Intelligent Multi-Dimensional Resource Allocation in MVNETs

In this chapter, we study the joint allocation of the spectrum, computing, and caching resources in MVNETs. To support different vehicular applications, we consider two typical MEC architectures and formulate multi-dimensional resource optimization problems accordingly. Since the formulated problems are usually with high computation complexity and overlong problem-solving time due to high vehicle mobility and the complex vehicular communication environment, we exploit RL to transform them into MDPs and then solve them by leveraging the DDPG and hierarchical learning architectures. Via off-line training, the network dynamics can be automatically learned and appropriate resource allocation decisions can be rapidly obtained to satisfy the QoS requirements of vehicular applications. From simulation results, the proposed resource management schemes can achieve high delay/QoS satisfaction ratios.