With the emergence of information overload in the Social Internet of Things (SIoT), personalized recommender systems have become essential for helping users locate the items they need. Effectively modeling the heterogeneous relationships across multiple information sources and weighting them according to their varying importance, especially under sparse social interactions in the SIoT, remains a key challenge. To address this issue, this paper proposes a Multi-source Adaptive Relational Graph Convolutional Network (MARGCN) framework for recommender systems. Useruser and useritem interactions are modeled as two relational graphs, and their embedded features are aggregated using graph convolutional networks (GCNs). A multi-source relationship perception model is designed to dynamically perceive and measure the importance of multiple relationships within the graph structure, thereby enhancing the ability to recognize heterogeneous information. An adaptive information fusion model is then constructed to dynamically integrate representations from different sources through a learnable fusion strategy, avoiding information loss or redundancy caused by simple weighting. Users and items are ultimately represented by aggregating and updating their embeddings via GCNs. Experiments show that compared with the most advanced methods, MARGCN improves the hit rate (HR) and the normalized discounted cumulative gain (NDCG) by 2.66%, 0.52%, and 3.93% in HR@5, HR@10, and HR@15 and by 1.58%, 1.32%, and 3.14% in NDCG@5, NDCG@10, and NDCG@15, respectively.

The unmanned aerial vehicle-assisted 6G supported intelligent transportation systems (UAV-assisted 6G-ITS) have great potential to make transportation systems efficient, smart, and sustainable. However, when connected and autonomous vehicles communicate with UAVs, it can lead to issues such as energy consumption and overlapping interference, which can affect system performance. Therefore, this paper proposes an interference tolerance-based energy harvesting (EH) resource allocation (IT-EHRA) strategy, aiming to improve the energy efficiency (EE) of UAV-assisted 6G-ITS and mitigate the overlapping interference. Firstly, we established an interference hypergraph model and analyzed the types and relationships of interference in the network. Based on this model, an interference tolerance method was designed to alleviate overlapping interference. Then, an EH optimization model with imperfect channel state information (CSI) is proposed based on the interference model, aiming to maximize the network EE and reduce energy consumption. Finally, we adopt the IT-EHRA algorithm to reduce the impact of imperfect CSI and use duality theory to obtain the optimal solution, thereby achieving maximum network EE. Simulation results show that the algorithm effectively ensures the EE requirements of the network, improves the throughput of the network, and promotes the sustainable development of the network.

In an ultra-dense Industrial Internet of Things (UDI-IoT) network with device-to-device (D2D) communication technology applied, interaction among a large number of industrial Internet of Things devices (IIoTDs) leads to heavily overlapping interference, and Age-of-information (AoI) sensitive service in the network is difficult to guarantee. In this paper, a learning-based robust resource allocation considering overlapping interference and AoI-sensitive services requirements (LRRA-OIAoISR) is proposed to solve the problem of AoI resource management for the UDI-IoT networks with overlapping interference. We first construct the interference hypergraph model and analyze the interference relationship between D2D devices, which can effectively improve the utilization of spectrum resources. Then, an AoI model was constructed to address the resource management issues of AoI-sensitive services, and a robust optimization model was established under imperfect CSI. This model considers power control and resource conflict constraints to achieve maximum network throughput. Finally, to solve this robust optimization problem, we propose an LRRA-OIAoISR algorithm based on learning theory. The algorithm obtains an optimal robust optimization solution by reducing the impact of imperfect CSI. The performance of the proposed resource allocation method was verified through simulation. Compared with other benchmark algorithms, the proposed algorithm improves energy efficiency by an average of 52.5%, interference efficiency by an average of 88.5%, and throughput by an average of 125%.

As the number of devices increases dramatically in the Internet of Things (IoT), features of dense deployment of massive devices generate mutual interference in communication overlapping areas, which will impose an imperative challenge on spectrum resource allocation. To handle this challenge, this article proposes a mutual interference-aware throughput enhancement scheme. For the mutual interference among multiple IoT devices, this scheme first builds an interference hypergraph model to quantify the impact of the mutual interference for each device. According to the main goal of the spectrum resource allocation, this article formulates a graph reinforcement learning (GRL) framework, whose action space is multidimensional discrete, and the reward function is designed to enhance the throughput and mitigate the impact of interference. Then, a graph convolutional network-double dueling deep Q-network-based spectrum resource allocation algorithm is developed upon the proposed GRL framework to extract the mutual interference information from the hypergraph model, and then achieve a dynamic resource allocation for massive IoT. Simulation results prove that the proposed GRL algorithm effectively improves the network throughput compared to the comparison algorithms.

In unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) networks, the communication ranges between densely deployed IoT devices overlap, resulting in wireless resource conflicts between them. Hence, achieving conflict-free resource allocation is a challenging issue that must be urgently addressed for UAV-enabled IoT networks. To tackle this issue, a hypergraph is used to quantify conflicts, and a federated reinforcement learning (RL)-based resource allocation framework is proposed. Specifically, a conflict graph model is developed for UAV-enabled IoT networks with dense deployments. The model is then converted into a conflict hypergraph model using hypergraph and faction theory. Consequently, the conflict avoidance problem of resource allocation can be reformulated as a hypergraph node coloring problem. The problem is formulated as a Markov decision process, which is solved using a deep RL-based approach. Additionally, to distribute the computational workload across the network and alleviate the burden on the central server, we propose the FedAvg dueling double deep Q-network (FedAvg-D3QN). The proposed FedAvg-D3QN is verified through simulation to have advantages in resource reuse rate and throughput compared to baseline approaches.

In the distributed Cognitive Industrial Internet of Things (CIIoT), since industrial devices may self-organize to determine their connection and dispersion and the network may be distributed with no infrastructure, network management with self-organizing characteristics is still an unsolved and a difficult problem. To overcome this challenge, this paper proposes a hierarchical network management strategy for distributed CIIoT with imperfect channel state information (CSI) considering services with different transmission delay requirements. First, we build a two-layer architecture that supports collaborative spectrum sensing and multi-node spectrum sharing in a distributed CIIoT. Then, to improve the network management efficiency and reduce the number of backbone nodes (BNs) while achieving spectrum sharing for massively distributed nodes, we establish a minimum backbone node set mathematical model and design the simplest backbone network optimization algorithm (SBNOA). For the problem of spectrum sharing with imperfect CSI while considering the delay-sensitive industrial services, we establish a time delay model that incorporates delay-sensitive and delay-tolerant services. Based on the model, we establish a robust optimization model with multiple conditional aim-listed probability constraints that consider imperfect CSI and transform the stochastic optimization problem into a convex optimization by using the quadratic transformation method and Gaussian Q function to solve it. Simulation results show that the proposed algorithm has good performance in a distributed CIIoT.

For the future 6G-enabled massive Internet of Things (mIoT), how to effectively manage spectrum resources to support huge data traffic under the large-scale overlapping caused by the dense deployment of massive devices is the imperative challenge. In this paper, a novel hypergraph interference model is designed, and two reinforcement learning (RL)-based resource management algorithms in the 6G-enabled mIoT are proposed to enhance the network throughput and avoid overlapping interference. Then, based on the hypergraph interference model, the resource management problem of execution network throughput maximization is theoretically formulated under large-scale overlapping interference scenarios. To handle this problem, we convert it into a Markov decision process (MDP) model and then deal with this MDP model through the advantage actor-critic (A2C)-based resource management algorithm and asynchronous advantage actor-critic (A3C)-based resource management algorithm, which aim to maximize network throughput of the spectrum resource allocation among massive devices. The simulation results verify that the proposed algorithms can not only avoid large-scale overlapping interference but also improve the network throughput.

A smart city involves big data transmission (BDT) between smart systems, which increases queue delays and leads to difficulty in enhancing the spectral efficiency. Adaptive modulation is an effective technique for enhancing data transmission rates in smart systems. However, traditional adaptive modulation approaches are not suitable for BDT in smart systems because the delays caused by the large amount of transmitted data lead to difficulty in evaluating the channel quality. In this paper, we propose a nondata-aided error vector (NDA-EVM) that can be employed in adaptive modulation over wireless channels. The proposed NDA-EVM can be used to evaluate the channel quality and symbol error rate (SER), which reflect the quality of service (QoS) of the system. We formulated the relationship between the NDA-EVM and SER, which provides a basis for designing adaptive modulation techniques for smart systems. To address the low average spectrum efficiency (ASE) caused by BDT queue delays, an adaptive modulation strategy based on the finite-state Markov chain (FSMC) of the NDA-EVM (i.e., NDA-EVM-AM) was designed. This method simplifies the adaptive modulation algorithm for smart systems to search for the optimal transfer probability in the FSMC matrix based on two typical states: the resident state and transient state. Moreover, we proposed an analytical procedure to describe queuing behavior to analyze the performance of the NDA-EVM-AM algorithm for smart systems in smart cities. The performance is compared with that of a conventional adaptive modulation algorithm through simulations. The results show that compared with traditional adaptive modulation, NDA-EVM-AM obtains a lower packet loss rate and higher spectral efficiency for smart systems.