Despite various advantages of the integration of computational intelligence techniques for industrial Internet-of-Things (IIoT), the timely data collection is one of the main challenges for the application of the artificial intelligence (AI) model. With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a potential solution to the real-time data processing for intelligent transportation systems (ITSs). In this paper, an intelligent transmissive surface is introduced into the vehicular communications, enabling vehicle-to-infrastructure (V2I) signals to penetrate the intelligent RIS to access the base station (BS) on the opposite side of the vehicle. Considering that the vehicle-to-vehicle (V2V) communication reuses the spectrum spanned for V2I link, we investigate the ergodic capacity optimization problem for the vehicle performing V2I communications with the assistance of RIS, while meeting the low-latency and high-reliability requirements of the V2V link. The RIS transmission coefficients and power allocation of vehicles are jointly optimized, for the management of the desired and undesired vehicular communication links. To make this optimization problem trackable, the packet latency constraint is firstly converted into a data rate constraint, following the derivation of the packet loss probability. Then, the expression of optimal phase shifts are derived in a closed-form, which reveals that the performance gain brought by RIS is proportional to the number of intelligent elements, while inversely proportional to the distance from vehicle-to-BS, in a quadratic form. Moreover, in the case of discrete phase shifts, an intelligent algorithm is proposed for the beamforming design at RIS. Afterwards, with the objective to maximize the ergodic capacity of the V2I link, the optimal power allocation is also proposed. Simulation results confirm the accuracy of the proposed resource allocation strategy, and that the system performance in terms of the ergodic V2I capacity can be significantly improved by the RIS, ensuring the low-latency and high-reliability requirements of V2V communications.

Inspired by the promising potentialof reconfigurable intelligent surface (RIS)-based transmission, this paper investigates the RIS-aided vehicle-to-everything (V2X) communication system with the social trust, where multiple vehicle-to-infrastructure (V2I) links share the spectrum with vehicle-to-vehicle (V2V) links. Subject to the reliability constraint of V2V links, the sum V2I link capacity is maximized, through jointly optimizing the power allocation, RIS reflection coefficients and spectrum allocation. The formulated mix-integer and non-convex problem is effectively converted into three subproblems, which can be iteratively solved by our proposed three-stage joint resource allocation and RIS optimization (JRARO) algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm, and that the RIS can significantly improve the quality of vehicular communications in terms of the sum V2I capacity.

This paper considers a cooperative device-to-device (D2D) system with non-orthogonal multiple access (NOMA). We assume that the base station (BS) can simultaneously communicate with all users to satisfy the full information transmission requirement. In order to characterize the impact of the weak channel and different decoding schemes, two novel decoding strategies are introduced: single signal decoding scheme and maximum ratio combining (MRC) decoding scheme, respectively. With the single signal decoding scheme, the users decode the received signals immediately after the receptions from the BS. On the other hand, the MRC decoding scheme jointly decodes the received signals via MRC until the corresponding phase comes and the users jointly decode the received signals by employing MRC. Considering Rayleigh fading channels, the ergodic sum-rate (SR), outage probability and outage capacity of the proposed D2D-NOMA system are analyzed. Moreover, approximate expressions for the ergodic SR are also provided with a negligible performance loss. Numerical results demonstrate that the ergodic SR and outage probability of the proposed D2D-NOMA scheme overwhelm that of the conventional NOMA schemes. Furthermore, it is also revealed that the system performance including the ergodic SR and outage probability are limited by the weak channel for both the single signal decoding scheme and conventional NOMA schemes, but not for the MRC decoding scheme.

To overcome the inherent defect of centralized data processing in cloud computing, the mobile edge computing (MEC) brings data storage and computing capacities, to the edge closer to end users. However, the uneven distribution of access vehicles, as well the volume of computing data, cause the workload diversity among various mobile edge computing servers (MECSs). In this paper, we propose a hierarchical model with quality of service (QoS)-aware and power-aware resource management for the cooperative edge-computing-based intelligent vehicular network (CEC-IoV), and the system latency and energy efficiency at MECSs are respectively optimized. Specifically, considering the changing response times versus MECSs’ workloads, the Minimum Latency with Migration Loads (MLML) scheme is developed for workload balance among multiple MECSs. By selecting the appropriate response time threshold and migration loads from overloading MECSs to idle MECSs simultaneously, the load-balancing problem can be efficiently solved for multiple MECSs with unbalanced workloads. On the other hand, through performing workload redistribution and dynamic reconfiguration of virtual machines (VMs) instantiated onto the parallel computing platform at one MECS, the energy-efficiency can be also optimized while guaranteeing the QoS requirement on the processing delay. With the latency constraint, the power minimization problem is formulated to be a convex one, and the semi-closed forms for optimal solutions of VMs’ workloads and processing rates are provided using KKT conditions. Compared with the performance obtained by benchmark schemes, numerical results exhibit that our resource management schemes gain lower system latency and higher energy efficiency.

This paper proposes a new artificial noise (AN) scheme that combines with the relay selection techniques to improve the physical layer security for a wiretap full-duplex decode-and-forward cooperative network. The communication consists of two phases. In the first phase, the source transmits a message to the selected full-duplex relay, which simultaneously injects AN to mitigate eavesdropping attacks. In the second phase, the selected relay decodes the source message, XORs the source message and AN, and then broadcasts the mixed-signal to the destination. Different form existing schemes, the proposed AN scheme makes the eavesdropper fail in obtaining any information about the source message directly in the second phase. We take into account the correlation of the channel coefficients for the same link during different phases in the performance analysis of AN-aided max-min (MM) and partial (Par) relay selection schemes, and characterize the security-reliability trade-off (SRT) of the proposed schemes by the closed-form expressions of outage probability and intercept probability. Numerical results show that the proposed AN-aided MM/Par relay selection achieves a better SRT performance than the corresponding full-duplex mode assisted AN-aided relay selection scheme in existing literature. Furthermore, the MM scheme outperforms the Par scheme in terms of the SRT, and the SRT of the MM scheme can be substantially improved by increasing the number of relays, while the improvement of the Par scheme is limited.

With deployment of 5G and emerging 6G networks, the demand of wireless communication for transmission capacity as well as the transmission security in consumer electronics is increasing. Reconfigurable intelligent surface (RIS) has shown a great potential in physical layer security (PLS) due to its enabling technology to engineer the radio signal prorogation. In this paper, we study PLS problems of RIS-assisted wireless communication system, where the RIS is deployed to aid pair of the transmitter (Alice) and receiver (Bob) under the active jamming (Jammer) and eavesdropper (Eve) attack. Different from traditional RIS schemes, our proposed scheme divides the RIS into two parts which not only enhances the legitimate user performance but also suppresses the eavesdropper. Specifically, the location of the jammer is also considered with two different scenarios, i.e., the distance between the jammer and RIS is smaller/greater than that of the one between the transmitter and RIS. Considering the influence of RIS reflection coefficient, path loss and uncertain jammer locations, we analyze the system performance based on the legitimate and eavesdropping channels, providing the upper and lower bounds of achievable received signal-to-noise ratio (SNR) instead of the secrecy outage probability (SOP). In addition, the simulation and asymptotic results reveal the location influence of the interference on the secrecy performance and the superiority of the proposed scheme.