The line-of-sight multiple-input multiple-output (LOS-MIMO) has emerged as a potential solution for increasing spectral efficiency in dense urban microwave links, given limited spectrum resources. Phase variation and power attenuation may introduce uncertainty to the channel estimation, affecting channel capacity and the performance of interference cancellation by zero-forcing receivers. This paper investigates the impact of this uncertainty in the presence of rain. Commercial backhaul links (CMLs) in cellular networks are not only essential for data transmission but have also proven useful for rainfall monitoring. They are now emerging as a new opportunistic integrated sensing and communication (OISAC) application for weather sensing. This paper also studies the use of LOS-MIMO backhaul technology for rain rate estimation. The availability of multiple data streams allows the number of rain estimation values to increase linearly with the minimum number of transmit and receive antennas in the MIMO link, Additionally, the potential for LOS-MIMO microwave links to retrieve parameters related to rain drop size distribution based on measurement data is also explored. This new backhaul solution shows great potential to be used for near-ground environmental monitoring and weather prediction studies, particularly with the advent of the big data era.

Digital twin (DT) migration technology is pivotal for ensuring seamless synchronization between physical entities and their virtual counterparts. However, in complex urban environments, high device mobility, dynamic network topology, and uncertain wireless channel conditions often lead to suboptimal migration decisions. Such inefficiencies exacerbate resource contention and diminish migration timeliness, thereby increasing DT state deviation and compromising service quality. To address these challenges, this paper proposes an AP group-enhanced digital twin edge network model, where multi-antenna access points (APs) are integrated as relay nodes to optimize channel quality and transmission reliability. Building on this, a Group-Collaborative Multi-Agent Proximal Policy Optimization (GC-MAPPO) migration strategy is presented. The strategy formulates the migration problem as a partially observable Markov decision process (POMDP). Specifically, the K-means++ clustering algorithm is first employed to construct optimal AP collaborative groups for mobile devices; subsequently, the MAPPO algorithm is utilized to derive optimal migration policies in dynamic environments. Experimental results demonstrate that, compared to existing baselines, the proposed GC-MAPPO scheme reduces the average synchronization delay by 6.69% to 37.05% and decreases DT state deviation by 29.24% to 82.91%.

The decision fusion rule and global optimality is studied for serial distributed detection in multihop multirelay wireless sensor networks (WSNs) under the end–edge–cloud orchestration. In particular, a multihop relay node serial distributed detection configuration is considered. Then, the optimal decision fusion rule is derived, and the detection probability and false alarm probability of the distributed detection system are given. Third, the suboptimal decision fusion rule under different conditions for the multihop relay channel is represented. Furthermore, in order to solve the high energy consumption and bandwidth limitation problems of WSNs, we consider the global optimization for serial distributed detection systems and obtain the sufficient condition. Finally, under different communication conditions, the detection performance of the system has been kept optimal when we increase the number of sensors in series. We validate the conclusions based on the numerical results. The sufficient condition for optimality detection of serial distributed detection multirelay sensor network system is satisfied then the system can achieve optimal detection performance.

The M-ary distributed decision fusion is studied for multi-hop amplify-and-forward Wireless Sensor Networks (WSNs), the implementation framework is given, and the performance analysis is developed. In particular, we first propose an M-ary distributed decision fusion configuration, wherein the multi-hop relay network is involved, and the relay node only forward information from their neighbors. Furthermore, the optimal decision rule with the explicit and exact form is derived, and the implementation structure is depicted. Our results show that, the Hamming Distance (HD), which is well-known in information theory, is finally involved in the optimal decision metric. Then, we derive the suboptimal decision fusion algorithms for three scenarios. Firstly, we achieve an interesting rule in the form of a Maximum Ratio Combining (MRC), wherein the local channel is considered to be ideal. When the idea of Equal Gain Combining (EGC) is directly followed, we propose the Minimum Hamming Distance Summation (MHDS) rule. We also give a Selective Combining (SC) statistic, wherein only received observation with the optimal quality is selected for decision fusion, and we correspondingly term this criterion as the Minimum Hamming Distance (MHD) rule. Secondly, we achieve a Chair-Varshney rule when it is assumed that the crossover probability of relay Binary Symmetric Channel (BSC) is small enough. The simple majority-based statistic is developed when the homogeneous multi-hop relay WSNs is considered. A statistic with the similar form of SC is also developed. Thirdly, when each relay BSC’s crossover probability is relatively large, we also propose the suboptimum fusion statistic in an analog form to the MRC and EGC, respectively. Our results show that the MHDS rule can be achieved via optimum decision rule in the first or third scenarios. The performance assessment is determined both Monte Carlo simulation and analysis.

