## 6 Conclusions  
In this work, the performance bounds in terms of SE achieved by the 5G-NR precoding matrices are numerically evaluated for SU-MIMO and MU-MIMO systems. A detailed description of the 5G-NR precoding matrices implementation, covering both Type I and Type II codebooks, is provided according to TS 38-211 and TS 38-214. Numerical results are obtained for several antenna array configurations at the gNB. It can be noted that, although optimal SVD and BD provide a higher SE than the 5G-NR precoders, they require an unfeasible overhead that cannot be currently supported in real deployments. Indeed, the main goal of 5G-NR is to guarantee a tradeoff between performance, complexity, and feedback overhead. Furthermore, it is illustrated that the number of parallel layers that achieves the highest performance depends on the spatial properties of the channel matrix. In general, it was found that for low SNR values it is advisable to focus the beam on a single highly directional data stream than to multiplex several parallel layers. The effect of imperfect channel estimation is evaluated revealing that for low estimation error variance, in the order of \(10^{-3}\), the performance loss is negligible. In the case of MU-MIMO systems, the sum-SE is limited by the interference between near users. The use of advanced multi-antenna techniques to reach the demanding SE in 5G networks has been explored and validated, providing a comprehensive guide with useful insights for practical deployments. Future research can be conducted to explore the enhancements of mTRP and some resource allocation algorithms in conjunction with SU-MIMO and MU-MIMO. In addition, a further analysis of the upcoming releases of 3 GPP regarding the CSI codebook may be considered. Conceptualization, D.A.U.V, H.O. and M.J.F.-G.G. ; methodology, D.A.U.V., H.O. and M.J.F.-G.G. ; software, D.A.U.V. and H.O. ; validation, D.A.U.V., H.O. and M.J.F.-G.G. ; formal analysis, D.A.U.V., H.O. and M.J.F.-G.G. ; investigation, D.A.U.V., H.O. and M.J.F.-G.G. ; resources, D.A.U.V., H.O. and M.J.F.-G.G. ; data curation, D.A.U.V. ; writing original draft preparation, D.A.U.V. ; writing review and editing, D.A.U.V., H.O., M.J.F.-G.G. and A.F. ; visualization, D.A.U.V., H.O. and  
Figure 11: Average SE vs. number of users (\(K\)) for MU-MIMO systems assuming \(N_{t}=32,N_{r}=4\), \(N_{s}=1\), and SNR=5 dB. The gNB is implemented assuming a single-panel ULA with \(N_{1}=16\), and \(N_{2}=1\). Imperfect CSI is also assumed with \(\sigma_{e}^{2}=10^{-3}\). M.J.F.-G.G. ; supervision, H.O., M.J.F.-G.G. and A.F. ; project administration, M.J.F.-G.G. and A.F. ; funding acquisition, M.J.F.-G.G. and A.F. All authors have read and agreed to the published version of the manuscript. This work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie ETN TeamUp5G, grant agreement No. 813391. Also, this work has been partially funded by the Spanish National project IRENE-EARTH (PID2020-115323 RB-C33/AEI/10.13039/501100011033). The data used to support the findings of this study are available from the corresponding author upon request. The authors declare that there are no conflict of interest regarding the publication of this paper. The following Symbols are used in this manuscript: