The growth of maritime activities has boosted the demand for efficient marine communications and computation offloading. Mobile edge computing (MEC)-powered space-air-ground-sea integrated network (SAGSIN) is a promising solution to satisfy these demands. To the best of our knowledge, the existing research on high altitude platforms (HAPs) in SAGSIN remains open. To exploit the HAPs’ advantages of facilitating communication at shorter distances and more stable computing services than terrestrial base stations, this work considers multi-HAP-assisted computation offloading in SAGSIN and formulates an optimization problem, which turns out to be a mixed integer nonlinear programming (MINLP) due to the joint optimization among task-HAP association, computational resource allocation of HAPs and task-satellite association. To this end, the problem is decoupled into three single-variable subproblems by relaxing the delay constraint, and the subproblems are solved by graph theory, the Lagrange multiplier method with Karush-Kuhn-Tucker (KKT) constraints, and the alternating optimization, respectively. Simulation results demonstrate the efficiency of the proposed scheme with superior performances compared to benchmark schemes.

The proliferation of intelligent connected vehicles and related applications with computation-intensive tasks has boosted a pivotal requirement for the unprecedented low latency and high throughput of the Internet-of-Vehicle (IoV). Rate-splitting multiple access (RSMA) has emerged as a promising technology for achieving high spectral efficiency and maintaining reliable connectivity in mobile scenes. Inspired by the idea of distributed computing, an RSMA-assisted distributed computation offloading scheme (RDCOS) in vehicular networks is designed, where various computation auxiliary nodes (CANs) are introduced in a stochastic geometry approach to alleviate the computation dilemma of the task vehicle. After analyzing the CAN pairing and the successful accessing probability (SAP), the total offloaded tasks maximization problem of the task vehicle is investigated. Then a successive convex approximation (SCA)-based computation offloading optimization algorithm (SCOOA) is developed to obtain the solution. The results of the performance evaluation not only verify the plausibility of the proposed model but also indicate that the RSMA can significantly promote the total offloaded tasks under moving conditions, which confirms the superiority of the proposed scheme through simulations.

With the development of wireless technology, signals propagating in space are easy to mix, so blind detection of communication signals has become a very practical and challenging problem. In this paper, we propose a blind detection method for broadband signals based on a weighted bi-directional feature pyramid network (BiFPN). The method can quickly perform detection and automatic modulation identification (AMC) on time-domain aliased signals in broadband data. Firstly, the method performs a time-frequency analysis on the received signals and extracts the normalized time-frequency images and the corresponding labels by short-time Fourier transform (STFT). Secondly, we build a target detection model based on YOLOv5 for time-domain mixed signals in broadband data and learn the features of the time-frequency distribution image dataset of broadband signals, which achieves the purpose of training the model. The main improvements of the algorithm are as follows: (1) a weighted bi-directional feature pyramid network is used to achieve a simple and fast multi-scale feature fusion approach to improve the detection probability; (2) the Efficient-Intersection over Union (EIOU) loss function is introduced to achieve high accuracy signal detection in a low Signal-Noise Ratio (SNR) environment. Finally, the time-frequency images are detected by an improved deep network model to complete the blind detection of time-domain mixed signals. The simulation results show that the method can effectively detect the continuous and burst signals in the broadband communication signal data and identify their modulation types.

The proliferation of smart vehicles and resource-hungry applications has imposed challenges to on-board systems. By exploiting the clustered vehicles in neighbor-following network (NFN), the multi-hop vehicular network (MHVN) enables cooperative communication among multiple vehicles, making it promising for vehicular edge computing (VEC). Nonetheless, to the best of our knowledge, the joint task offloading and resource allocation strategies in MHVN remains open due to challenges arising from the prevalence of link disruptions, the substantial variability in channel states, and the limited computational resource. With the above considerations, in this work, the task offloading and resource allocation are jointly optimized in the MHVN to minimize the offloading cost and maximize the task completion rate (TCR). However, the optimization problem turns out to be a mixed integer nonlinear programming (MINLP) problem. To this end, the problem is decoupled into two subproblems, which are solved by graph theory and improved water-filling algorithm, respectively. Furthermore, an iterative optimization algorithm is designed to mitigate the impact of the relaxation of delay constraints. Simulations are conducted to confirm the effectiveness of the proposed scheme.

With the growing deployment of intelligent applications in the Internet of Vehicles (IoV), collaborative deep neural network (DNN) inference across edge nodes has become essential for satisfying real-time processing and service demands. As a key enabler for 6G networks, rate-splitting multiple access (RSMA) offers improved transmission performance to meet the diverse requirements of multi-user communications. In this work, we propose an RSMA-assisted edge co-inference framework for distributed DNN task offloading in the IoV, where DNN inference tasks are partitioned and offloaded across roadside unit (RSU) and vehicles in a collaborative manner. The objective is to minimize the total task completion time for all vehicles by jointly optimizing the DNN partitioning and offloading strategy, RSMA power allocation, and computing resource allocation. The problem is inherently non-convex due to the strong coupling among communication, computation, and model partitioning variables. To address this, we develop a unified optimization framework and decompose it into three coordinated sub-problems. A triple-iteration joint optimization algorithm is formulated, combining successive convex approximation (SCA) based RSMA power control, genetic algorithm based DNN task scheduling, and Lagrange multiplier method based computing resource allocation at RSU. Simulation results demonstrate that, compared with baseline schemes, the proposed framework can reduce inference latency by up to $20\%$.