Advancements in technology hold significant promise for the future of smart agriculture. Drones, serving as innovative tools for data acquisition, play a pivotal role in this context. Traditional pollination drones, which rely on extensive spraying methods, suffer from poor efficiency. To address this challenge, our focus is on enhancing pollination through object detection technology, leading to the introduction of the YOLO-FT detection algorithm based on YOLOv7. To validate our algorithm, we have curated a dataset of fruit tree flowers. Our approach involves several key components. Firstly, we propose a lightweight adaptive backbone network, which effectively extracts feature information through partial convolution and seamlessly integrates feature data from diverse channels. Secondly, we introduce a non-parametric attention module into neck network, bolstering the fusion of critical feature information. Finally, we leverage a rapid convergence function based on centroid distance to enhance bounding box regression performance. Experimental results demonstrate the superiority of the YOLO-FT algorithm, achieving$mAP_{50}$of 92.8%,$mAP_{50-95}$of 48.8%, and F1 score of 0.90, marking improvements of 3.6%, 1.6%, and 0.17, respectively, over the baseline. Furthermore, YOLO-FT exhibits a significant reduction in model complexity, with parameters decreased by 13.4% and FLOPs by 17.8%. This research serves as a valuable theoretical reference for advancements in smart agriculture.

With the development of vehicle electrification, automation and connectivity, collaborative optimization among the traffic throughput, driving comfort, fuel economy and driving safety targets is still a huge challenging barrier for a connected and automated fuel cell/battery hybrid electric vehicle. Hence, this paper proposes an optimal car-following energy management strategy (EMS) that combines energy management and adaptive cruise control considering the above targets. Specifically, based on vehicle-to-vehicle and vehicle-to-infrastructure information, an optimal following distance algorithm is developed to obtain the optimal following distance considering driving safety, driving comfort and traffic throughput. Then, based on the established vehicle longitudinal dynamics model, an adaptive cruise controller using back-stepping technique is designed to accurately track optimal following distance. Meantime, combining the obtained controller, optimal EMS based on equivalent consumption minimization strategy is proposed to coordinate the output power of fuel cell and battery to improve fuel economy. The simulations of short and long-term driving cycles indicate that the proposed method can reduce hydrogen consumption by 12.12%, jerk by 61.21%, and keep the desired following distance tracking error within 0.5m.

This article studies the confidential transmission performance of an electric vehicle (EV) in heterogeneous network when it communicates with vehicle to grid (V2G). Based on the relay selection strategy that maximizes the signal-to-noise ratio(SNR), the electric vehicle as a legitimate user in this article uses a multi-antenna maximum ratio combining method for signal reception. Among them, a single antenna is configured for the power grid sender, relay nodes and illegal eavesdropping users. The wireless channel adopts Nakagami-m fading channel and the relay adopts decode and forward (DF) method. First, based on the stochastic geometric analysis method, statistical characteristics such as probability density function(PDF) and cumulative distribution function(CDF) of the received SNR are obtained at legitimate users and illegal eavesdropping users, respectively. Then, a functional analysis method is used to derive closed expressions for the secrecy outage probability (SOP) and non-zero security capacity probability in multieavesdropping user systems. Finally, the effects of the system's related parameters on SOP and non-zero security capacity probability are verified through simulations. The simulation results prove the correctness of the theoretical analysis, which can guarantee the privacy and security of electric vehicle users in heterogeneous network.