In this paper, we propose a novel service continuityaware Service Function Chain (SFC) embedding scheme for dynamic large-scale LEO satellite networks, where service disruptions occur when satellites hosting virtual network functions of an SFC move out of the service region. Particularly, we define a new metric, i.e., the Remaining Time to Migration (RTTM), which indicates the remaining functional time of an SFC before SFC reconfiguration is needed. We then formulate a service continuity-aware SFC embedding problem with the objective of maximizing the long-term acceptance ratio while increasing the normalized RTTM of accepted SFCs. We propose a scalable graph neural network-assisted deep reinforcement learning (DRL) approach to solve the embedding problem. By employing a differentiable pooling technique, we condense the feature representation of large-scale LEO satellite networks, thereby reducing the computational complexity of the down-stream DRL-based decision-making. Simulation results show that our approach reduces the proportion of reconfigured SFCs by 60 % compared to the benchmark, indicating its effectiveness in enhancing service continuity.

Resource slicing in low Earth orbit satellite networks (LSN) is essential to support diversified services. In this paper, we investigate a resource slicing problem in LSN to reserve resources in satellites to achieve efficient resource provisioning. To address the challenges of non-stationary service demands, inaccurate prediction, and satellite mobility, we propose an adaptive digital twin (DT)-assisted resource slicing scheme for robust and adaptive resource management in LSN. Specifically, a slice DT, being able to capture the service demand prediction uncertainty through collected service demand data, is constructed to enhance the robustness of resource slicing decisions for dynamic service demands. In addition, the constructed DT can emulate resource slicing decisions for evaluating their performance, enabling adaptive slicing decision updates to efficiently reserve resources in LSN. Simulation results demonstrate that the proposed scheme outperforms benchmark methods, achieving low service demand violations with efficient resource consumption.

MOTIVATION: Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples. RESULTS: We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and makes complementary contributes to the final prediction of the drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. These results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies. AVAILABILITY AND IMPLEMENTATION: Source code and data are available at https://github.com/LiHongCSBLab/JointSyn.

Ultra-dense low earth orbit (LEO) satellite network (UD-LSN) is one of the most promising architectures in the sixth-generation (6G) systems, providing several types of services with different service level agreements (SLAs). Network slicing technology effectively meets these SLAs by building multiple logical networks isolated from each other on the physical network. In the UD-LSN, due to the spatiotemporal variations of users and available satellites, it poses a considerable challenge to make dynamic slicing decisions individually for each LEO satellite. This paper proposes a two-layer dynamic reconfigurable radio access network (RAN) slicing architecture for the UD-LSN. We consider the characteristics of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (uRLLC) services and formulate a stochastic optimization problem to maximize the long-term slicing utility, which consists of resource utilization, throughput, and reconfiguration cost. The original problem is transformed into a Markov Decision Process (MDP) and solved with the Branch Dueling Q-Network (BDQ)-based dynamic reconfigurable RAN slicing (DRRS) algorithm in a large slicing window and the priority-based user access algorithm in a small time slot. The simulation results validate the effectiveness of the proposed two-layer DRRS strategy, which has a better performance in the slicing utility, resource utilization, and throughput.

With the rapid expansion of the scale of deployed low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated network (LTIN) is envisioned as a promising architecture in the sixth-generation (6G) system to implement seamless connectivity and high-speed data rate service. Especially for ultra-remote real-time services with long transmission distance and high delay requirements, the integrated network can guarantee its end-to-end service continuity. However, many challenges have been posed to the efficient resource orchestration for the service delivery, owing to the large scale, heterogeneity and high mobility of the integrated network. For each service, its data needs to go through a series of on-board processing, before being downloaded to the terrestrial network for further applications. To this end, service function chain (SFC), an ordered concatenation of network functions (NFs), is introduced to support service provision. By allocating the constituent NFs over the LTIN, we propose an efficient multiple service delivery scheme to minimize the overall delivery completion latency, while taking into account resource sharing and competition among multiple SFCs. First, we formulate the multiple SFC embedding problem as a noncooperative game that is further proved as the weighted potential game with at least one Nash equilibrium (NE). With the help of the proposed global coordination mechanism, we design two algorithms to obtain the NE. One is the best response (BR) algorithm with faster convergence, while the other is adaptive play (AP) algorithm with more capacity for best solutions. Then, the stochastic learning (SL) algorithm is proposed to adapt to network dynamics and reduce global information exchange. Finally, extensive simulations validate the convergence and effectiveness of the proposed algorithms.

