## 5 Conclusions  
In this paper, we studied the performance evaluation of an uplink cell-free massive MIMO network under Weichselberger Rician fading channels. This paper analyses the average UL SE, EE, and area throughput for multiple-antenna APs and multiple-antenna UE. As shown in the simulation results, the performance of the proposed estimator is evaluated based on the SE, EE, and area throughput with multi-antenna APs and multi-antenna UE. The fully centralized processing scheme is considered. The performance of the proposed estimator is high when the number of access points and the number of antennae per UE are increased. As the pilot reuse factor increased, the SE, EE, and area throughput are reduced, and the pilot length also decreased due to pilot contamination. The number of antennae per AP, the number of APs, and coherence time have positive effects on the EE, SE, and area throughput; i.e., when those parameters increased, the EE, SE, and area throughput rose. For a small number of antennae per UE, i.e., N < 2, the SE, EE, and area throughput under different numbers of APs for the proposed and existing estimators overlap; i.e., the performance of the proposed estimator is not effective for the single-antenna user equipment, and the performance is reduced as the number of antennae per UE and the number of AP rise due to interference.  
In addition to the qualitative trends, simulation results demonstrate clear quantitative benefits of the proposed estimator. The proposed method improved spectral efficiency by up to 14.93% (at N = 5 and M = 80) and energy efficiency by up to 9.77% (at N = 6 and M = 80) compared to the existing MMSE estimator. Area throughput gains reached up to 9.37% in dense AP deployments. However, the advantage is less pronounced for N < 2, where performance overlaps between the estimators. Further, with the increased pilot reuse factor (w = 2), the SE, EE, and AT decreased despite shorter pilot lengths, though the proposed estimator still consistently outperformed the existing one. These  
Figure 16: Area throughput versus the number of access points for fully centralized processing with the MMSE combiner at K = 10, L = 4, \(\tau_{c}\) = 200, and w = 2.  
findings underline the computational efficiency and scalability of the proposed estimator for large-scale CF massive MIMO networks.  
Conceptualization, B.D. and J.S.; methodology, B.D.; software, B.D.; validation, M.N. and G.I.; formal analysis, J.S. and G.I.; investigation, B.D.; resources, M.N.; data curation, U.S. and B.D.; writing--original draft preparation, B.D.; writing--review and editing, K.K.K., G.I. and J.S.; visualization, B.D.; supervision, U.S. and K.K.K.; project administration, G.I.; funding acquisition, G.I. All authors have read and agreed to the published version of the manuscript.  
This study is financed by the European Union--NextGenerationEU--through the National Recovery and Resilience Plan of the Republic of Bulgaria, project No. BG-RRP-2.004-0005.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
AP Access Point EE Energy Efficiency MMSE Minimum Mean Square Error MIMO Multiple Input Multiple Output SE Spectral Efficiency DL Downlink UL Uplink UE User Equipment