Accurate cellular traffic prediction is crucial for the rational allocation of network resources. Existing methods generally overlook the accurate extraction and full utilization of features across different periods of cellular traffic. Thus, we propose a Relative Long Short-Term Adaptive Spatial-Temporal Targeted Extraction (RLSASTTE) network, which fully extracts long-term trends and short-term dynamics, and then maximizes their utilization. First, to extract long-term spatial-temporal features, we exploit the strength of the Transformer in capturing long-range temporal dependencies, address its limitations in spatial relationship modeling by redesigning a spatial-temporal attention mechanism, and further adopt a pre-decomposition strategy to emphasize seasonal component mining. Second, to capture short-term spatial-temporal features, we define a multi-dilation convolution to explore short-term dependencies and design a novel local strong correlation dynamic attention mechanism to investigate local spatial influences, which also eliminates the limitation of fixed neighboring nodes. Finally, we innovatively employ an adaptive dual gating mechanism to efficiently integrate diverse features. We conducted experiments on three real-world datasets. Compared to the state-of-the-art methods, RLSASTTE achieves reductions of at least 3.1% and 3.7% in mean absolute error and root mean square error, respectively. In addition, we explore auxiliary training based on data augmentation, achieving additional performance improvements of 2.0% to 4.5%.

Mobile Edge Cache allows CDN edge nodes to be deployed closer to users, reducing content transmission latency in Vehicular Networks (VANETs). However, how to effectively utilize the limited storage space of cache nodes is the main issue in current research. To address this problem, we propose an incentive-driven hierarchical collaborative caching algorithm based on D3QN combined with user preferences (PC-ID3QN). Firstly, we constructed a UAV-assisted vehicular content-centric network framework, designing different collaborative caching strategy for various layers based on user preferences. Secondly, we proposed a hierarchical incentive mechanism based on social priority to motivate users to participate in collaborative sharing, thereby enhancing the utilization of system caching resources. Next, we modeled the cache placement problem as a system utility maximization problem and proved its NP-hardness. Then, by adjusting the constraint conditions, we conducted theoretical analysis and transform it into a linear programming(LP) problem to obtain an offline theoretical solution. This solution was validated against the simulation optimal solution obtained using the proposed PC-ID3QN algorithm, demonstrating the effectiveness of the algorithm. Finally, we validate the proposed caching strategy using the MovieLens dataset and conduct extensive experiments to verify the applicability and superiority of our solution in improving cache utility. Compared with DDQN, Dueling DQN and DQN, our proposed ID3QN algorithm reduces the request delay by 1.03%,1.73% and 2.21%, and reduces the energy cost by 38.8%, 19.6% and 17.2%, respectively.

To cope with the dramatic growth of future network traffic and the diversity of services, beyond fifth-generation (B5G) and sixth-generation (6G) wireless communication need to balance the network load while being service-oriented. In this paper, we consider a multi-access edge computing (MEC) driven radio access network (RAN) slicing scenario in a heterogeneous cellular network (HetNet) with local traffic overload. First, since users are generally non absolutely rational when selecting base stations (BSs), an evolutionary game (EG) based user association (UA) scheme is proposed to solve the problem of overload. Specifically, we innovatively define the load function of the base stations (BSs) and combine resource capacity of BSs to form the payoff function to dynamically adjust the load. Second, we model the network slicing (NS) problem using the transmission rate, average latency and quality of service (QoS). The problem is further relaxed and an NS algorithm based on distributed successive convex approximation (DSCA) is presented to derive a theoretical upper bound reference value as a static offline criterion. Finally, considering the high randomness of user task arrival in the real scenario, we formulate the multi-base station slicing problem as a stochastic game (SG) and a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based distributed network slicing algorithm is proposed to obtain excellent slicing strategies. Simulation results show that our proposed UA algorithm has a unique evolutionary equilibrium (EE) solution and is highly scalable. The proposed MATD3-based NS algorithm has better performance compared to other baseline algorithms and converges to a utility value that best approximates the theoretical upper bound.

In the upcoming 6G era, the demand for network communication and computing is expected to surge and diversify. A Space-Air-Ground Integrated Network (SAGIN) is introduced as a solution to provide seamless global connectivity. Meanwhile, network slicing technology can further enhance network capabilities by supporting customized services. However, the joint optimization of multi-dimensional resource allocation and task offloading decisions presents a significant challenge in dynamic and complex environments with diverse task types. In this work, we establish an SAG IN paradigm that integrates network slicing. A multi-level optimization scheme for resource allocation and task offloading is proposed to improve Key Performance Indicators (KPIs) and Quality of Service (QoS). Specifically, we propose a State Encoding-based Multi-Agent Soft Actor-Critic algorithm (SE- MASAC) within a Centralized Training with Decentralized Execution (CTDE) architecture. This algorithm processes current sensing states and historical information, leveraging reinforcement learning to make inter-slice resource and offloading scheduling decisions. Intra-slice decisions for intelligent User Equipments (iUEs) are determined based on their sensing data-driven priority. Simulation results demonstrate that our approach outperforms baselines in terms of system utility, QoS satisfaction, and various KPIs.

