT环境时,实际吞吐量远远低于理论最大值。因此,本文提出了一种基于CRDSA ++的基于智能负载控制的随机接入方案,其是争用分辨率分集开槽Aloha(CRDSA)的增强版本,并将CRDSA概念扩展到多于两个复制品。所提出的方案是被称为基于载荷控制的三拷贝竞争分辨率分集分子开槽Aloha(LC-CRDSA3)。 LC-CRDSA3积极控制网络负载。当负载威胁要超过临界值时,只允许某些节点发送数据,并且控制负载靠近临界值,从而有效地提高吞吐量。为了准确地执行负载控制,我们创新地提出了最大似然估计(MLE)基础的负载估计算法,其通过充分利用不同状态的时隙数来估计每个接收帧的负载值。在此基础上,LC-CRDSA3采用基于计算智能的时间序列预测技术来预测使用历史负载值来预测未来帧的负载值。我们通过一系列仿真实验评估了LC-CRDS3的性能,并将其与CRDSA ++进行了比较。我们的实验结果表明,在载荷动态变化的S-IOT背景下,LC-CRDS3可以通过精确的负载控制获得跨越宽负载范围靠近理论最大值的网络吞吐量。

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