Energy-efficient resource allocation in NOMA-integrated V2X networks

• Published in: Computer communications Vol. 197; pp. 23 - 33
• Main Authors: Shan, Liqing, Gao, Songtao, Chen, Shuaishuai, Xu, Mingkai, Zhang, Fenghui, Bao, Xuecai, Chen, Ming
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2023
• Subjects: Device-to-Device (D2D); Energy efficiency (EE); Non-orthogonal multiple access (NOMA); Resource allocation; Vehicle-to-Everything (V2X); Vehicle-to-Everything (V2X); Energy efficiency (EE); Device-to-Device (D2D); Non-orthogonal multiple access (NOMA); Resource allocation
• ISSN: 0140-3664; 1873-703X
• DOI: 10.1016/j.comcom.2022.10.005

In Vehicle-to-Everything (V2X) communication networks, cellular Device-to-Device (D2D) communication can improve the spectrum efficiency, and non-orthogonal multiple access (NOMA) can further enhance the connection density. However, for the cellular D2D communications, the involved execution condition for NOMA may be affected by the additional interference introduced by cellular links in V2X. In this paper, we study the energy-efficient resource allocation problem in cellular D2D-aided V2X networks with NOMA. To efficiently meet the quality of service (QoS) requirements while taking into account the maximum performance from the user’s perspective, we are committed to maximizing the minimum energy efficiency (EE) of each matching link, by jointly optimizing power allocation and spectrum reusing at Vehicle-to-Infrastructure (V2I) links and cellular D2D-based Vehicle-to-Vehicle (V2V) links. Since this problem is mathematically mixed-integer and non-convex, we first develop a two-layer block coordinate descent (BCD) scheme to solve power allocation subproblem. Specifically, the optimal intra-group power allocation strategy is derived in the inner-layer. Then, Dinkelbach and concave–convex procedure (CCCP) are employed to tighten the lower bound of original problem in the outer-layer. Followed by this, we address the remaining spectrum sharing subproblem based on the principle of minimum EE maximization. Simulation results show that the proposed algorithm can achieve excellent performance and outperform the other benchmark schemes significantly.