Safety-related V2X communication plays an essential role in road safety and requires stringent high reliability and low latency. In this paper, we propose a novel approach of joint power control and resource allocation mode selection to solve the resource allocation problem of the mixed centralized/distributed V2X communication system under different network load conditions. Two major resource allocation modes are considered for V2X communication: scheduled resource allocation (centralized system) and UE autonomous resource selection mode (distributed system). In order to meet the specific Quality of Service (QoS) related demands of safety-related V2X communication, we aim to maximize overall information value of V2X communication by considering both ProSe Per-Packet Priority (PPPP) and communication link quality of V2X messages while meeting the requirements of minimum SINR and maximum transmit power limitation for both pedestrian user equipments (PUEs) and vehicular user equipments (VUEs). In particular, in scenarios where the network is lightly loaded we propose Vacant Resource Blocks and Power Allocation (VRBPA) algorithm while for scenarios wherein the network is heavily loaded we propose Occupied Resource Blocks and Power Allocation (ORBPA) algorithm. Furthermore, the proposed algorithm can achieve a stable matching between VUEs and their reuse PUEs partners. Extensive simulation results show that both the information value of VUEs as well as the spectral efficiency of PUEs can be improved efficiently by the proposed approach.

The integrated sensing and communication (ISAC) endows the next generation of wireless networks with the new paradigm to the security communication. In this paper, the dual-functional radar-communication (DFRC) with the property of spectral and energy efficiency is considered to strengthen the physical layer security (PLS) under the channel state information (CSI) uncertainty of the eavesdropper, where satisfies the tolerate sensing requirement and the minimum secrecy rate for each legitimate user. Specifically, we propose the PLS optimization model which employs the Cramér-Rao bound (CRB) as the sensing metric, and characterize the sensing-communication trade-off under the PLS enhancement. The efficiency closed-form solution composed of the generalized eigenvector and the power budge is derived for the case of single legitimate user with the perfect CSI of the eavesdropper. Considering the general case of multiple users, we first propose the novel sensing-specific imperfect CSI model to construct the inherent integration between the sensing capacity and the norm bound of the imperfect CSI. Further, the infinite uncertainty is handled by the S-procedure, and we propose the block coordinate descent (BCD) iterative algorithm to solve the PLS optimization problem comprising with the semidefinite relaxation (SDR) and the successive convex approximation (SCA) to address the non-convexity. Simulation results demonstrate the performance gain and the effectiveness of the proposed algorithm in the average secrecy rate over other baseline schemes.

In disaster response, 2D wireless sensor networks fail to be deployed since a large number of sensors cannot be placed in harsh ground environments. In this case, as the best air carrier for sensors, unmanned aerial vehicles (UAVs) are always used to construct a 3D flying ad hoc network (FANET) to realize emergency communication. In FANET, however, node localization and data transmission are great challenges due to the increase in network dimensions and the high mobility of UAVs. In this paper, a novel FANET clustering routing protocol based on nodes location is proposed. Firstly, an improved particle swarm optimization (IPSO) algorithm is designed by fusing variable neighborhood search, and IPSO is used to improve the locating precision of UAVs. Furthermore, the base station (BS) clusters UAVs evenly according to nodes location, and the cluster head is determined by weighting the intra-cluster distance and residual energy of nodes. Finally, the optimal next-hop routing is selected by IPSO according to the link reliability and the distance between nodes and the BS to ensure data are transmitted to the BS timely and reliable. The simulation results show that routing protocol based on IPSO realize more efficient performance compared with the existing schemes in terms of positioning indicators, energy consumption, delay, and clustering lifetime.

Excellent performance has been demonstrated by convolutional neural network (CNN) in salient object detection for optical remote sensing images (ORSI-SOD). However, the limitations of CNN's feature extraction using sliding window approach hinder the capture of global representations. Therefore, an end-to-end detection model, known as adaptive dual-stream sparse transformer network (ADSTNet), has been proposed for ORSI-SOD and is assisted by the vision transformer. It effectively addresses the compensation issue of global and local information in ORSI-SOD. In particular, an adaptive interaction encoder has been devised, amalgamating the multi-scale sparse transformer (MST) and the pyramid atrous attention (PAA) to constitute the adaptive dual-stream sparse encoder (ADSE). This encoder collaborates with the CNN to enhance longrange dependency modeling and preserve global information more effectively base on local features. Additionally, a directional feature reconfiguration (DFR) is constructed to extract texture details from multiple directional dimensions. Finally, we propose the adaptive feature cascade decoder (AFCD) that synthesizes content information from the foreground, edges, and background to enhance the representational capacity of the image. Furthermore, a structural loss function, known as the weight compensation mechanism, is introduced to balance the performance of boundary and salmap segmentation losses. The proposed model has been demonstrated to outperform 26 stateof-the-art (SOTA) ORSI-SOD methods across eight evaluation metrics on two standard datasets, as evidenced by extensive experiments. Furthermore, to verify its robustness, the generalization performance of the model on the latest challenging ORSI4199 dataset is reported. The code and results for this work can be found at https://github.com/JieZzzoo/ADSTNet

In this paper, we proposed a joint user pairing (UP) and power allocation (PA) algorithm in the non-orthogonal multiple access uplink communication systems, aiming at improving the proportional fairness of the users. We first solve the optimization problem in a basic scenario, where the users are distributed in only one base station (BS), which is broadly used in many papers. Subsequently, the algorithm is further extended into a complex scenario that the interfering users are allocated randomly outside the BS by spatial homogeneous Poisson point process (HPPP). The joint UP and PA is an NP-hard problem in both scenarios. To solve the problem efficiently, we decouple the UP and PA part. In the PA part of the basic scenario, according to our analysis, the user pair can be divided into three kinds according to the different relationship between the channel condition and the signal to noise and interference constraint. The different kind of user pair's near optimal PA solution is found in different ways. In the UP part of the basic scenario, a probability-based Tabu search user-pairing algorithm is provided to find the near optimal user pairing solution. While, in the PA part of the complex scenario, the optimization problem is extended into a stochastic programming problem aiming at enhancing proportional fairness in users with the outage rate constraint. We derive the closed form expression of outage rate and average data rate for each possible user pairs according to HPPP. Then, a prediction-based particle swarm optimization algorithm is proposed to solve the stochastic programming problem. The simulation results show that the proposed algorithm provides better proportional fairness comparing to previous algorithms.