The slice-based Open Radio Access Network (O-RAN) enables rapid deployment of logical networks and provides personalized services. In designing the inter-slice resource sharing scheme, multiple factors need to be considered. Firstly, resource isolation level (RIL) and interference isolation level (IIL) are two key factors in ensuring slice isolation. Secondly, the new business models brought by network slicing imply the need to consider tenants’ resource costs. However, to date, no prior studies have considered RIL, IIL and costs simultaneously, often involving only one or two of them. Accordingly, we propose an inter-slice resource block (RB) leasing and association adjustment scheme (ISRLA) that allows slices to borrow RBs from the shared resource pool and other slices. ISRLA is divided into three modules, namely, prediction module, leasing module, and RB association adjustment module. Firstly, the prediction module uses a Long Short-Term Memory model to predict RB demand and guide subsequent resource optimization. Then, the leasing module balances RIL and costs to determine the number of RBs to be leased in or leased out. This is defined as a non-linear integer programming problem, which is solved through an iterative strategy based on camp swapping (ISBCS). ISBCS is further proven to be convergent. Finally, the RB association adjustment module determines specific RB adjustment strategies based on the results obtained from the leasing module, aiming to ensure IIL. Here, we design a potential game based on the Lagrangian relaxation (LR) method to obtain an approximate optimal solution while reducing computational complexity. Compared with camp enumeration (CE), solver (MOSEK), and genetic algorithm (GA), the proposed ISRLA reduces simulation time by up to 84%, 95%, and 99%, respectively. When the number of slices is 4, the performance of ISRLA is the same as that of CE. Moreover, the IIL gap between ISRLA and MOSEK is less than 4.5%. The simulation results show that the proposed ISRLA can obtain the approximate optimal solution with low computational complexity.

Relay-aided Device-to-Device (D2D) communication combining visible light communication (VLC) with radio frequency (RF) is a promising paradigm in the internet of things (IoT). Static relay limits the flexibility and maintaining connectivity of relays in Hybrid VLC/RF IoT systems. By using a drone as a relay station, it is possible to avoid obstacles such as buildings and to communicate in a line-of-sight (LoS) environment, which naturally aligns with the requirement of VLC Systems. To further support the application of VLC in the IoT, subject to the challenges imposed by the constrained coverage, the lack of flexibility, poor reliability, and connectivity, drone relay-aided D2D communication appears on the horizon and can be cost-effectively deployed for the large-scale IoT. This paper proposes a joint resource allocation and drones relay selection scheme, aiming to maximize the D2D system sum rate while ensuring the quality of service (QoS) requirements for cellular users (CUs) and D2D users (DUs). First, we construct a two-phase coalitional game to tackle the resource allocation problem, which exploits the combination of VLC and RF, as well as incorporates a greedy strategy. After that, a distributed cooperative multi-agent reinforcement learning (MARL) algorithm, called WoLF policy hill-climbing (WoLF-PHC), is proposed to address the drones relay selection problem. Moreover, to further reduce the computational complexity, we propose a lightweight neighbor–agent-based WoLF-PHC algorithm, which only utilizes historical information of neighboring DUs. Finally, we provide an in-depth theoretical analysis of the proposed schemes in terms of complexity and signaling overhead. Simulation results illustrate that the proposed schemes can effectively improve the system performance in terms of the sum rate and outage probability with respect to other outstanding algorithms.

Analysis of urban area function is crucial for urban development. Urban area function features can help to conduct better urban planning and transportation planning. With development of urbanization, urban area function becomes complex. In order to accurately extract function features, researchers have proposed multisource data mining methods that combine urban remote sensing and other data. Therefore, the research of efficient multisource data analysis tools has become a new hot topic. In this paper, a novel urban data analysis method combining spatiotemporal wireless network data and remote sensing data was proposed. First, a Voronoi-diagram-based method was used to divide the urban remote sensing images into zones. Second, we combined period and trend components of wireless network traffic data to mine urban function structure. Third, for multisource supported urban simulation, we designed a novel spatiotemporal city computing method combining graph attention network (GAT) and gated recurrent unit (GRU) to analyze spatiotemporal urban data. The final results prove that our method performs better than other commonly used methods. In addition, we calculated the commuting index of each zone by wireless network data. Combined with the urban simulation conducted in this paper, the dynamic changes of urban area features can be sensed in advance for a better sustainable urban development.

The integration of mobile edge computing (MEC) and network slicing can provide low-latency and customized services. In such integrated wireless networks, we propose a pricing-driven joint MEC server orchestration and network slice deployment scheme (PD-JSOSD), jointly solving the pricing, MEC server orchestration and network slicing deployment issues. We divide the system into an infrastructure provider layer (IPL), network planning layer (NPL) and resource allocation layer (RAL), and a three-stage Stackelberg game is proposed to describe their relationships. To obtain the Stackelberg equalization, we propose a three-layer deep reinforcement learning (DRL) algorithm. Specifically, the dueling double deep Q-network (D3QN) is used in the IPL, and the DRL with branching dueling Q-network (BDQ) is used in the NPL and the RAL to cope with the large-scale discrete action spaces. Moreover, we propose an innovative illegal action modification algorithm to improve the convergence of the BDQ. Simulations verify the convergence of the three-layer DRL and the superiority of modified-BDQ in dealing with large-scale action spaces, where modified-BDQ can improve the convergence by 21.9% and 28.3%. Furthermore, compared with the benchmark algorithms, JSOSD in the NPL and the RAL can improve system utility by up to 52.1%, proving the superiority of the server orchestration and slice deployment scheme.

The vehicular slicing network with dynamic multi-service (VSN-DMS) enables vehicles to support multiple services with dynamically changing types. In this architecture, vehicles can access multiple base stations (BSs) and leverage different network slices (NSs) to meet the requirements of various services. However, frequent service variations and vehicle mobility introduce high handover (HO) costs and inefficient decision making. This letter proposes a novel two-timescale optimization framework to jointly optimize vehicle service (VS) association and subcarrier (SC) allocation. Specifically, in the long-timescale, we propose a federated deep re-inforcement learning (FDRL) algorithm to optimize VS association. Unlike conventional reinforcement learning approaches that require centralized data collection, FDRL enables distributed model training across vehicles while aggregating parameters in the cloud. This mitigates the issue of insufficient training data caused by frequent VS changes, while also ensuring vehicle data privacy. In the short-timescale, we propose a versatile alternating coalitional game and swap-matching game algorithm (ACGSM) to optimize SC allocation. By iteratively executing coalitional formation and swap-matching mechanisms, ACGSM efficiently improves the quality of the solution while adapting to dynamic network conditions. Simulation results demonstrate the convergence of FDRL and show that the proposal outperforms benchmarks in improving long-term QoS and reducing the number of HOs.