The construction of Low Earth Orbit satellite constellations has recently spurred tremendous attention from both academia and industry. 5G and 6G standards have specified the LEO satellite network as a key component of the mobile network. However, due to the satellites' fast traveling speed, ground terminals usually experience frequent and high-latency handover, which significantly deteriorates the performance of latencysensitive applications. To address this challenge, we propose a parallel handover mechanism for the mobile satellite network which can considerably reduce the handover latency. The main idea is to use plan-based handovers instead of measurementbased handovers to avoid interactions between the access and core networks, hence eliminating the significant time overhead in the traditional handover procedure. Specifically, we introduce a novel network function named Satellite Synchronized Function (SSF), which is designed for being compliant with the standard 5G core network. Moreover, we propose a machine learning model for signal strength prediction, coupled with an efficient handover scheduling algorithm. We have conducted extensive experiments and results demonstrate that our proposed handover scheme can considerably reduce the handover latency by 21× compared to the standard NTN handover scheme and two other existing handover schemes, along with significant improvements in network stability and user-level performance.

Satellites equipped with computing capabilities serve as invaluable access platforms for 5G and beyond (NextG) non-terrestrial networks (NTNs). They facilitate the continuous execution of resource-intensive edge-assisted deep learning (DL) tasks that are offloaded from Internet-of-Things (IoT) user equipment (UEs) in remote areas. To this end, satellite access network (SAN) resources need to be carefully “sliced”, consid-ering both the constrained energy availability and the scarcity of SAN resources. Existing SAN slicing approaches tend to treat offloaded tasks conventionally, overlooking the intricate semantics associated with DL tasks. In this paper, we propose semantic SAN (SemSAN), the first semantic SAN slicing algorithm for NextG AI-native NTNs. Our keen observations reveal that various DL tasks (i) can tolerate different degrees of image compression, and (ii) may yield equivalent model accuracy when employing DNN models with different sizes. These observations inspire us to further exploit the computation capability of a SAN to support more tasks while still minimizing overall energy consumption. After analyzing the characteristics of this optimization problem, we propose an online greedy SemSAN slicing algorithm to approximate its optimal solution. Extensive experiments verify the effectiveness of SemSAN in energy saving and its ability to support a substantial number of tasks, compared with other baselines.

Collaborative Edge Computing (CEC) is a new edge computing paradigm that enables neighboring edge servers to share computational resources with each other. Although CEC can enhance the utilization of computational resources, it still suffers from resource waste. The primary reason is that end-users from the same area are likely to offload similar tasks to edge servers, thereby leading to duplicate computations. To improve system efficiency, the computation results of previously executed tasks can be cached and then reused by subsequent tasks. However, most existing computation reuse algorithms only consider one edge server, which significantly limits the effectiveness of computation reuse. To address this issue, this paper applies computation reuse in CEC networks to exploit the collaboration among edge servers. We formulate an optimization problem that aims to minimize the overall task response time and decompose it into a caching subproblem and a scheduling subproblem. By analyzing the properties of optimal solutions, we show that the optimal caching decisions can be efficiently searched using the bisection method. For the scheduling subproblem, we utilize projected gradient descent and backtracking to find a local minimum. Numerical results show that our algorithm significantly reduces the response time in various situations.

With the rapid development of Mobile Edge Computing (MEC), various real-time applications have been deployed to benefit people's daily lives. The performance of these applications relies heavily on the freshness of collected environmental information, which can be quantified by its Age of Information (AoI). In the traditional definition of AoI, it is assumed that the status information can be actively sampled and directly used. However, for many MEC-enabled applications, the desired status information is updated in an event-driven manner and necessitates data processing. To better serve these applications, we propose a new definition of AoI and, based on the redefined AoI, we formulate an online AoI minimization problem for MEC systems. Notably, the problem can be interpreted as a Markov Decision Process (MDP), thus enabling its solution through Reinforcement Learning (RL) algorithms. Nevertheless, the traditional RL algorithms are designed for MDPs with completely unknown system dynamics and hence usually suffer long convergence times. To accelerate the learning process, we introduce Post-Decision States (PDSs) to exploit the partial knowledge of the system's dynamics. We also combine PDSs with deep RL to further improve the algorithm's applicability, scalability, and robustness. Numerical results demonstrate that our algorithm outperforms the benchmarks under various scenarios.

