As an integral component of the space-air-ground integrated network (SAGIN), the low Earth orbit (LEO) satellite network has displayed immense potential in providing ubiquitous connectivity and broadband mobile communication. However, the intrinsic dynamics of LEO satellites pose unprecedented challenges in network management and service delivery. In this paper, we investigate the service function chain (SFC) orchestration in dynamic LEO satellite networks to achieve flexible and efficient service provision. Considering the service requirements and the limitations of network resources, we formulate the SFC orchestration problem as the integer nonlinear programming (INLP) problem for maximizing the service acceptance and the load fairness of satellites. Then, an efficient heuristic algorithm is proposed to solve this problem. Addressing the situation with frequent service requests, a graph attention network (GAT)-based approach with low complexity is also presented. Simulation results demonstrate that our proposed approaches outperform the benchmarks by a substantial margin in terms of load fairness and service acceptance. Besides, the proposed GAT-based approach shows its advantage in computation complexity, and exhibits robustness in unstable network scenarios with intermittent link interruptions.

This paper proposes a novel knowledge-driven approach for resource allocation in wireless networks using the graph neural network (GNN) architecture. To meet the millisecond-level timeliness and scalability required for the dynamic network environment, our proposed approach, named UWGNN, incorporates the deep unrolling of the weighted minimum mean square error (WMMSE) algorithm, referred to as domain knowledge, into GNN, thereby reducing computational delay and sample complexity while adapting to various data distributions. Specifically, by unrolling WMMSE algorithm into a series of interconnected submodules, UWGNN aligns closely with the optimization steps of the algorithm. Our analysis reveals the effectiveness of the deep unrolling method within UWGNN, which decomposes complicated end-to-end mappings, leading to a reduction in model complexity and parameter count. Experimental results demonstrate that UWGNN maintains optimal performance with computation latency 3 to 4 orders of magnitude lower than the WMMSE algorithm and exhibits strong performance and generalization across diverse data distributions and communication topologies without the need for retraining. Our findings contribute to the development of efficient and scalable wireless resource management solutions for distributed and dynamic networks with strict latency requirements.

In vehicular networks (VNets), vehicular federated learning (VFL) is a new learning paradigm that can protect data privacy of vehicle nodes (VNs) while training models. In VFL, the importance of data (IoD) is a key factor that affects model training accuracy. However, due to the heterogeneity of data in the VFL, it is a challenge to evaluate the quality of data owned by different VNs and design an efficient federated learning scheme to enable the VNs to complete learning tasks collaboratively. In this paper, we consider the IoD and propose a redundancy-aware collaborative federated learning (RCFL) scheme for the VFL. In the scheme, by jointly considering the data quality and the cooperation among VNs, we first design a redundancy-aware federated learning architecture to efficiently provide learning services in VNets. Then, we develop a data importance model that integrates the non-independent and identically distributed (non-IID) degree and the redundancy of data (RoD) to evaluate the data quality and formulate the cooperation of the VNs as a coalition game to improve their data importance, where the equilibrium of the coalition game is obtained by designing a coalition formation algorithm. After that, by considering the diversified characteristics of data and the available resources of different VNs in each coalition, a coalition-based federated learning algorithm is designed to enable the distributed coalitions to complete the learning task cooperatively with the target of improving the learning accuracy. The simulation results show that the proposed scheme outperforms the benchmark schemes in terms of the IoD obtained by the VNs and the training accuracy.

Federated learning (FL) has gained increasing attention due to its ability to collaboratively train while protecting client data privacy. However, vanilla FL cannot adapt to client heterogeneity, leading to a degradation in training efficiency due to stragglers, and is still vulnerable to privacy leakage. To address these issues, this paper proposes RingSFL, a novel distributed learning scheme that integrates FL with a model split mechanism to adapt to client heterogeneity while maintaining data privacy. In RingSFL, all clients form a ring topology. For each client, instead of training the model locally, the model is split and trained among all clients along the ring through a pre-defined direction. By properly setting the propagation lengths of heterogeneous clients, the straggler effect is mitigated, and the training efficiency of the system is significantly enhanced. Additionally, since the local models are blended, it is less likely for an eavesdropper to obtain the complete model and recover the raw data, thus improving data privacy. The experimental results on both simulation and prototype systems show that RingSFL can achieve better convergence performance than benchmark methods on independently identically distributed (IID) and non-IID datasets, while effectively preventing eavesdroppers from recovering training data.

