Selected mapping (SLM) is a promising peak-to-average power ratio (PAPR) reduction technique for orthogonal frequency division multiplexing (OFDM) system. In SLM, phase sequences are combined with the data for generating alternative signals, so as to reduce the PAPR. In this paper, a new criterion is developed to examine the effects of different phase sequence sets in SLM-OFDM based on the mathematical correlation analysis among the alternative signals. Furthermore, according to the proposed criteria, the Chaotic phase sequence set is introduced for improving the PAPR reduction performance in SLM. Simulation results show that the improved SLM outperforms conventional methods such as proposed in [6].

The accelerated development of the industrial Internet of Things (IIoT) is catalyzing the digitalization of industrial production to achieve Industry 4.0. In this article, we propose a novel digital twin (DT) empowered IIoT (DTEI) architecture, in which DTs capture the properties of industrial devices for real-time processing and intelligent decision making. To alleviate data transmission burden and privacy leakage, we aim to optimize federated learning (FL) to construct the DTEI model. Specifically, to cope with the heterogeneity of IIoT devices, we develop the DTEI-assisted deep reinforcement learning (DRL) method for the selection process of IIoT devices in FL, especially for selecting IIoT devices with high utility values. Furthermore, we propose an asynchronous FL scheme to address the discrete effects caused by heterogeneous IIoT devices. Experimental results show that our proposed scheme features faster convergence and higher training accuracy compared to the benchmark.

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