Hypergraph-Based Interference Avoidance Resource Management in Customer-Centric Communication for Intelligent Cyber-Physical Transportation Systems

In customer-centric communication for intelligent cyber-physical transportation systems (ICTS), the extensive deployment of customer electronics will lead to massive overlapping interference, consequently constraining the network capacity. To solve the problem, we design an interference avoidance resource allocation (IARA) strategy based on a hypergraph in customer-centric communication for ICTS. The overlapping interference relationship between customers is modeled in the interference model by hypergraph theory, the IARA under complex interferences is generalized to a hypergraph vertex strong coloring problem. To achieve IARA, a deep Q-network (DQN) algorithm based on reinforcement learning (RL) is proposed to avoid the conflicts of hypergraph coloring. Moreover, for maximizing the network capacity, a robust allocation algorithm with channel state information (RAA-CSI) is proposed for customer-centric communication in ICTS. Simulation results show that compared with the comparison algorithms, the proposed algorithm can average an increase of 25% in the resource reuse rate and effectively improve network capacity for customer-centric communication in ICTS.