With the rapid development of applications with different use cases and service demands for edge network, network slicing is an emerging solution for satisfying service-oriented requirements, while the low earth orbit (LEO) satellite caching-assisted communication has been considered as one of the key elements for effective services. With limited resources at the edge of the radio access network (RAN), it is challenging to take advantage of the LEO content cache to joint allocation of communication, computing and caching space (3C) resources. To this end, we investigate the problem of resource slicing and scheduling of joint 3C resources in RAN edge scenario assisted by LEO content caching. A hierarchical resource slicing framework is proposed for dynamic allocation of multidimensional resources. The optimization variables are relaxed and the constraints are adjusted. The sequential quadratic programming (SQP) iteration algorithm is proposed as theoretical offline baseline. Due to its complex solving process and limited real-time performance, we incorporate Long Short-Term Memory (LSTM) into the Soft Actor-Critic (SAC) algorithm to aware extract the distribution characteristics of historical information and propose the deep reinforcement learning algorithm of LSTM-SAC. Meanwhile, the proportional priority based scheduling algorithm is employed in the intra-slice. Compared to SAC, TD3 and DDPG algorithms, the proposed algorithm is the closest to the theoretical value, improves the objective function by 6.95%, 9.52% and 11.52% respectively, which can significantly improve the system rate while satisfying the service level agreements.

With the development of Intelligent Transportation Systems (ITS), edge caching has gradually emerged as a critical technology to reduce transmission delay and optimize network load. However, the limited storage capacity and service scope of individual cache servers significantly degrade the performance of edge caching. To address this issue, we propose a social-aware assisted edge collaborative caching algorithm based on Dueling Double Deep Q-Network and Digital Twin Network (SACTD-D3). The algorithm can dynamically adjust the caching decision based on the similarity of user semantic information and the availability of edge services to fully utilize the caching capacity of edge servers. Firstly, vehicle clusters are formed based on users’ semantic similarity, and an on-board cloud is constructed to reduce user request delay by sinking edge services. Secondly, based on the establishment of the three-layer structure of macro base station, roadside units and on-board cloud, the content heat-based caching decision policy is utilized to effectively improve the content cache hit rate. Moreover, an optimization problem is formulated to maximize the overall utility of the system subject to transmission delay and system cost, and thus the optimal solution is obtained using the proposed$\varepsilon$-greedy SACTD-D3 algorithm. Furthermore, due to the dynamic complexity of the network topology, digital twin is used to simplify and map the network topology into digital twin networks for analysis and processing to improve network efficiency. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm in improving the system performance. Compared with Double DQN, Dueling DQN and DQN, the proposed SACTD-D3 algorithm reduces the request delay by 2.62$\%$, 3.06$\%$and 3.95$\%$, and reduces the energy cost by 26.07$\%$, 47.05$\%$and 49.90$\%$, respectively.

Due to buildings blocking GPS and Wi-Fi signals, traditional techniques can't offer the user's required positioning accuracy in resource-constrained underground parking, but the cooperation of agent nodes can provide the exact localization information to improve the positioning accuracy. However, some well-localized agents may not be willing to sacrifice additional power to improve the others' positioning accuracy. To encourage cooperation among nodes and allocate transmission power reasonably, this paper proposes a bidding-auction-based cooperative localization (BACL) algorithm to improve the positioning accuracy of agent nodes by joint node selection incentive and power allocation strategy. Firstly, the contribution of channel parameters and prior localization information of agent nodes for positioning accuracy are quantified and an incentive mechanism of cooperative localization from an economic perspective is proposed. Secondly, a virtual currency incentive rule is developed to compensate agent nodes of cooperative localization reasonably due to the consumption of energy for transmitting their location information. Finally, the simulation results have shown that the proposed BACL algorithm has excellent performance in terms of localization accuracy in resource-constrained scenarios. Compared with the full-power cooperative localization (FPCL) and non-cooperative localization (NCL) algorithms, the proposed BACL algorithm improved the positioning accuracy by 10% and 65%, respectively. Meanwhile, compared with the FPCL algorithm, the proposed algorithm reduced resource consumption by 50%.

With the continuous development of the Internet of Vehicles(IoV), Vehicle Edge Computing(VEC) has become a key technology for computational resource scheduling, but more and more smart devices are connected to the internet, which makes it difficult for traditional Vehicle Edge Networks(VEN) to deal with tasks in time. In this paper, in order to cope with the challenges of the large number of devices accessing the internet, we propose a vehicle-assisted computation offloading algorithm based on proximal policy optimization(VCOPPO) for User Equipment(UE) tasks, and it combines dynamic parked vehicles incentives mechanism and computational resource allocation strategy by using road vehicles and parked vehicles as edge servers. Firstly, a non-convex optimization problem combining VEN utility and task processing delay is formulated, subject to the constraints of the residual energy and the transmission rate of the task. Secondly, the proposed VCOPPO is used to solve the formulated non-convex optimization problem, and we use stochastic policy to obtain the optimal computation offloading decisions and resource allocation schemes. Finally, the experimental results have shown that the proposed VCOPPO has an excellent performance in network reward and task processing delay respectively, and it can effectively schedule and allocate computational resources. Compared with using Dueling Deep Q Network(Dueling DQN), Deep Q Network(DQN) and Q-learning methods, the proposed VCOPPO improves the network reward by 31%, 18% and 91%, reduces the delay in task processing by 78%, 63% and 74%, respectively.