In order to realize the internet of everything in sixth generation (6G), the emerging 6G applications have brought increasing demands for the high-speed communication and high-accuracy positioning concurrently. Relying on the potentials of high transmission power, large quantity and excellent geometric topology, low earth orbit (LEO) satellites have become strong candidates for providing integrated communication and positioning (ICAP) services. However, the resource competition between services and high dynamics of space-ground environment make it intractable to strike a balance between communication and positioning performance, which is one of the key design issue in LEO-ICAP networks. Against this backdrop, we consider an ICAP system with multiple LEO satellites, and adopt communication rate and squared position error bound (SPEB) as performance evaluation metrics. Based on that, we further formulate a weighted utility maximization problem, where the balance between communication and positioning performance can be achieved by jointly optimizing the subcarrier and power allocation, while simultaneously satisfying users’ quality of service (QoS) requirements. To solve this mixed-integer nonlinear programming problem, we propose a compressed sensing-based resource allocation algorithm, where the sparsity property of optimization variables is exploited to reformulate the problem into a continuous form, and the sequential convex programming method is then applied to solve the problem iteratively until convergence. Extensive simulations verify the superiority of our proposed algorithm compared to various benchmark schemes, where the proposed algorithm achieves a sum-rate improvement of at least 22% and a SPEB reduction of at least 28%, showing its effectiveness in realizing the balanced optimization of communication and positioning performance.

Relying on the powerful communication capabilities and rapidly changing geometric configurations, Low Earth Orbit (LEO) satellites have become strong candidates for offering the integrated communication and navigation (ICAN) services in future sixth generation (6G) networks. Considering the distinct performance and resource requirements, how to strike a balance between communication and navigation is one of the key design issues in LEO-ICAN systems. Against this backdrop, we take the transmission rate and geometric dilution of precision (GDOP) as evaluation metrics of communication and navigation performance, respectively, and formulate a weighted rate and GDOP maximization problem by jointly optimizing the beamforming design and satellite selection. To deal with the optimization problem, we split the original problem into the beamforming design and satellite selection subproblems, and propose a two-layer resource allocation algorithm to solve these subproblems iteratively until convergence. Specifically, in the inner layer, the beamforming design is modeled as a difference-of-convex (DC) problem, and the DC programming method is applied to maximize the communication rate. In the outer layer, the satellite selection is modeled as an overlapping coalition formation (OCF) game, and the OCF-based satellite selection algorithm is proposed to simultaneously reconcile the navigation GDOP. Extensive simulation results demonstrate the effectiveness of our proposed algorithms and reveal the trade-off between communication and navigation performance.

Unmanned Aerial Vehicles (UAVs) face significant challenges in autonomous navigation due to their limited energy and computational resources. This paper introduces a knowledge-driven meta-learning framework specifically designed for ultra-fast path planning in lightweight UAVs. The proposed approach integrates domain-specific knowledge across three core domains-environment, network, and behavior-with visual data to enable adaptive learning from unlabeled data and rapid model retraining in various scenarios. To evaluate this framework, we created the Meta-UAV Optimal Path Dataset, a unique dataset tailored for complex, multi-domain path planning tasks. Additionally, a knowledge-driven loss function incorporating physics-based constraints ensures that the model's predictions align with real-world conditions. Experimental results demonstrate that our model achieves superior path efficiency, cross-domain adaptability, and lower resource consumption compared to traditional models, making it a suitable choice for real-world UAV applications.

Training deep neural networks typically relies on gradient descent learning schemes, which is usually time-consuming, and the design of complex network architectures is often intractable. In this article, we explore the building of multilayer neural networks based on an efficient gradient-free learning scheme offering a potential solution to the architectural design. The proposed learning scheme encompasses both forward and backward training (BT) processes. In the forward process, the pseudoinverse learning (PIL) algorithm is employed to train a multilayer neural network, in which the network is dynamically constructed leveraging a layer-by-layer greedy strategy, enabling the automatic determination of the architecture across different hierarchies in a data-driven manner. The network architecture and connection weights determined in the forward training (FT) process are shared with the backward process which also conducts gradient-free learning to update the connection weights. After the bidirectional learning, a neural network comprising two twin subnetworks is obtained, and the fused features of subnetworks are used as inputs for downstream tasks. Comprehensive experiments and detailed analyses demonstrate the effectiveness and superiority of the proposed learning scheme.

