Jian Sun

To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication (JRC) systems under dynamic environments, an intelligent optimization framework integrating Deep Reinforcement Learning (DRL) and Graph Neural Network (GNN) is proposed. This framework models resource allocation as a Partially Observable Markov Game (POMG), designs a weighted reward function to balance radar and communication efficiencies, adopts the Multi-Agent Proximal Policy Optimization (MAPPO) framework, and integrates Graph Convolutional Networks (GCN) and Graph Sample and Aggregate (GraphSAGE) to optimize information interaction. Simulations show that, compared with traditional methods and pure DRL methods, the proposed framework achieves improvements in performance metrics such as communication success rate, Average Age of Information (AoI), and policy convergence speed, effectively enabling resource management in complex environments. Moreover, the proposed GNN-DRL-based intelligent optimization framework obtains significantly better performance for resource management in multi-agent JRC systems than traditional methods and pure DRL methods.

With the advancement of Vehicle-to-Everything (V2X) technology, efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance. Existing methods suffer from high computational complexity and decision latency under high-density traffic and heterogeneous network conditions. To address these challenges, this study presents an innovative framework that combines Graph Neural Networks (GNNs) with a Double Deep Q-Network (DDQN), utilizing dynamic graph structures and reinforcement learning. An adaptive neighbor sampling mechanism is introduced to dynamically select the most relevant neighbors based on interference levels and network topology, thereby improving decision accuracy and efficiency. Meanwhile, the framework models communication links as nodes and interference relationships as edges, effectively capturing the direct impact of interference on resource allocation while reducing computational complexity and preserving critical interaction information. Employing an aggregation mechanism based on the Graph Attention Network (GAT), it dynamically adjusts the neighbor sampling scope and performs attention-weighted aggregation based on node importance, ensuring more efficient and adaptive resource management. This design ensures reliable Vehicle-to-Vehicle (V2V) communication while maintaining high Vehicle-to-Infrastructure (V2I) throughput. The framework retains the global feature learning capabilities of GNNs and supports distributed network deployment, allowing vehicles to extract low-dimensional graph embeddings from local observations for real-time resource decisions. Experimental results demonstrate that the proposed method significantly reduces computational overhead, mitigates latency, and improves resource utilization efficiency in vehicular networks under complex traffic scenarios. This research not only provides a novel solution to resource allocation challenges in V2X networks but also advances the application of DDQN in intelligent transportation systems, offering substantial theoretical significance and practical value.

With the development of the Internet of Things (IoT) and intelligent transportation systems (ITS), vehicle ad hoc networks (VANETs) are prone to network congestion and link instability caused by the high mobility of vehicles. Therefore, introducing efficient routing technologies between vehicles can reduce delay and improve reliability. The high-speed mobility of vehicle nodes leads to the rapid change of network topology. Therefore, not only location and speed, but also distance, direction and other parameters should be considered during selecting route weight factors. Given the inefficient routing selection and link instability, this paper proposes a traversal peripheral weight routing selection algorithm. According to the established paths from the source node to the destination node, the algorithm selects the optimal path according to four weight factors: distance between vehicles, the angle value of the vehicle relative to the horizontal direction, link interruption rate, and traffic density. The simulation results show that compared with traditional routing algorithms, the proposed algorithm improves the data transmission rate and link survival time by 25.12\% and 17.68\%, respectively.

With the rapid popularity of smart terminals, the device-to-device(D2D) communication is recognized as one of the most promising techniques in the fifth-generation(5G) communications networks due to its capabilities of substantially improving the spectral efficiency, relieving the traffic burden in the base stations(BSs), and reducing the terminals’ power consumptions, etc. By properly activating some D2D links(DLs), the sum data rate of the D2D-aided cellular networks(CNs) can be substantially improved. However, constrained by the severe interference imposed on the conventional cellular users (CUs) by the activating D2D users (DUs), the sum data rate of the D2D-aided CNs cannot be unlimitedly improved in a crude way of simply increasing the number/density of DLs. In other words, there must exist a maximum/optimal number of activating DLs in terms of the sum data rate, as revealed in this paper. By identifying the maximum/optimal number of activating DLs, the closed-form expressions for sum data rates of both cellular links(CLs) and DLs can be obtained. Numerical results show that the optimum number of activating DLs, as a function of several critical parameters such as DU’s transmit power, signal-to-interference-plus-noise ratio threshold and outage probability of CLs/DLs, etc, can be determined by implementing the proposed algorithms.

