The dynamic topology of vehicular ad hoc networks (VANETs) has a significant impact on the transmission and reception of data. It will cause receiver blocking due to the increase in the number of vehicles on the road and the rapid change of network topology. How to reduce the blocking and improve the reliability of data transmission is still a challenging problem. In this paper, we first propose a joint distributed power control and multiple antennas (DPC-MA) scheme, which can minimize blocking and simultaneously improve throughput. Moreover, it works well with an increased number of vehicles. Then we build an optimization model to select the optimal transmission power and number of antennas for vehicles based on their neighborhood relationships. The formulated problem is a mixed-integer nonlinear programming problem (MINLP), which is NP-hard and usually has no feasible solution. To solve this problem, we propose a low-complexity efficient algorithm using Lagrangian duality, which decomposes the original problem into a series of sub-problems and solves them iteratively using the fixed-point iterative algorithm and sub-gradient method. Simulation results show that our proposed scheme has better performance in resolving blocking and achieving high throughput, especially in high-density vehicle scenarios.

车载自组织网络 (VANET) 的动态拓扑结构对数据的传输和接收具有重大影响。由于道路上车辆数量的增加和网络拓扑结构的快速变化，会导致接收器阻塞。如何减少阻塞，提高数据传输的可靠性，仍然是一个具有挑战性的问题。在本文中，我们首先提出了一种联合分布式功率控制和多天线 (DPC-MA) 方案，该方案可以最大限度地减少阻塞并同时提高吞吐量。此外，它适用于增加的车辆数量。然后我们建立一个优化模型，根据车辆的邻域关系为车辆选择最佳的发射功率和天线数量。公式化问题是一个混合整数非线性规划问题（MINLP），这是NP-hard，通常没有可行的解决方案。为了解决这个问题，我们提出了一种使用拉格朗日对偶的低复杂度高效算法，它将原始问题分解为一系列子问题，并使用定点迭代算法和子梯度方法迭代求解。仿真结果表明，我们提出的方案在解决阻塞和实现高吞吐量方面具有更好的性能，尤其是在高密度车辆场景中。