Channel Capacity Optimization Using MIMO Systems: Application of the MMSE Algorithm to Near-field Hyperscaling

In this paper, we investigate the application of MMSE algorithm to optimize the channel capacity in near-field super massive MIMO (Multiple-Input Multiple-Output) systems. Conventional MIMO systems also have problems such as poor spectral efficiency and high computational complexity, and this paper aims to optimize the spectral utilization efficiency, reduce the computational complexity, and realize the channel capacity optimization by using the MMSE algorithm. In the paper, the MMSE (Minimum Mean Square Error) algorithm is firstly designed on the basis of the traditional MMSE algorithm, combined with the orthogonal characteristics of the digital matrix, and through the idea of iterative correction; and then, based on the angular power spectral characteristics under the different application scenarios, the high-density antenna arrays are applied to the near-field ultra-large-scale MIMO system, and the antenna array geometries are realized and the optimization of the upper limit of channel capacity. Then, the paper implements the modelling of the channel by selecting the extended Saleh-Valenzuela channel model; finally, the effectiveness of the MMSE algorithm is also verified. Through the empirical study, it is found that when the number of RF links is 5 and the signal-to-noise ratio is 0 dB, the spectral efficiencies of this paper's algorithm are 36.71 bit/s/Hz and 51.77 bit/s/Hz, respectively; at this time, the spectral efficiencies of the traditional algorithm are 25.50 bit/s/Hz and 43.00 bit/s/Hz, respectively. Experiments have demonstrated that the algorithm in this paper has significant advantages in enhancing system capacity and transmission efficiency.