Knowledge-Driven Resource Allocation for Efficient Task Offloading in Connected Autonomous Vehicles

Task offloading is a potential solution for computation-intensive vehicular applications due to limited on-board computing resources. However, traditional model-driven methods are hindered by long online processing time, while data-driven methods are deficient in interpretability and generalizability. To overcome this challenge, this paper formulates the resource allocation for task offloading in connected autonomous vehicles (CAVs) as a multi-objective optimization problem, and proposes a novel knowledge-driven algorithm that integrates both model-driven and data-driven methods. Specifically, the framework of a model-driven alternating minimization (AM) algorithm, which solves the formulated problem via alternatively optimizing power allocation subproblem and bandwidth and CPU frequency allocation subproblem, is regarded as knowledge. Inspired by such knowledge, our proposed knowledge-driven neural network consists of two long short term memory networks (LSTMs) to alternatively updating these two subproblems. Furthermore, to get away from the local optimum usually occurred in the AM algorithm, our proposed knowledge-driven neural network updates network parameters with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the LSTM without knowledge.