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In 5G Radio Access Networks (RAN), network slicing is a crucial technology for offering a variety of services. Inter-slice resource allocation is important for dynamic service requirements. In order to implement inter-slice bandwidth resource allocation at a large time scale, we used the Multi-Agent deep reinforcement learning (DRL) Asynchronous Advantage Actor Critic (A3C) algorithm with a focus on maximizing the utility function of slices. In addition, we used the K-means algorithm to categorize users for beam learning. We used the proportional fair (PF) scheduling technique to allocate physical resource blocks (PRBs) within slices at a small time scale. The results show that the A3C algorithm has a very fast convergence speed for utility function and packet drop rate. It is superior to alternative approaches, and simulation results support the proposed approach.

Security and privacy are vital challenges in Industrial Internet of Things (IIoT) systems due to the inherent broadcast property of industrial wireless communication. Therefore, how to improve the security of smart devices in IIoT systems has attracted widespread attention. In this article, a novel security optimization scheme for space shift keying (SSK)-based IIoT systems is proposed by applying secrecy enhancing techniques in the physical layer. Specifically, two methods are adopted in the proposed optimization scheme. First, the sender transmits artificial noise (AN) to interfere with the eavesdropper, while the legitimate receiver can eliminate AN. Second, the sender can select optimal antennas based on the proposed minmax criteria to transmit AN and information, which further promotes the security of industrial wireless communication. The optimal antenna selection pattern increases the difference of the average mutual information between the legitimate receiver and the eavesdropper. In addition, the proposed minmax antenna selection approach only requires the channel state information of the legitimate receiver, which satisfies the application scenario of unknown eavesdropper attacks in IIoT. Finally, we adopt a deep learning-based maximum likelihood (ML) detection algorithm to detect the optimal antenna index and analyze the bit error rate (BER) and secrecy rate (SR) of the proposed optimization scheme. Simulation results show that the proposed scheme can achieve similar SR improvements and superior BER performance compared with the state-of-the-art scheme, namely the signal-leakage-to-noise ratio (SLNR) scheme. The BER performance of the proposed scheme achieves a maximum of 100 times superior to that of the SLNR scheme when the signal-to-noise ratio (SNR) is 27 dB. Moreover, the proposed scheme outperforms the traditional antenna selection scheme (random selection) in terms of BER and SR.