To enable flexible base stations (BS) association and dynamic resource management for personalized user equipment (UE) download service provision in the next-generation mobile communication network (6G), in this paper, we investigate the downlink (DL) transmission scenario in an origin fully-decoupled radio access network (FD-RAN) architecture. Considering the unique fully-decoupled UL/DL access feature, we propose an efficient two-stage DL channel estimation method in the FD-RAN. We formulate a novel multi-connectivity and dynamic resource cooperation problem with joint multiple-BS and multiple-UE association and coordinated beamforming, aiming at maximizing the weighted sum achievable rate in DL FD-RAN. By leveraging the many-to-many swap-matching theory and fractional relaxation approach, we solve the dynamic UE scheduling problem with multiple-BS and multiple-UE association and the coordinated beamforming problem, respectively. Extensive simulation results based on standard 3GPP 36.873 urban micro channel demonstrate that the proposed framework can improve the average spectral efficiency by 34.9% as compared to the traditional maximum ratio transmission beamforming method.

Recently, the rapid-developed mega low earth orbit (LEO) satellite constellation has shown its great potential in cooperating with terrestrial networks to provide seamless global connectivity and high-speed data rate services. However, the heterogeneity of physical resources and diversity of service demands pose challenges for delivering service in an efficient way in the ultra-dense LEO satellite-terrestrial integrated networks (LTIN). When implementing service delivery, service data generally needs a series of on-board processing and then downloading to the terrestrial network for further applications. In this paper, we introduce service function chain (SFC), a sequence of network functions, to process the data on board and propose an efficient multiple service delivery scheme in the LTIN to minimize the total delivery completion time. Considering the heterogeneous resource sharing and competition among multiple SFCs, we formulate the problem as a noncooperative game, which is further proved as a weighted potential game. We design an improved response (IR) algorithm with fast convergence and an adaptive play (AP) algorithm to find the best Nash equilibrium (NE). Extensive simulation results validate the convergence and effectiveness of the proposed algorithms.

The emerging uplink (UL) and downlink (DL) decoupled radio access networks (RAN) has attracted a lot of attention due to the significant gains in network throughput, load balancing and energy consumption, etc. However, due to the diverse vehicular service requirements in different vehicle-to-everything (V2X) applications, how to provide customized cellular V2X services with diversified requirements in the UL/DL decoupled 5G and beyond cellular V2X networks is challenging. To this end, we investigate the feasibility of UL/DL decoupled RAN framework for cellular V2X communications, including the vehicle-to-infrastructure (V2I) communications and relay-assisted cellular vehicle-to-vehicle (RAC-V2V) communications. We propose a two-tier UL/DL decoupled RAN slicing approach. On the first tier, the deep reinforcement learning (DRL) soft actor-critic (SAC) algorithm is leveraged to allocate bandwidth to different base stations. On the second tier, we model the QoS metric of RAC-V2V communications as an absolute-value optimization problem and solve it by the alternative slicing ratio search (ASRS) algorithm with global convergence. The extensive numerical simulations demonstrate that the UL/DL decoupled access can significantly promote load balancing and reduce C-V2X transmit power. Meanwhile, the simulation results show that the proposed solution can significantly improve the network throughput while ensuring the different QoS requirements of cellular V2X.

Mobile edge computing (MEC) has been widely considered as an effective technology to handle computationally intensive tasks generated by mobile devices. However, the computation resources at an edge node is usually several orders of magnitude smaller than that of a cloud. Thus, it is rather vital to take an investigation into the collaboration between the cloud and the edge. In this paper, to fully exploit the computation power of the cloud server and achieve energy efficient task offloading, we propose an LEO-assisted terrestrial-satellite network (TSN) architecture for cloud-edge collaborative computation offloading. We formulate the collaborative cloud-edge computing problem that minimizes the energy consumption of the whole TSN under the quality-of-service (QoS) constraints. The optimization problem is further decomposed into two subproblems which are solved by deep neural networks (DNN) and successive convex approximation (SCA) algorithm, respectively. Simulation results show the effectiveness of our proposed cloud-edge collaborative computation offloading architecture on achieving a lower energy cost.

The Uplink (UL) and Downlink (DL) decoupled cellular access through flexible cell association has attracted a lot of attention due to numerous benefits such as higher network throughput, better load balancing, and lower energy consumption, etc. In this paper, we introduce a novel reinforcement learning aided decoupled RAN access framework for Cellular Vehicle-to-Everything (V2X) communications, and propose a two-step RAN slicing approach to dynamically allocate the radio resource to V2X services in different time granularity. We derive an innovative QoS metric of V2V cellular mode by taking consideration of the bidirectional nature of V2V cellular communications. Moreover, we maximize the sum utility considering the proposed QoS metric by leveraging the Deep Deterministic Policy Gradient (DDPG) enabled RAN slicing method. Simulation results are provided to demonstrate the advance of the proposed reinforcement learning aided decoupled RAN slicing framework in achieving load balancing, maximizing total network utility and satisfying the QoS metric of Cellular V2X communications.