Spectral Clustering and Deep Reinforcement Learning-Based Dynamic Resource Allocation in SM-MIMO Vehicular System

Considering the inefficient resource allocation (RA) and high quality of service (QoS) requirement in vehicular communications, this paper proposes two dynamic RA algorithms, spectral clustering based greedy (SCGR) algorithm and multi-agent deep reinforcement learning (DRL) algorithm, to maximize both the sum capacity of the vehicle-to-infrastructure (V2I) uplinks and the total energy efficiency (EE) of the vehicle-to-vehicle (V2V) links by assigning the proper power and resource block (RB) to each V2V link in spatial modulation (SM) multiple-input multiple-output (SM-MIMO) vehicular system. For the SCGR algorithm, the spectral clustering (SC) scheme is first utilized to group the V2V links for the suitable RBs. Then, the optimal power is distributed to each V2V link by the greedy (GR) algorithm. For the DRL algorithm, a decentralized model-free network, improved multi-agent deep Q fully connected neural network (IDQFN), is developed to simultaneously find the best power allocation (PA) and RB assignment (RBA). Moreover, the SM technology is exploited to convey the information through the V2I and V2V links and improve the system capacity. Numerical results reveal that the proposed SCGR and IDQFN RA schemes outperform the typical RA algorithms, and the IDQFN scheme achieves better EE than the SCGR scheme, while the SCGR algorithm obtains the optimal average bit error rate (ABER) performance.