Relying on the powerful communication capabilities and rapidly changing geometric configuration, the Low Earth Orbit (LEO) satellites have the potential to offer integrated communication and navigation (ICAN) services. However, the isolated resource utilization in the traditional satellite communication and navigation systems has led to a compromised system performance. Against this backdrop, this paper formulates a joint beamforming design and satellite selection optimization problem for the LEO-ICAN network to maximize the sum rate, while simultaneously reconciling the positioning performance. A two-layer algorithm is proposed, where the beamforming design in the inner layer is solved by the difference-of-convex programming method to maximize the sum rate, and the satellite selection in the outer layer is modeled as a coalition formation game to simultaneously reconcile the positioning performance. Simulation results verify the superiority of our proposed algorithms by increasing the sum rate by 16.6% and 29.3% compared with the conventional beamforming and satellite selection schemes, respectively.

Driven by deep learning, the field of content generation has witnessed remarkable progress. However, it still faces several challenges, such as low efficiency and high difficulty in training. To confront these obstacles, we propose an efficient and effective lightweight generative model, termed the pseudoinverse learning based variational autoencoder, within the framework of the synergetic learning system. The proposed learning system in this study comprises a reductive subsystem and a generative subsystem, incorporates a non-gradient learning scheme. The reductive subsystem employs variants of the pseudoinverse learning algorithm and probabilistic principal component analysis to embed the inputs into the latent space which is constrained to follow a standard normal distribution. The generative subsystem performs the inverse reconstruction process of the reductive subsystem, which can be used for content generation after training. The experimental results show that the proposed model significantly speeds up the training while achieving comparable generation quality to the baselines.

In this article, an unmanned aerial vehicle (UAV)-mobile edge computing (MEC) network is considered, where cellular-connected UAVs can either handle the computing tasks locally or offload to base stations. Considering the emerging computation-intensive and delay-sensitive applications, how to strike a balance between energy and delay, is one of the key design issues in UAV-MEC networks. Against this backdrop, we establish a double-queue model innovatively, in which the virtual queue is introduced to sense the backlog status of the actual queue. As such, the delay guarantee of computing tasks can be turned into the stable control of virtual queues. The network quality and server heterogeneity are considered to schedule the workloads rationally. Based on the Lyapunov optimization method, we formulate the deterministic problem to achieve a tradeoff between the long-term energy consumption and the time-average traffic delay, by jointly optimizing the offloading decision, resource allocation and trajectory planning, subject to the constraints of queue stability, resource budgets and flying kinematics. To solve this mixed-integer nonlinear programming problem, we propose an energy-efficient and delay-aware online algorithm, in which the problem is first split into equivalent resource allocation and trajectory planning subproblems, and the closed-form solutions of power, slot and computing resource allocation can be derived. Then, based on the Lagrange dual and successive convex approximation methods, these subproblems are solved iteratively to explore the optimality. Extensive simulations validate the superiority of our proposed algorithm over various benchmark schemes, showing its effectiveness in minimizing the energy consumption while simultaneously maintaining the low latency.

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables the IoT devices of limited computation capabilities and energy to offload their computation-intensive and delay-sensitive tasks to the network edge, thereby providing high quality of service to the devices. In this article, we apply non-orthogonal multiple access (NOMA) technique to enable massive connectivity and investigate how it can be exploited to achieve energy-efficient MEC in IoT networks. In order to maximize the energy efficiency for offloading, while simultaneously satisfying the maximum tolerable delay constraints of IoT devices, a joint radio and computation resource allocation problem is formulated, which takes both intra- and inter-cell interference into consideration. To tackle this intractable mixed integer non-convex problem, we first decouple it into separated radio and computation resource allocation problems. Then, the radio resource allocation problem is further decomposed into a subchannel allocation problem and a power allocation problem, which can be solved by matching and sequential convex programming algorithms, respectively. Based on the obtained radio resource allocation solution, the computation resource allocation problem can be solved by utilizing the Knapsack method. Numerical results validate our analysis and show that our proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in IoT networks compared to the existing baselines.

Mobile edge computing (MEC) enables the users of limited computation capabilities and energy to offload their computation-intensive and delay-sensitive tasks to the network edge, thereby providing high quality of service to the users. In this paper, we investigate how non-orthogonal multiple access (NOMA) techniques can be exploited to achieve energy-efficient MEC in multi-cell networks. To this end, we first characterize the energy efficiency of the considered system, taking into account the impact of both intra- and inter-cell interference in multi-cell networks. We then jointly optimize the subchannel allocation, power allocation, and the computation resource allocation to maximize the energy efficiency of NOMA-enabled MEC, while simultaneously satisfying the maximum tolerable delay constraints of the users. Numerical results validate our analysis and show that our proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in multi-cell networks compared to the existing baselines.

