Structural Knowledge-Driven Meta-Learning for Task Offloading in
  Vehicular Networks with Integrated Communications, Sensing and Computing
Ruijin Sun Yao Wen Nan Cheng Wei Wan Rong Chai Yilong Hui
Task offloading is a potential solution to satisfy the strict requirements of
computation-intensive and latency-sensitive vehicular applications due to the
limited onboard computing resources. However, the overwhelming upload traffic
may lead to unacceptable uploading time. To tackle this issue, for tasks taking
environmental data as input, the data perceived by roadside units (RSU)
equipped with several sensors can be directly exploited for computation,
resulting in a novel task offloading paradigm with integrated communications,
sensing and computing (I-CSC). With this paradigm, vehicles can select to
upload their sensed data to RSUs or transmit computing instructions to RSUs
during the offloading. By optimizing the computation mode and network
resources, in this paper, we investigate an I-CSC-based task offloading problem
to reduce the cost caused by resource consumption while guaranteeing the
latency of each task. Although this non-convex problem can be handled by the
alternating minimization (AM) algorithm that alternatively minimizes the
divided four sub-problems, it leads to high computational complexity and local
optimal solution. To tackle this challenge, we propose a creative structural
knowledge-driven meta-learning (SKDML) method, involving both the model-based
AM algorithm and neural networks. Specifically, borrowing the iterative
structure of the AM algorithm, also referred to as structural knowledge, the
proposed SKDML adopts long short-term memory (LSTM) network-based meta-learning
to learn an adaptive optimizer for updating variables in each sub-problem,
instead of the handcrafted counterpart in the AM algorithm.