In modern day communication systems, the massive MIMO architecture plays a pivotal role in enhancing the spatial multiplexing gain, but vice versa the system energy efficiency is compromised. Consequently, resource allocation in-terms of antenna selection becomes inevitable to increase energy efficiency without having any obvious effect or compromising the system spectral efficiency. Optimal antenna selection can be performed using exhaustive search. However, for a massive MIMO architecture, exhaustive search is not a feasible option due to the exponential growth in computational complexity with an increase in the number of antennas. We have proposed a computationally efficient and optimum algorithm based on the probability distribution learning for transmit antenna selection. An estimation of the distribution algorithm is a learning algorithm which learns from the probability distribution of best possible solutions. The proposed solution is computationally efficient and can obtain an optimum solution for the real time antenna selection problem. Since precoding and beamforming are also considered essential techniques to combat path loss incurred due to high frequency communications, so after antenna selection, successive interference cancellation algorithm is adopted for precoding with selected antennas. Simulation results verify that the proposed joint antenna selection and precoding solution is computationally efficient and near optimal in terms of spectral efficiency with respect to exhaustive search scheme. Furthermore, the energy efficiency of the system is also optimized by the proposed algorithm, resulting in performance enhancement of massive MIMO systems.

在现代通信系统中，大规模MIMO架构在增强空间复用增益方面起着举足轻重的作用，但是反之，系统的能效却受到损害。因此，天线选择期间的资源分配变得不可避免，以在不产生任何明显影响或不损害系统频谱效率的情况下提高能量效率。可以使用穷举搜索来执行最佳天线选择。然而，对于大规模MIMO架构，穷举搜索不是可行的选择，因为随着天线数量的增加，计算复杂度呈指数增长。我们提出了一种基于概率分布学习的高效计算和优化算法，用于选择发射天线。分布算法的估计是一种学习算法，可从最佳可能解的概率分布中学习。所提出的解决方案在计算上是有效的，并且可以针对实时天线选择问题获得最佳解决方案。由于预编码和波束成形也被认为是解决由于高频通信而引起的路径损耗的必不可少的技术，因此在选择天线之后，采用连续干扰消除算法对所选天线进行预编码。仿真结果证明，相对于穷举搜索方案，所提出的联合天线选择和预编码解决方案在频谱效率方面计算效率高且接近最佳。此外，所提出的算法还优化了系统的能效，