Enabling Digital Twin in Vehicular Edge Computing: A Multi-Agent Multi-Objective Deep Reinforcement Learning Solution
Xincao Xu, Kai Liu, Penglin Dai, and Biwen Chen^*

• We present a DT-VEC architecture, where the heterogeneous information can be sensed by vehicles and uploaded to the edge node via V2I communications. The DT-VEC are modeled at the edge node, forming a logical view to reflect the physical vehicular environment.
• We model the DT-VEC by deriving an ISAC-assisted sensing model and a reliability-guaranteed uploading model.
• We formulate the bi-objective problem to maximize the system quality and minimize the system cost, simultaneously. In particular, we define the quality of DT-VEC by considering the timeliness and consistency, and define the cost of DT-VEC by considering the redundancy, sensing cost, and transmission cost.
• We propose a multi-agent multi-objective (MAMO) deep reinforcement learning solution implemented distributedly in the vehicles and the edge nodes. Specifically, a dueling critic network is proposed to evaluate the advantage of action over the average of random actions.
• Submitted to IEEE Transactions on Consumer Electronics (under review)

Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-agent Deep Reinforcement Learning Approach
Xincao Xu, Kai Liu^*, Penglin Dai, Ruitao Xie, and Jiangtao Luo

• We present a cooperative sensing and heterogeneous information fusion architecture in VCPS via vehicular edge computing. The heterogeneous information can be sensed via either onboard sensors such as LIDAR, GPS, and cameras, or roadside infrastructures such as traffic lights. The sensed information is queued in vehicles for uploading via the V2I bandwidth, which is allocated by the corresponding edge node. Logical views can be constructed via the information fusion at edge nodes, and different views may be required to enable upper-layer applications.
• We formulate the problem to maximize the quality of VCPS. Specifically, we derive a cooperative sensing model, in which the information queuing and data uploading are modeled based on the multi-class M/G/1 priority queue and the Shannon theory, respectively. Then, we derive a heterogeneous information fusion model by modeling the timeliness, completeness, and consistency of views. On this basis, a new metric called Age of View (AoV) is defined to quantitatively measure the quality of information fusion. Finally, we model the quality of VCPS and present the optimization objective, which is to maximize the VCPS quality.
• We propose a multi-agent deep reinforcement learning solution. Specifically, the solution model is presented, in which vehicles act as independent agents with action space of determining the sensing frequencies and uploading priorities, and the edge action space is the V2I bandwidth allocation. The system state consists of vehicle sensed information, edge cached information, and view requirements. The system reward is defined as the achieved VCPS quality. In particular, the system reward is divided into the Difference Reward (DR) to capture vehicle individual contributions on view constructions by the DR-based credit assignment.
• Submitted to IEEE Transactions on Intelligent Transportation Systems (under review)

Joint Task Offloading and Resource Optimization in NOMA-based Vehicular Edge Computing: A Game-Theoretic DRL Approach
Xincao Xu, Kai Liu^*, Penglin Dai, Feiyu Jin, Hualing Ren, Choujun Zhan, and Songtao Guo

• We present a NOMA-based VEC architecture, where the vehicles share the same frequency of bandwidth resources and communicate with the edge node with the allocated transmission power. The tasks arrive stochastically at vehicles and are heterogeneous regarding computation resource requirements and deadlines, which are uploaded by vehicles via V2I communications. Then, the edge nodes with heterogeneous computation capabilities, i.e., CPU clock speed, can either execute the tasks locally with allocated computation resources or offload the tasks to neighboring edge nodes through a wired network.
• We propose a cooperative resources optimization (CRO) problem by jointly offloading tasks and allocating communication and computation resources to maximize the service ratio, which is the number of tasks serviced before the deadlines divided by the number of requested tasks. Specifically, a V2I transmission model considering both intra-edge and inter-edge interference and a task offloading model considering the heterogeneous resources and cooperation of edge nodes are theoretically modeled, respectively.
• We decompose the CRO problem into two subproblems: 1) task offloading game model and 2) resource allocation convex problem. Specifically, we model the first subproblem as a non-cooperative game among edge nodes, which is further proved as an EPG with the existence and convergence of NE. Then, we design a MAD4PG algorithm, a multi-agent version of D4PG, to achieve the NE, where edge nodes act as independent agents to determine the task offloading decisions and receive the achieved potential as rewards. Further, we model the second subproblem as two independent convex problems and derive an optimal mathematical solution based on the gradient-based iterative method and KKT condition.
• Accepted by Journal of Systems Architecture [JCR Q1|SCI Q2|CCF B]