Multi-Beam Opportunistic Rate-Splitting Multiple Access in MEC-Assisted Vehicular Networks

Abstract

The evolution of sixth-generation (6G) communication technologies and intelligent transportation has driven vehicular networks toward low latency and high connectivity. Mobile edge computing (MEC) has been widely recognized as a promising paradigm for supporting the diverse service requirements of vehicular networks. However, energy consumption, latency constraints and resource limitations pose prominent challenges to MEC-assisted vehicular networks. The rate-splitting multiple access (RSMA) framework can achieve efficient multi-user multiplexing through message splitting and obtain a satisfactory rate of task offloading. To reduce the dependence on perfect channel state information (CSI), we introduce opportunistic beamforming (OBF) and multi-beam selection into the RSMA framework. In the proposed multi-beam opportunistic RSMA (MB-ORSMA) system, multiple OBF vectors are generated across training slots, and the favorable beam is selected according to the equivalent SINR for task offloading. By exploiting both the user selection gain from candidate vehicles and the beam selection gain from multiple OBF vectors, MB-ORSMA improves the equivalent channel quality without full CSI feedback. An energy minimization problem is formulated by jointly optimizing task division, RSMA power splitting, MEC resource allocation, and training-slot selection. Then, an iterative energy cost minimization (IECM) algorithm is developed. Simulation results verify that the proposed MB-ORSMA system can reduce the total energy consumption in MEC-assisted vehicular networks.