In recent years, the concept of non-orthogonal multiple access (NOMA) has gathered much attention due to its potential to offer high spectral efficiency, present user fairness and grant free access to sixth generation (6G) vehicular networks. This paper proposes a new optimization framework for NOMA-enabled cooperative vehicular network. In particular, we jointly optimize the vehicle paring, channel assignment, and power allocation at source and relaying vehicles. The objective is to maximize the sum rate of the system subject to the power allocation, minimum rate, relay battery lifetime and successive interference cancelation constraints. To solve the joint optimization problem efficiently, we adopt duality theory followed by Karush-Kuhn-Tucker (KKT) conditions, where the dual variables are iteratively computed through sub-gradient method. Two less complex suboptimal schemes are also presented as the benchmark cooperative vehicular schemes. Simulation results compare the performance of the proposed joint optimization scheme compared to the other benchmark cooperative vehicular schemes.

中文翻译：

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优化支持 6G NOMA 的协同车载网络的资源分配

近年来，非正交多址 (NOMA) 的概念因其提供高频谱效率、呈现用户公平性和允许免费访问第六代 (6G) 车载网络的潜力而备受关注。本文为支持 NOMA 的协同车载网络提出了一种新的优化框架。特别是，我们联合优化了源和中继车辆的车辆配对、信道分配和功率分配。目标是在功率分配、最小速率、中继电池寿命和连续干扰消除约束下最大化系统的总速率。为了有效地解决联合优化问题，我们采用对偶理论，遵循 Karush-Kuhn-Tucker (KKT) 条件，其中对偶变量通过次梯度方法迭代计算。还提出了两个不太复杂的次优方案作为基准协作车辆方案。仿真结果比较了所提出的联合优化方案与其他基准合作车辆方案的性能。