一种高精度变步长的大规模MIMO能效优化算法

A High-Precision Variable-step Massive MIMO Energy-efficiency Optimization Algorithm

5G时代更多通信用户接入通信系统中,用户增多导致系统资源分配更加复杂,传统大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)能效优化算法的性能受到前所未有的挑战。针对传统大规模MIMO系统采用牛顿法进行能效优化的过程中存在性能不佳的问题,提出了一种高精度变步长能效优化算法。该算法在每一轮求解最优能效的迭代过程中,利用当前的循环迭代次数改变拉格朗日乘子的步长,再利用快速收敛的Dinklebach算法,提高收敛速度。通过上述方式,该算法获得了精度高和收敛快的优点,并将所提算法与其他交替优化等同类算法开展仿真与复杂度对比测试与分析。仿真结果证实,该算法在略微损失复杂度的情况下,显著提高了大规模MIMO系统能效。

In 5G era,more communication users are accessing the communication system,and the increase in users leads to more complicated system resources allocation.The performance of traditional massive MIMO(Multiple-Input Multiple-Output)energy efficiency optimization algorithm is unprecedentedly challenged.Aiming at the problem of poor performance in the traditional massive MIMO system using Newton’s method for energy efficiency optimization,a high-precision variable-step energy efficiency optimization algorithm is proposed.This algorithm uses the current cycle iteration number to change the step length of the Lagrangian multiplier in each round of the iterative process of solving the optimal energy efficiency,and then uses the fast convergence Dinklebach algorithm to improve the convergence speed.Through the above methods,the algorithm has the advantages of high accuracy and quick convergence.The proposed algorithm and other similar algorithms such as alternate optimization are tested and analyzed in simulation and complexity comparison.Simulation results indicate that the proposed algorithm significantly improves the performance of massive MIMO systems with a slight loss of complexity.