Tiecheng Song’s research while affiliated with Southeast University and other places

In the evolution of the Internet of vehicles (IoV), the increasing demand for vehicular computation tasks presents significant challenges, particularly in the context of constrained local computation resources and high processing delays. To mitigate these challenges, multi-access edge computing (MEC) offers a potential solution by leveraging edge servers for lowlatency processing. However, it also encounters issues such as sub-channel competition and workload imbalance owing to the uneven distribution of vehicle densities. This paper introduces a novel IoV architecture that incorporates multi-task and multi-roadside unit (RSU) capabilities, enabling edge-toedge collaboration for efficient task offloading among RSUs. The optimization problem is formulated with the objective of minimizing the overall task delay, which is further divided into two sub-problems: communication resource allocation and load balancing. Considering the non-deterministic polynomial (NP)- hard nature of these sub-problems, we propose a two-stage deep reinforcement learning-based communication resource allocation and load balancing (DRLCL) algorithm to address them sequentially. Based on realistic vehicle trajectories, comprehensive evaluation results demonstrate the superiority of the proposed algorithm in reducing system delay compared to existing stateof-the-art baselines, offering an effective approach for optimizing the performance of vehicular edge computing (VEC) networks.

Vehicular networks face significant challenges in achieving high energy efficiency (EE) while guaranteeing diverse quality of service (QoS) requirements of users, especially under limited bandwidth and power budgets in highly dynamic and dense topologies. To address these challenges, this study formulates a joint resource optimization problem to maximize the average EE of cellular users (CUs) and vehicle-to-vehicle (V2V) users by jointly optimizing subchannel assignment, frequency reuse patterns, and power allocation while ensuring the required QoS of both users. To solve the non-convex optimization problem, we propose a semi-persistent scheduling (SPS)-based energy-efficient resource allocation scheme that integrates non-orthogonal multiple access (NOMA) with network slicing (NS). Specifically, during the frequency reservation phase of SPS periods, CUs are assigned to network slices using the proposed NS grouping strategy, and V2V users are clustered into V2V NOMA clusters using the proposed clustering optimization algorithm. Frequency reuse patterns are then determined for network slices and V2V NOMA clusters. In the subsequent data transmission phase, a centralized energy-efficient iterative power control algorithm is introduced to enhance the average CU EE, and a distributed heuristic power control method is leveraged to improve the average V2V EE. Simulation results demonstrate that the proposed scheme outperforms the baseline methods in improving EE and satisfying the required QoS of both CUs and V2V users while avoiding over-allocation of frequency resources.

In urban environments, direct communication links between a base station (BS) and user equipment (UEs) are often obstructed by buildings. To mitigate these blockages, we integrate unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance system flexibility and improve transmission efficiency. This paper investigates an RIS-assisted multi-user multiple-input single-output (MU-MISO) downlink system, where the RIS is mounted on a UAV. To maximize the system rate while minimizing the UAV's energy consumption and flight duration, we formulate a multi-objective optimization problem. To address this problem, we propose a hybrid algorithm that integrates the soft deep deterministic policy gradient (SD3) algorithm with a graph neural network (GNN) architecture, named SD3-GNN-RIS. The original problem is decomposed into two subproblems: joint active beamforming at the BS and passive beamforming at the RIS, optimized via a GNN-based approach, and three-dimensional (3D) UAV trajectory optimization, formulated as a Markov decision process and solved using the SD3 algorithm. Simulation results demonstrate the superior performance of the proposed algorithm compared to baseline methods in terms of system rate, energy efficiency, and UAV trajectory optimization.

The collaboration between unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) presents an innovative approach for delay-tolerant data harvesting in distributed Internet of Things (IoT) networks. However, existing research mostly overlooks the dynamic changes in communication links caused by the real-time UAV movement and the realistic geographical features. In this paper, we address these challenges by considering a practical three-dimensional (3D) urban scenario with a centralized IRS. Our aim is to minimize the completion time of data harvesting missions by jointly optimizing the 3D trajectory of the UAV and the phase shift of the IRS. Specifically, the formulated problem is decoupled into two subproblems. First, for the 3D continuous trajectory design, we propose a robust memory-based softmax deep double deterministic policy gradients (MSD3) approach, which enables the UAV to adaptively collect delay-tolerant data from randomly distributed ground devices starting from any arbitrary point. Second, we present a comprehensive theoretical analysis for the continuous IRS phase control, which provides a practical and intuitive numerical solution. Simulation results demonstrate that the proposed MSD3-IRS algorithm outperforms other mainstream baselines based on deep reinforcement learning.