Energy saving plays an important role in designing AI-native 6G networks. Radio Access Network (RAN) slicing is a fundamental tool to save energy through resource multiplexing. However, as the AI services required by users become more heterogenous than ever in 6G network, service-oriented RAN slicing naturally consumes a lot of energy, leading to a tradeoff between QoS guarantees and energy saving for the network scheduler to decide. In this paper, we propose sustainable service-oriented (SSO) RAN slicing scheduler for 6G networks to jointly optimize workload distribution and resource allocation. The target is to minimize the long-term average energy consumption using the meta reinforcement learning (MRL) method. To be specific, each type of services is treated as an independent optimization problem, where the workload distribution is solved by convex optimization and the resource allocation is solve by Q-learning policy. Numerical results show that SSO effectively reduces the system energy consumption while satifying QoS requirements, as compared with benchmarks.

Mobile Edge Computing (MEC) is a promising computing paradigm that provides cloud computing services in proximity to end users. Due to the bursty and spatially imbalanced arrival of computation tasks, the workload on different edge servers may vary wildly. To improve the Quality of Experience (QoE), peer offloading has been proposed as an effective cooperation method that offloads tasks from busy edge servers to idle ones. Although the average latency has been extensively considered in the design of peer offloading strategies, the worst-case latency, a common Quality of Service (QoS) requirement that is usually demanded by latency-sensitive applications, yet receives much less attention. In this paper, we study the task scheduling among collaborative edge servers and propose an online algorithm that aims to maximize the system utility under the worst-case latency requirement and long-term energy consumption constraints. Both theoretical analysis and simulation results demonstrate that our algorithm performs well under various situations.

With the development of Mobile Edge Computing (MEC) and Internet of Things (IoT) technology, various real-time monitoring and control applications are deployed to benefit people’s daily life. The performance of these applications relies heavily on the timeliness of collected environmental information, which can be effectively quantified by the recently introduced metric named age of information (AoI). Although extensive researches have been conducted to optimize AoI under various circumstances, these works commonly require a priori information about the system dynamics that is usually unknown in realistic situations. To design a more practical scheduling algorithm, in this paper, we formulate the AoI minimization problem as a Constrained Markov Decision Process (CMDP) which can be solved by Reinforcement Learning (RL) algorithms without prior knowledge. To improve the running efficiency, we (1) introduce post-decision states (PDSs) to exploit the partial knowledge of the system’s dynamics, (2) perform a batch update in every learning step, (3) decompose the system-level value function into multiple device-level value functions, and (4) propose a heuristic algorithm to find the greedy action. Numerical results demonstrate that our algorithm is highly efficient and outperforms the benchmarks under various scenarios.

The integration of Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT), which is usually referred to as Wireless Powered Mobile Edge Computing (WP-MEC), has been recognized as a promising technique to enhance the lifetime and computation capacity of wireless devices (WDs). Compared to the conventional battery-powered MEC networks, WP-MEC brings new challenges to the computation scheduling problem because we have to jointly optimize the resource allocation in WPT and computation offloading. In this paper, we consider the energy minimization problem for WP-MEC networks with multiple WDs and multiple access points. We design an online algorithm by transforming the original problem into a series of deterministic optimization problems based on the Lyapunov optimization theory. To reduce the time complexity of our algorithm, the optimization problem is relaxed and decomposed into several independent subproblems. After solving each subproblem, we adjust the computed values of variables to obtain a feasible solution. Extensive simulations are conducted to validate the performance of the proposed algorithm.

In recent years, with the rapid proliferation of smart devices, Mobile Edge Computing (MEC) has been regarded as a promising technique that provides computing services in proximity to end-users. To improve the performance of MEC systems, Collaborative Edge Computing (CEC) is proposed to balance the load among cooperative edge servers. In practice, however, edge servers belong to different MEC service providers (SPs) and they have no incentive to help others. To encourage the cooperation between self-interested SPs, in this paper, we propose a profit-sharing incentive mechanism based on the Shapley value. In addition to the desirable properties such as efficiency and fairness, we also proved that our mechanism induces optimal offloading strategies and provides every SP an incentive to join the coalition. To protect the private information of SPs, we defined an aggregate profit function for each SP and showed that revealing this function is sufficient to calculate the profit allocation. Simulation results demonstrate that the system performance and SPs' revenue are substantially improved under cooperation.

Mobile-edge computing (MEC) is a new paradigm that provides cloud computing services at the edge of networks. To achieve better performance with limited computing resources, peer offloading between cooperative edge servers (e.g., MEC-enabled base stations) has been proposed as an effective technique to handle bursty and spatially imbalanced arrival of computation tasks. While various performance metrics of peer offloading policies have been considered in the literature, the worst case response time, a common quality of service (QoS) requirement in real-time applications, yet receives much less attention. To fill the gap, we formulate the peer offloading problem based on a stochastic arrival model and propose two online algorithms for cases with and without prior knowledge of task arrival rate. Our goal is to maximize the utility function of time-average throughput under constraints of energy consumption and worst case response time. Both theoretical analysis and numerical results show that our algorithms are able to produce close to optimal performance.