Integrated satellite terrestrial networks and non-orthogonal multiple access (NOMA) have been confirmed to be promising and effective approaches to achieve substantial performance gains for future wireless paradigms. This paper studies the effect of hardware impairments (HIs) on the secrecy performance of NOMA-based integrated satellite multiple-terrestrial relay networks (ISMTRNs). Particularly, we consider two important wiretapping cases: Case I, colluding case: all eavesdroppers cooperatively overhear the information; and Case II, non-colluding case: one eavesdropper with the best wiretapping quality is chosen to overhear the main channel information. Specially, the closed-form expressions for the secrecy outage probability (SOP) of the considered NOMA-based ISMTRNs in the presence of the above two eavesdropping cases and partial relay selection scheme are obtained. To obtain further insights in high signal-to-noise ratios (SNRs) regime, the asymptotic analysis of SOP with two considered cases are also derived, which give efficient means to evaluate the benefit of NOMA scheme and the impacts of HIs on the SOP. Moreover, we obtain Monte Carlo (MC) simulations for the secrecy energy efficiency (SEE).

This work investigates the secrecy issues for the secrecy reconfigurable intelligent surface-based integrated satellite unmanned aerial vehicle (UAV) relay networks with multiple vehicle eavesdroppers. Particularly, the maximal ratio combining (MRC) eavesdropping is utilized to overhear the legitimate signal information. The UAV is applied to forward the legitimate signal to the destination user. Reconfigurable intelligent surface is applied to ensure the transmission. Relied on the proposed secrecy system model and MRC eavesdropping scheme, this paper gets the detailed investigations for the secrecy outage probability (SOP) through the whole signal-to-noise ratios (SNRs) to valuate the effects of important parameters on the SOP. Moreover, to achieve the further impacts of main parameters on the SOP at high SNRs, the approximate analysis for the SOP is also gotten together with the secrecy diversity order (SDO) and secrecy coding gain (SCG). At last, several typical Monte Carlo (MC) results are obtained to present the rightness of obtained theoretical analysis.

In this paper, we investigate the performance of two-way multiple relay non-orthogonal multiple access (NOMA) based networks with hardware impairments (HIs). Especially, opportunistic relay selection scheme is applied to enhance the spectral efficiency along with the spatial diversity. With the increasing of relay’s processing capacity, decode-and-forward (DF) protocol is considered in the networks. Based on the proposed relay selection scheme and NOMA protocol, the closed-form expressions for the outage probability (OP) of the system are derived. In order to study the energy efficiency (EE) of the system, simulation results for EE are also obtained. Furthermore, for the purpose of observing the impacts of HIs’ level, NOMA protocol and the number of relays on the system performance at high signal-to-noise ratios (SNRs), the asymptotic expressions of the OP are also derived. Finally, Monte Carlo (MC) simulation results are presented to verify the correctness of the analytical results.

To address issues such as low detection accuracy and limited real-time performance in road scene detection, a novel road scene detection algorithm based on CMS-YOLO is proposed in this paper. In this algorithm, an efficient backbone called the cross-stage partial DWNeck is devised. By using large-scale depthwise separable convolutions and residual structures, it enables the acquisition of more comprehensive feature information, thereby increasing both the receptive field and the richness of extracted features. Meanwhile, a feature pyramid called the multi-scale fusion feature pyramid network is designed to strengthen the fusion of shallow and deep-level information, effectively preventing the loss of feature information in the transmission process. Besides, a new decoupled head structure called the special decoupled head is introduced, which effectively addresses the conflict between classification and regression tasks through a three-layer joint output structure. Finally, experiments were conducted on two publicly available datasets, namely Udacity Self-Driving and BDD100K. Experimental results indicate that the CMS-YOLO algorithm achieved an impressive detection accuracy of 90.3% and 59.1% in mAP@0.5, demonstrating a remarkable improvement of 4.2% and 7.2% over YOLOv5 respectively. Moreover, in real-world scenarios, the algorithm achieves an impressive real-time detection speed of 34.5 frames per second. These results demonstrate that CMS-YOLO not only meets but also surpasses the requirements for detection accuracy and real-time performance for object detection in autonomous driving scenarios.

In this paper, we investigate the physical layer security of land mobile satellite (LMS) communication networks, where multiple legitimate users and eavesdroppers are considered in the system. In order to obtain the best system performance, we propose the cooperating scheme for legitimate users to receive the main signal. Besides, we design two representative eavesdropping scenes for the eavesdroppers, namely, Scene I, colluding scene: the eavesdroppers cooperate with each other and wiretap the information of main channel together, and the Scene II, non-colluding scene which is the best eavesdropping scene, namely, the most harmful eavesdropper will be selected to overhear the information channel. Furthermore, we obtain the closed-form expressions for the non-zero probability of secrecy capacity (NZPSC), the secrecy outage probability (SOP) and average secrecy capacity (ASC) based on the proposed user cooperation scheme in the presence of two eavesdropping scenes. In order to obtain more insights at high signal-to-noise-ratios (SNRs), the asymptotic expressions for the SOP and ASC are also derived under two scenes, from which we can derive the effect of different parameters on the system performance conveniently. Finally, some representative Monte Carlo (MC) simulation results are provided to verify the correctness of the obtained analytical results.

In this paper, we consider reconfigurable intelligent surface (RIS)-assisted integrated satellite high-altitude platform terrestrial networks (IS-HAP-TNs) that can improve network performance by exploiting the HAP stability and RIS reflection. Specifically, the reflector RIS is installed on the side of HAP to reflect signals from the multiple ground user equipment (UE) to the satellite. To aim at maximizing the system sum rate, we jointly optimize the transmit beamforming matrix at the ground UEs and RIS phase shift matrix. Due to the limitation of the unit modulus of the RIS reflective elements constraint, the combinatorial optimization problem is difficult to tackle effectively by traditional solving methods. Based on this, this paper studies the deep reinforcement learning (DRL) algorithm to achieve online decision making for this joint optimization problem. In addition, it is verified through simulation experiments that the proposed DRL algorithm outperforms the standard scheme in terms of system performance, execution time, and computing speed, making real-time decision making truly feasible.