基于拟牛顿内点法的认知车联网能效优先资源分配算法

Resource allocation algorithm with energy efficiency priority based on quasi-Newton interior point method in cognitive internet of vehicles

为了提高认知车联网中多用户资源分配的能效及实时性,提出了一种在信道状态信息不理想情况下最大化系统能效的资源分配算法.联合考虑额定系统传输功率、主用户干扰阈值、最低通信速率以及用户间比例公平性等约束条件,将主用户的干扰约束条件转换成概率型约束条件.然后,采用Bernstein近似的方法处理该概率型约束,通过设置公平门限来解决用户间的比例公平性问题.最后,分别采用高、低复杂度的子载波分配算法,配合拟牛顿内点法进行功率分配.仿真结果表明,所提算法的能效约为最优解上界的93%,既能满足系统能效要求,又降低了计算复杂度,适用于对实时性要求较高的车联网系统.

To improve the energy efficiency and real-time performance of multi-user resource allocation(RA) in cognitive internet of vehicles(IoV), a resource allocation algorithm maximizing system energy efficiency was proposed with imperfect channel state information(CSI). Considering the constraints of the transmission power budget, the primary users’ interference thresholds, the minimum communication rate and the proportional fairness among users, the interference constraints of the primary users were transformed into the probabilistic constraints. Then, the Bernstein approximation was used to deal with the probabilistic constraints and the proportional fairness among users was solved by setting the fair threshold. Finally, high and low complexity subcarrier allocation algorithms were used respectively, and combining with the quasi-Newton interior point method, a power allocation was carried out. The simulation results show that the energy efficiency of the proposed algorithm is about 93% of the upper bound of the optimal solution, which can meet the energy efficiency requirement of the system and reduce the computational complexity. It is suitable for the IoV with high real-time requirements.