In this work, we generalize the Stochastic Hybrid Systems (SHSs) analysis of traditional AoI to the AoII metric. Hierarchical ageing processes are adopted using the continuous AoII for the first time, where two different hierarchy schemes, i.e., a hybrid of linear ageing processes with different slopes and a hybrid of linear and quadratic ageing processes, are considered. We first modify the main result in \cite[Theorem 1]{yates_age_2020b} to provide a systematic way to analyze the continuous hierarchical AoII over unslotted real-time systems. The closed-form expressions of average hierarchical AoII are obtained based on our Theorem \ref{theorem1} in two typical scenarios with different channel conditions, i.e., an M/M/1/1 queue over noisy channel and two M/M/1/1 queues over collision channel. Moreover, we analyze the stability conditions for two scenarios given that the quadratic ageing process may lead to the absence of stationary solutions. Finally, we compare the average age performance between the classic AoI results and our AoII results in the M/M/1/1 queue, and the effects of different channel parameters on AoII are also evaluated.

The proliferation of computation-intensive and delay-sensitive services in intelligent transportation systems, such as autonomous driving and vehicle-mounted infotainment services, presents a significant challenge for vehicular users (VUs) with limited resources. To address this issue, multi-access edge computing (MEC) has been considered a favorable solution to mitigate computation delay. This paper considers computation offloading for an air-ground integrated computing platform in vehicular networks. Specifically, we first propose a multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm to optimize the trajectory of UAVs. Then, an algorithm named federated upgraded dueling double deep Q network (FUD3QN) is proposed to meet quality of service (QoS) requirements. The algorithm allocates cross-domain resources after offloading decision-making, aiming to minimize delay and energy consumption while meeting reliability requirements, maximum tolerable delay, communication requirements, and computing limitations. Addressing the NP-hard problem, we employ a multi-agent federated learning and upgraded dueling double deep Q network algorithm (UD3QN) with centralized training and distributed execution. Simulation results illustrate that the MATD3-FUD3QN algorithm proposed significantly surpasses the baselines, highlighting the advantages of introducing UAVs to enhance transmission quality.

The Internet of Vehicles (IoV) will carry a large amount of security and privacy-related data, which makes the secure communication between the IoV terminals increasingly critical. This paper studies the joint beamforming for physical-layer security transmission in the coexistence of Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication with Reconfigurable Intelligent Surface (RIS) assistance, taking into account hardware impairments. A communication model for physical-layer security transmission is established when the eavesdropping user is present and the base station antenna has hardware impairments assisted by RIS. Based on this model, we propose to maximize the V2I physical-layer security transmission rate. To solve the coupled non-convex optimization problem, an alternating optimization algorithm based on second-order cone programming and semidefinite relaxation is proposed to obtain the optimal V2I base station transmit precoding and RIS reflect phase shift matrix. Finally, simulation results are presented to verify the convergence and superiority of our proposed algorithm while analyzing the impact of system parameters on the V2I physical-layer security transmission rate. The simulation results further demonstrate that the proposed robust beamforming algorithm considering hardware impairments will achieve an average performance improvement of 0.7 dB over a non-robustly designed algorithm. Furthermore, increasing the number of RIS reflective units from 10 to 50 results in an almost 2 dB enhancement in secure transmission rate.

Edge caching has emerged as an effective solution to the challenges posed by massive content delivery in the vehicular network. In vehicular networks, vehicles and roadside units (RSUs) can serve as intermediate relays with caching capabilities. However, due to the mobility of vehicles, the topology of the edge network changes frequently, which leads to frequent link interruptions and increases the transmission delay. This paper proposes an adaptive cooperative caching (ACC) strategy to adapt the frequent changes in the vehicular edge network topology and describes an optimization problem to minimize the average transmission delay. Then, the optimization problem is transformed into two sub-optimization problems: multiplechoice knapsack (MCK) problem and multiple minimum-weight dominating set (MMWDS) problem. Finally, two greedy algorithms with low complexity are designed to solve the above two optimization problems and obtain approximate solutions to the optimal caching decision. Simulation results show that ACC can effectively improve the cache hit rate and reduce the average transmission delay and the communication overhead compared with other caching strategies.

With the rapid development of the Internet of Vehicles (IoV), the cognitive radio-based IoV (CIoV) network is widely used to overcome the lack of spectrum resources. In this paper, a novel joint spectrum sensing and resource allocation (JSS-RA) scheme in the CIoV system is investigated. The JSS-RA process is divided into two phases. In the spectrum sensing phase, our scheme considers the dynamic vehicular environment to adjust the decision threshold individually for each vehicle. In the resource allocation (RA) phase, we first propose a general utility-efficiency function. The various parameters contained in this function enable its transformation into many mainstream objective functions. Subsequently, the RA issue is formulated as a joint orthogonal frequency division multiplexing (OFDM) symbol and power optimization, which is also known as a mixed integer nonlinear programming (MINLP). We solve the original optimization by decomposing it into two subproblems. The theorems prove that the global optimum solution can be obtained by combining the results of two decomposed subproblems. Besides, the convergence rate of the proposed algorithm is discussed. Finally, numerical simulations verify the excellent performance and robustness of our JSS-RA scheme.