Energy-Efficient Subchannel Matching and Power Allocation in NOMA Autonomous Driving Vehicular Networks

Abstract

Autonomous driving is the key technology to achieve "smart cars" and "intelligent traffic." The non-orthogonal multiple access (NOMA)-based autonomous driving vehicular (ADV) network is recognized as a promising application scenario in next generation mobile networks. It is also an inevitable trend in the future development of automobiles due to the use of large amounts of fuel and lane occupancy. We propose an architecture of NOMA-based ADV networks to satisfy the communication requirements. In this article, we discuss the challenges and resource allocation problem for NOMA-based ADV networks. In order to improve ADV networks' performance, this architecture also considers cross-layer interference, vehicle quality of service, and algorithm complexity. Therefore, we have investigated the subchannel and power allocation in the NOMA system. The optimization problem is formulated as a non-convex problem for the convenience of calculation, and the problem is transformed into a convex form by introducing an alternative direction algorithm of multipliers method. Simulation results prove the proposed scheme has good feasibility and reliability, and also verify that the proposed algorithm has better energy efficiency compared to the existing schemes.