As an important complement and enhancement technique for cellular communication systems, the device-to-device (D2D) technique has exhibited a number of benefits in terms of, for instance, offering an improved spectral efficiency, enabling a reduced transmit power and providing an enhanced channel capacity, etc. However, a severe interference may be imposed on the conventional cellular users by the activated D2D links, thus, substantially eroding the performance of the former. In this paper, we propose an interference limited area (ILA) based D2D-management scheme along with an appropriately designed power control algorithm for mitigating the above-mentioned interference. We first derive the closed-form expressions of the average coverage probability for both the cellular and D2D links under the proposed ILA scheme, followed by proposing a resource-allocation algorithm subject to a power control mechanism. Numerical results show that the proposed ILA scheme is capable of outperforming the conventional scheme in terms of both the coverage probability (i.e., for both cellular and D2D links) and the sum data rate, particularly in the scenarios with a high D2D density and a high signal-to-interference-plus-noise ratio.

Once the direct communication conditions are not satisfied between D2D pairs, relay-aided mode may play an important role in enhancing the reliability and reducing the transmit power of D2D communications. In order to obtain the maximum system capacity with a low computation complexity, this paper proposes a novel scheme by considering interference limit, optimal relay selection based on delineated area and optimal link selection based range division. Simulation results reveal that the proposed scheme is capable of substantially improving the system performance and reducing the computational complexity compared with either the existing brute- force technique or the area-division scheme.

The device-to-device (D2D) communications mode has been regarded as an effective technique for solving/relieving the contradiction between the exponentially increased data traffic requirements of mobile customers and the essence of scarcity of radio resources in wireless communication networks. In the presence of unfavorable direct communication links between D2D peers, cooperative relays may play an important role in enhancing both reliability and flexibility of D2D communications. Aiming at maximizing the sum throughout of the network with a low computation complexity, a new scheme, which jointly considers a number of critical aspects, such as power control, interference limit based on the location information, optimal relay selection based on delineated area, and optimal link selection based range division, etc., is proposed in this paper. Numerical results reveal that the proposed scheme is capable of substantially improving the system's performance compared with either the existing brute-force technique or the area-division scheme. Furthermore, the proposed scheme also exhibits its advantages over the existing techniques in terms of computational complexity.

The device-to-device (D2D) communication has been regarded as an effective technique for complementing and enhancing the conventional cellular systems owing to its capability of substantially improving both the spectral and power efficiencies of wireless networks. However, the severe interference imposed on the conventional cellular users (CUs) by the geographically close-by D2D pairs may cause a significant performance erosion in the D2D-aided underlaying cellular networks (CNs). In this paper, performance analysis for the D2D-aided underlaying CNs in terms of throughput is provided. We first derive the closed-form expressions of the coverage probability for both the conventional cellular links and the D2D links, followed by giving out the approximated expressions of the ergodic data rate for both an individual cellular/D2D link and the whole underlaying network. Furthermore, the key parameters (e.g., the density of D2D users (DUs) or CUs, and the average geographical distance between a pair of D2D peers) significantly impacting the channel capacity are adaptively adjusted for maximizing the sum data rate of the proposed underlaying networks. In addition, both theoretical analysis and simulation results reveal the attainability of the maximal throughput by optimizing the critical parameters, such as the density of DUs, provided that the scale factor between the DUs and sum users (i.e., comprising both conventional CUs and DUs) can be effectively balanced subject to the constraints specified in the proposed scheme.

The device-to-device (D2D) communication has become one of the most promising methods for alleviating the deficiency of wireless spectrum. In spite of the potential gains brought by activating D2D communications, the problem of severe interference between cellular and D2D users cannot be ignored. In this paper, we introduce the power-control scheme based on interference limited area (ILA) for alleviating the abovementioned interference. In a single cell where there are M cellular user equipments (CUEs) supporting both the conventional cellular mode and the D2D mode, the controlling thresholds λD and λC are defined for the D2D transmitter (DT) and the base station (BS), respectively; we forbid all the DT from transmission within ILA-S1 but activating DT only within the inner section of ILA-S2, where ILA-S1 and ILA-S2 are defined as the areas in which the signal-to- interference ratio (SIR) is higher than λC and λD, respectively. Furthermore, we propose a novel resource-allocation scheme relying on interference control mechanism based on the criteria of "maximum/minimum power of DT". Numerical results show that the proposed scheme is capable of achieving the best system performance in terms of capacity, where the optimal ILA size is attainable.