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The rapid development of wireless communication in today's society is facing the development bottleneck of increasingly tense spectrum resources. As the key technology of 5G technology, Massive Multiple Input Multiple Output (MMIMO) technology has been widely studied and applied in recent years. It is expected to bring progress to wireless communication systems. The key characteristic of MMIMO is its large antenna array that results in a high dimension of the wireless system channel. This article examines the use of precoding technology in large-scale multi-antenna MIMO systems to address the issue of Common Channel Interference (CCI). The study starts by introducing the concept of linear digital precoding and provides mathematical demonstrations of three linear digital precoding schemes. The performance of these schemes is then simulated and analyzed using MATLAB, with the results showing a significant improvement in the bit error performance and combining rate of the MIMO system. [ABSTRACT FROM AUTHOR]

In this paper, a Takenaka-Malmquist-Volterra (TMV) model structure is employed to improve the approximations in the low-pass equivalent behavioral modeling of radio frequency (RF) power amplifiers (PAs). The Takenaka-Malmquist basis generalizes the orthonormal basis functions previously used in this context. In addition, it allows each nonlinearity order in the expanded Volterra model to be parameterized by multiple complex poles (dynamics). The state-space realizations for the TMV models are introduced. The pole sets for the TMV model and also for the previous Laguerre-Volterra (LV) and Kautz-Volterra (KV) models are obtained using a constrained nonlinear optimization approach. Based on experimental data measured on a GaN HEMT class AB RF PA excited by a WCDMA signal, it is observed that the TMV model reduces the normalized mean-square error and the adjacent channel error power ratio for the upper adjacent channel (upper ACEPR) by 1.6 dB when it is compared to the previous LV and KV models under the same computational complexity. [ABSTRACT FROM AUTHOR]