Non-orthogonal multiple access (NOMA) has been considered as a promising communication technology to enhance the spectral efficiency and support massive connections in fog radio access networks (F-RANs). In this paper, with the aim of maximizing the weighted sum rate while taking co-channel interference into consideration, a joint resource block (RB) and power allocation problem is formulated. To solve this problem, we first propose the optimal resource allocation scheme. Specifically, the monotonic optimization is applied and an outer polyblock approximation algorithm is proposed to get the global optimal solution. In order to reduce the computational complexity, we then propose the suboptimal resource allocation scheme. In particular, the original problem is decomposed into separated RB and power allocation problems. The RB allocation problem is modeled as a many-to-one matching game and a modified swap-enabled matching algorithm is proposed. The power allocation problem is converted into a convex form through some approximations and solved by a successive convex approximation algorithm. Simulation results demonstrate that the suboptimal scheme can achieve almost the same performance as the optimal scheme, while requiring much less computational complexity. In addition, the superiority of NOMA-enabled F-RANs over the conventional OMA-enabled F-RANs is verified.

Discovering 1-day vulnerabilities in binary patches is worthwhile but challenging. One of the key difficulties lies in generating inputs that could reach the patched code snippet while making the unpatched program crash. In this paper, we named it as a target-oriented input generation problem or a ToIG problem for clarity. Existing solutions for the ToIG problem either suffer from path explosion or may get stuck by complex checks. In the paper, we present a new solution to improve the efficiency of ToIG which leverage a combination of a distance-based directed fuzzing mechanism and a dominator-based directed symbolic execution mechanism. To demonstrate its efficiency, we design and implement 1dVul, a tool for 1-day vulnerability discovering at binary-level, based on the solution. Demonstrations show that 1dVul has successfully generated inputs for 130 targets from a total of 209 patch targets identified from applications in DARPA Cyber Grant Challenge, while the state-of-the-art solutions AFLGo and Driller can only reach 99 and 107 targets, respectively, within the same limited time budget. Further-more, 1dVul runs 2.2X and 3.6X faster than AFLGo and Driller, respectively, and has confirmed 96 vulnerabilities from the unpatched programs.

In order to achieve efficient communication in the fifth generation (5G) networks, non-orthogonal multiple access (NOMA) technique has been utilized in fog radio access networks (F-RANs). In this paper, we investigate the resource allocation problem in a NOMA-enabled downlink F-RAN. To maximize the weighted sum rate of NOMA users served by fog-computing-based access points (F-APs), the resource block (RB) allocation and power allocation are optimized. Specifically, we decouple the problem into RB allocation and power allocation problems. The former is modeled as a many-to-one matching game and we propose a modified swap-enabled matching algorithm to solve it, which takes interference threshold into consideration. The later is a non-convex problem, we transform it into a tractable one via some approximations and get the closed-form expressions of power allocation coefficients. Finally, we combine the both to propose a joint resource allocation algorithm, which is preformed iteratively to obtain the optimal result. Simulation results are provided to show the performance of the algorithm.

Non-orthogonal multiple access (NOMA) has been considered as a promising communication technology to enhance spectral efïciency (SE) and support massive connections in fog radio access networks (F-RANs). In this paper, to maximize weighted sum rate while taking co-channel interference into consideration, a joint resource block (RB) and power allocation problem is formulated. Based on the suboptimal scheme with low computational complexity proposed in last work, the optimal algorithm is proposed to provide an upper bound of the system performance. In particular, monotonic optimization is applied and an outer polyblock approximation algorithm is proposed to get the globally optimal solution. Simulation results demonstrate the performance of proposed algorithms and verify the superiority of NOMA-enabled F-RANs over OMA scenarios.

Open-source licenses are widely used in open-source projects. However, developers using or modifying the source code of open-source projects do not always strictly follow the licenses. GPL and AGPL, two of the most popular copyleft licenses, are most likely to be violated, because they require developers to open-source the entire project if any code under GPL/AGPL protection is included whether modified or not. There are few license violation detectors focusing on binary software, owning to the challenge of mapping binary code to source code efficiently and accurately at large scale. In this paper, we propose a scalable and fully-automated system to check open-source license violation of binary software at large scale. We match source code to binary code by analyzing file attributes of executable files and code features that are not affected by compilation and could vary between projects. Moreover, to break the barrier of large-scale analysis, we introduce an automatic extractor to parse executable files from installation packages that are broadly available in software download sites. In empirical experiments of binary-to-source mapping, we have got a remarkable high accuracy of 99.5% and recall of 95.6% without significant loss of precision. Besides, 2270 pairs of binary-to-source mapping relationships are discovered, with 110 license violations of GPL and AGPL licenses related to 7.2% of the 1000 real-world binary software projects.