Due to the wireless broadcasting and broad coverage in satellite-supported Internet of things (IoT) networks, the IoT nodes are susceptible to eavesdropping threats. Considering the distance difference between satellite and nearby destinations is negligible, the main and wiretapping channels between satellite and IoT node are similar, it poses great challenges to reach physical layer security in satellite-assisted IoT networks. In this paper, to guarantee secure transmissions for satellite-assisted IoT downlink communications, the multi-domain resource multiplexing based secure approach is proposed. Particularly, the self-induced co-channel interference between adjacent nodes is leveraged to increase the difference of signal transmission quality over both main and wiretapping channels. By comprehensively optimizing multi-domain resources, i.e., frequency, power, and spatial domains, secure transmissions from satellite to IoT nodes are reached. Specifically, the problem to maximize the sum secrecy rate of IoT nodes is formulated with a constraint of common communication rate of IoT nodes. To solve this non-convex problem, an alternating optimization (AO) algorithm with two inner successive convex approximation (SCA) algorithms are executed to solve the power allocation, spectral multiplexing, and precoding. In addition, simulation results are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.

Due to the dense deployment of a small base station (SBS), wired backhauling is not always available, nor it is efficient. Therefore mmWaves are introduced to serve as backhauling links that offer high backhauling throughput and low CAPEX. However, mmWaves suffer from a high attenuation rate as the distance between SBSs and a macro base station (MBS) increases, which can severely degrade the system performance. Therefore, it is more efficient to use some SBSs to aggregate from different SBSs to MBS. On the other hand, densely deployed SBSs with wireless backhauling can cause high energy consumption in the system. In this work, we present a new network model in which SBSs are able to harvest energy from a renewable source and utilize it for backhauling and their associate UEs. A mathematical Optimization problem is formulated to solve UEs association, dynamic sleeping, backhauling, and transmission power. Moreover, due to the complexity of the formulated problem, a heuristic algorithm is introduced. Namely, a heuristic backhauling and dynamic sleeping (HBDS) algorithm is introduced to decomposes the formulated problem into two parts and solve it iteratively. Finally, computer simulation results that demonstrate the model’s performance are presented for comparison between optimal solution and HBDS, which shows that HBDS has better computation efficiency with minimum performance difference.

In this paper, we propose a low-complexity user selection algorithm for an uplink multiuser transmission in millimeter wave (mmWave) WLAN. We first formulate the user selection problem, taking hybrid beamforming (HBF), an NP-hard problem, into consideration. We then develop a three-step HBF algorithm that incorporates user selection. Specifically, users can be selected based on semi-orthogonality instead of collecting perfect channel state information (CSI) from all potential users. We optimize the digital beamforming to mitigate residual interference among the selected users. Furthermore, we provide analytical validation for the proposed user selection algorithm and study the impact of angle correlation, analog beam pattern, and beamwidth on the achievable rate of the selected users. Extensive simulations validate the performance of the proposed overall HBF algorithm when compared with existing solutions.

The ultra-dense edge networks with mmWave and beamforming are envisioned as a potential solution to satisfy the high rate and capacity requirements in 5G networks. In IEEE 802.11 ad, which is the first beamforming-enabled WLAN standard, all stations (STs) contend for beamforming (BF) training opportunities in associated beamforming training (A-BFT) slots. However, due to limited number of A-BFT slots, BF training suffers from a severe collision issue, especially in dense networks, which results in a low channel utilization in the A-BFT stage. To achieve the maximum channel utilization, it is of significance to allocate A-BFT slots efficiently. Therefore, in this paper, we propose an analytical model to analyze IEEE 802.11 ad medium access control (MAC) protocol in BF training stage. In particular, we analyze the successful transmission probability and channel utilization of IEEE 802.11 ad MAC protocol in the dense network. Based on theoretical analysis, we provide the optimal number of A-BFT slots. In addition, theoretical analysis indicates that the maximum channel utilization in the A-BFT stage is barely e−1which is the same as that of slotted ALOHA protocol. Simulation results are provided to validate the accuracy of the analytical model and theoretical analysis.