基于多维效用函数的多基站运行成本优化方法

现有移动通信系统出现业务量迅猛增长现象,为缓解增长的基站负荷带来的基站功耗,移动通信系统中为基站配备了可再生能源产能设备。如何实现通信系统信息流与能量流匹配,完成通信业务与基站可再生能源储备的精确配对,更加充分的利用通信系统内可再生能源,是下一步优化通信系统网络性能并降低系统运行成本的关键。为此,本文构建了一个多维效用函数,该函数联合考虑了用户的接受信干噪比、可再生能源利用与基站负载3种因素。通过将求解多基站系统运行成本最小化问题转化为多维效用函数效用值最大化问题,对多基站系统进行优化,并实现系统运行成本最小化。问题转化后为一个混合整数非线性优化的非凸问题。为求解该问题本文提出了多维效用函数迭代优化算法,将该问题拆分为用户调度、功率分配与基站负载均衡3个子问题,通过采用交替优化和连续凸近似技术对问题进行迭代求解。仿真结果表明,相比于最大SINR关联优化算法与“最大SINR+可再生能源利用”优化算法,本文算法在可再生能源利用率方面分别提升了58.68%和29.74%,且系统总费用一直维持在较低水平,具备显著优势。

Multi-base station operation cost optimization method based a multi-dimensional utility function

At present, there is a rapid growth in communication services in mobile communication systems. To alleviate the power consumption caused by the increasing base station load, renewable energy production equipment has been equipped for mobile communication system base stations. By matching the information flow and energy flow in the communication system, communication services and renewable energy storage in the communication system can be accurately paired. This can further improve the utilization rate of renewable energy within the communication system, which is the key to optimizing the network performance of the communication system and reducing system operating costs in the next step of research. Therefore, this paper constructs a multi-dimensional utility function. This function comprehensively considers three factors: user signal interference noise ratio, renewable energy utilization, and base station load. This paper solves the initial problem of minimizing the operating cost of a multi base station system by transforming it into a problem of maximizing the utility value of a multi-dimensional utility function. The transformed problem is a non-convex problem of mixed integer nonlinear optimization. To solve this problem, this paper proposes the Multidimensional Utility Function Iterative Optimization Algorithm. This algorithm divides the problem into three subproblems: user scheduling, power allocation, and load balancing. Then, this problem can be iteratively solved by using alternating optimization and continuous convex approximation techniques. The simulation results show that compared to the Maximum SINR Association Optimization Algorithm and the "Maximum SINR and Renewable Energy Utilization" Optimization Algorithm, the algorithm in this paper has improved the utilization efficiency of renewable energy by 58.68% and 29.74%, respectively. At the same time, the total cost of applying the algorithm proposed in this paper has been consistently lower than other algorithms during the simulation period. This indicates the advantages of the algorithm proposed in this paper.