Device-to-device (D2D) communication can effectively meet the demanding high data rate by providing direct links among mobile users in cellular networks. In this paper, we analyze the transmission capacity of relay-assisted D2D communication coexisting with cellular networks in both overlay and underlay modes. D2D users can use the delicate spectrum resources in the overlay mode, while reuse the cellular resources in the underlay mode. Based on stochastic geometry, cellular users, D2D transmitters, and relay nodes (RNs) in the networks are all modeled as Poisson point process. Then, we calculate the RN existence probability and the expectation of relay link distance to obtain the successful transmission probabilities for D2D communication. According to two relay mechanisms for enhancing D2D transmission distance, we further obtain the transmission capacities of D2D communication with the assistance of RNs in both two modes, which reflect the influence from D2D density and power. In addition, the D2D transmission capacities with variable D2D link distance are also analyzed in two modes. Simulation results verify that D2D transmission capacity can be enhanced by relay transmission and influenced by a multitude of factors, including the user density, power, the D2D link distance, and the way of using RNs.

Rate-splitting multiple access (RSMA) softly reconciles and decodes the extreme interference by non-orthogonal transmission, which can remarkably solve the spectrum scarcity for future six-generation (6G) low earth orbits (LEO) satellite communication system. In this letter, we investigate the power allocation problem in LEO satellite networks with RSMA mechanism based on the deep reinforcement learning (DRL) technique. Specifically, in order to achieve better RSMA performance, the LEO satellite base station (SBS) has to effectively allocate transmit power to common and private streams, which is very challenging due to the uncertain and limited information of the channel distribution. To solve this problem, a highly-effective proximal policy optimization (PPO) based scheme is further proposed, which enables the LEO SBS to learn an optimal power allocation strategy to maximize the sum rate of the system without knowing any prior information. Simulation results prove that the proposed scheme significantly outperforms the other three baseline schemes in terms of the sum rate metric with low computation complexity.

This paper considers the joint optimization of resource allocation and power control for rate-splitting multiple access (RSMA) based low earth orbits (LEO) satellite-terrestrial networks, where resource sharing between terrestrial and LEO satellite communications is optimized and the LEO satellite serves multiple ground stations (GSs) simultaneously through RSMA technique. Particularly, to make full use of RSMA technique, the LEO satellite needs to appropriately schedule transmitting power to common and private streams. Therefore, the key issue is to jointly optimize the resource allocation and power control to fully utilize the benefits of resource sharing and RSMA. However, the combination of continuous power control and discrete resource allocation becomes the bottleneck for providing an effective solution with limited system information. To deal with this problem, we propose a deep reinforcement learning (DRL)-based framework which jointly employs deep Q-network (DQN) algorithm for discrete resource allocation and proximal policy optimization (PPO) algorithm for continuous power control to maximize a joint objective. Simulation experiments evaluate the performance of the proposed scheme compared with several baseline schemes and the results show the advantages of the proposed scheme.

The continuous development of network technology has driven the emergence of smart devices, and the demand for smart devices interconnection has increased sharply, which requires the identity of devices to be authenticated to carry out secure communication. The traditional certificate-based identity authentication scheme can no longer meet the authentication requirements of massive devices. As an authority that issues and manages certificates, Certificate Authority (CA) creates data islands of intra-domain certificates, increasing the complexity of cross-domain authentication. In order to improve the efficiency of cross-domain authentication, this paper introduces blockchain technology, which can establish trust in an untrusted environment. We propose a multi-CA-based authentication architecture to establish distributed trust and share cross-domain certificate information among multiple domains. On this basis, we design a simplified identity authentication scheme to quickly complete cross-domain identity authentication and reduce authentication overhead. To further improve the efficiency of cross-domain authentication, a cross-domain certificate revocation mechanism is designed. The scheme has passed the formal security analysis, and the simulation results show that the cross-domain authentication scheme is efficient.

Device-to-device (D2D) communications are envisioned as a critical technology to support future ubiquitous mobile communications applications. However, the requirements of high mobility and low latency seriously constrain the performance improvement of D2D communications. In this letter, we investigate a deep reinforcement learning (DRL) based scheme of power and resource allocation for maximizing the throughput of D2D users (DUEs) and cellular users (CUEs). Based on DRL theory, D2D pairs are defined as distributed multiple agents, which aim to adaptively select the transmission power and resources to ease the co-channel interference without any prior information. Furthermore, a priority sampling based dueling double deep Q-network (PS-D3QN) distributed algorithm is proposed to help agents to learn the predominant features. Simulation results show that the proposed algorithm has higher throughput than the existing DRL algorithms with a strict delay constraint. Particularly, the probability that the agent selects high power decreases with the increase of remaining transmission time, which proves the agents effectively learn and dynamically sense the impacts of the delay constraint.

The rapid development of digital technology is reshaping the architecture of the Industrial Internet of Things (IIoT). The traditional architecture cannot process vast amounts of data exchanges and provide entities with trust. The future IIoT is expected to be a decentralized architecture in which blockchain and digital twin-driven IIoT can enable trusted data exchanges. However, this architecture cannot obtain huge amounts of external real-time data and isolated data. Moreover, it cannot handle complex industrial computing tasks. Therefore, we combine oracle with decentralized learning to propose a novel IIoT-oriented digital twin architecture. We also propose an effective decentralized collaboration mechanism to support external data and resources exchanges. Moreover, we propose a novel computing collaboration mechanism to expand the learning capabilities of the industrial ecology. Experiments show that our proposed paradigm has less processing time, a more stable process, and better learning ability compared to other paradigms.