This paper studies the multi-agent resource allocation problem in vehicular networks using non-orthogonal multiple access (NOMA) and network slicing. To ensure heterogeneous service requirements for different vehicles, we propose a network slicing architecture. We focus on a non-cellular network scenario where vehicles communicate by the broadcast approach via the direct device-to-device interface. In such a vehicular network, resource allocation among vehicles is very difficult, mainly due to (i) the rapid variation of wireless channels among highly mobile vehicles and (ii) the lack of a central coordination point. Thus, the possibility of acquiring instantaneous channel state information to perform centralized resource allocation is precluded. The resource allocation problem considered is therefore very complex. It includes not only the usual spectrum and power allocation, but also coverage selection (which target vehicles to broadcast to) and packet selection (which network slice to use). This problem must be solved jointly since selected packets can be overlaid using NOMA and therefore spectrum and power must be carefully allocated for better vehicle coverage. To do so, we provide a optimization approach and study the NP-hardness of the problem. Then, we model the problem using multi-agent Markov decision process. Finally, we use a deep reinforcement learning (DRL) approach to solve the problem. The proposed DRL algorithm is practical because it can be implemented in an online and distributed manner. We show that our approach is robust and efficient when faced with different variations of the network parameters and compared to centralized benchmarks.
翻译:本文研究使用非垂直多存取(NOMA)和网络切片的车辆网络的多试剂资源分配问题。 为确保不同车辆的不同服务要求,我们提议了一个网络切片结构。我们侧重于非细胞网络情景,即车辆通过直接设备对设备对设备接口的广播方式进行通信。在这样的车辆网络中,车辆之间的资源分配非常困难,主要原因是:(一)高机动车辆之间无线频道的迅速变化,以及(二)缺乏中央协调点。因此,无法获取即时频道状态信息以实施集中资源分配参数。因此,考虑的资源分配问题非常复杂。它不仅包括通常的频谱和电力分配,还包括覆盖选择(以广播为对象的车辆为对象)和包选择(将使用网络切片)。 这一问题必须共同解决,因为选择的包可以用NOMA过宽,因此频谱和电力必须仔细分配,以便更好的车辆覆盖。为了做到这一点,我们提供了最优化的方法,并研究NP- 集中分配资源配置参数的可能性。因此,所考虑的资源分配问题非常复杂。 不仅包括通常的频谱和网络配置,我们后来采用多路路路段的模型,因为我们学习了多路路的问题。