With the unprecedented development of smart mobile devices, e.g., Internet of Things devices and smart phones, various computation-intensive applications are explosively increasing in ultra-dense networks (UDNs). Mobile edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of smart mobile devices and decrease service latency for computation-intensive applications. With the benefits of network function virtualization, MEC can be integrated with cloud radio access network (C-RAN) in UDNs for computation and communication cooperation. However, with stochastic computation task arrivals and time-varying channel states, it is challenging to offload computation tasks online with energy-efficient computation and radio resource management. In this paper, we investigate the task offloading and resource allocation problem in MEC-enable dense C-RAN, aiming at optimizing network energy efficiency. A stochastic mixed-integer nonlinear programming problem is formulated to jointly optimize the task offloading decision, elastic computation resource scheduling, and radio resource allocation. To tackle the problem, Lyapunov optimization theory is introduced to decompose the original problem into four individual subproblems which are solved by convex decomposition methods and matching game. We theoretically analyze the tradeoff between energy efficiency and service delay. Extensive simulations evaluate the impacts of system parameters on both energy efficiency and service delay. Simulation results also validate the superiority of the proposed task offloading and resource allocation scheme in dense C-RAN.

Low Earth Orbit (LEO) satellite communication networks have become an important means to provide internet access services for areas with limited infrastructure. Compared with the Geostationary Earth Orbit (GEO) satellites, the LEO satellites have limited on-board communication caching and calculating resources. Furthermore, the distribution of traffic requests is dynamically changing and uneven due to the relative movement between the LEO satellites and the ground. Therefore, how to schedule the multi-dimensional resources is an important issue for the LEO satellite communication networks. Beam-hopping is an efficient approach to improve the resource utilization by dynamically allocating time, power, and frequency according to the traffic requests. This paper proposes an efficient multi-dimensional resource allocation mechanism for beam-hopping in LEO satellite networks, which simultaneously satisfies the GEO interference avoidance. First, we construct the beam-hopping model of LEO satellites, and formulate the resource optimization problem. Second, we provide the weighted greedy strategy to determine the illumination pattern. In order to reduce the search space, the cells are clustered to non-interference clusters. Then, an improved genetic algorithm is provided to jointly allocate the communication resources. Finally, we construct various simulations to evaluate our proposed mechanism. Compared with the random-BH, polling-BH and traditional genetic algorithm, our algorithm achieves better performance in terms of both system throughput, access success rate, average delay and fairness between cells. The performance improvement is more significant in scenarios where traffic demand is unevenly distributed.

Fast convergence routing is a critical issue for Low Earth Orbit (LEO) constellation networks because these networks have dynamic topology changes, and transmission requirements can vary over time. However, most of the previous research has focused on the Open Shortest Path First (OSPF) routing algorithm, which is not well-suited to handle the frequent changes in the link state of the LEO satellite network. In this regard, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL–SR) for LEO satellite networks, where the satellite can quickly obtain the network link status and adjust its routing strategy accordingly. In FRL–SR, each satellite node is considered an agent, and the agent selects the appropriate port for packet forwarding based on its routing policy. Whenever the satellite network state changes, the agent sends “hello” packets to the neighboring nodes to update their routing policy. Compared to traditional reinforcement learning algorithms, FRL–SR can perceive network information faster and converge faster. Additionally, FRL–SR can mask the dynamics of the satellite network topology and adaptively adjust the forwarding strategy based on the link state. The experimental results demonstrate that the proposed FRL–SR algorithm outperforms the Dijkstra algorithm in the performance of average delay, packet arriving ratio, and network load balance.

The low earth orbit (LEO) constellation network has become a promising approach to provide global communication services, due to its advantages in wide global coverage, low transmission delay, and convenient networking. However, the instability of the intersatellite laser terminal and the high relative speed between adjacent satellites cause frequent network topology changing problems for data routing. In this paper, a disruption tolerant distributed routing algorithm (DTDR) is proposed, where the satellites calculate the alternate path for transmission when the network topology changes, which improves the performance of packet loss. Specifically, each satellite maintains the intersatellite link (ISL) information within a specified number of hops. When an ISL state changes within the specified number of hops, the corresponding satellite calculates and switches to the detour path. Furthermore, the traffic is balanced through the detour process. Various simulations were constructed and show that the proposed algorithm outperforms the existing algorithm in terms of packet loss ratio and transmission delay.

In the realm of providing space-based internet access services, utilizing large-scale low Earth orbit (LEO) satellite networks have emerged as a promising solution for bridging the digital divide and connecting previously unconnected regions. The deployment of LEO satellites can augment terrestrial networks, with increased efficiency and reduced costs. However, as the size of LEO constellations continues to grow, the routing algorithm design of such networks faces numerous challenges. In this study, we present a novel routing algorithm, designated as Internet Fast Access Routing (IFAR), aimed at facilitating faster internet access for users. The algorithm consists of two main components. Firstly, we develop a formal model that calculates the minimum number of hops between any two satellites in the Walker-Delta constellation, along with the corresponding forwarding direction from source to destination. Then, a linear programming is formulated, to match each satellite to the visible satellite on the ground. Upon receipt of user data, each satellite then forwards the data only to the set of visible satellites that correspond to its own satellite. To validate the efficacy of IFAR, we conduct extensive simulation work, and the experimental results showcase the potential of IFAR to enhance the routing capabilities of LEO satellite networks and improve the overall quality of space-based internet access services.

Federated Learning (FL) has emerged as an unparalleled machine learning paradigm where multiple edge clients jointly train a global model without sharing the raw data. However, sharing local models or gradients still compromises clients’ privacy and could be susceptible to delivery failures due to unreliable communication links. To address these issues, this paper considers a multi-server FL where E edge clients wish to jointly train the global model with the help of H servers while guaranteeing data privacy and meanwhile combating s ≤ H unreliable links per client. We first propose a hybrid coding scheme based on repetition coding and MDS Coding, such that any T_s colluding servers cannot deduce any client data besides the aggregated model, and any T_e colluding clients remain unaware of honest clients’ data. Furthermore, we propose a Lagrange coding with mask (LCM) to ensure more stringent privacy protection that additionally demands that colluding servers possess no knowledge about either the local or global models. Furthermore, we establish lower bounds for both the uplink and downlink communication loads and theoretically prove that the hybrid scheme and LCM scheme can achieve the optimal uplink communication loads under the first and second threat models, respectively. For the second threat model with no straggling link, the LCM scheme is optimal. These demonstrate the communication efficiency, robustness, and privacy guarantee of our schemes.