## 1 Introduction  
Future wireless networks will need to manage billions of devices simultaneously, and each one will require high throughput to serve a variety of applications including speech, real-time video, high-definition movies, etc. [1, 2]. Cellular networks cannot handle such high connection volumes due to the high levels of interference and the poor performance of user terminals located at the cell boundary. Additionally, traditional cellular systems are primarily built for human users. The Internet of Things, the Internet of Everything,smart X, and other machine-type communication systems are anticipated to be crucial in the development of future wireless networks. Expandable and effective connectivity for billions of units is the primary difficulty in machine-type communications. Since each cell can only serve a certain number of user equipment (UE), a centralized architecture with a cellular topology does not appear to be effective in these situations. Therefore, why not build a cell-free structure with distributed technology? It is called the CF M-MIMO system; it is created by combining M-MIMO technology with cell-free structures [1; 2; 3; 4]. A comparison of traditional and CF M-MIMO technologies is shown in Figure 1[5]. In cellular M-MIMO, the base station has a lot of antennas installed, allowing for the spatial multiplexing of large user signals in the identical frequency range [6]. Cellular M-MIMO's performance is, however, constrained by several issues, such as pilot contamination, inter-cell interference, and the requirement for precise channel state data [7]. CF M-MIMO, on the other hand, is not dependent on the conventional cellular network architecture [8; 9]. The transmission and reception of signals are instead coordinated across the antennas by the use of signal processing algorithms, which are widely dispersed over the coverage area. By using this strategy, inter-cell interference may be reduced, and the communication lines' durability can be increased. The CF M-MIMO approach is an excellent fit for indoor and hot-spot coverage situations in the next generation of wireless communication, including locations such as subways, smart factories, shopping malls, railway stations, small towns, hospitals stadiums, community centers, and college campuses [5]. Cell-free refers to a situation in which there are no cell borders during UL and downlink transmission, as seen by the UE since every access point (AP) that has an impact on the UE will actively participate in the connection [10; 11; 12; 13]. For instance, all APs that receive an uplink data signal from a UE will work together to decode the signal. Even though not all APs may serve every UE, the network is collectively serving all K-active UE within its coverage region. The infrastructure and signal processing aspects of cellular and cell-free networks differ, although the UE may not be aware of these variations. The same UE should be able to connect to both types of networks without having to update its software [14]. The following are the contributions as a result of the inspirations from the aforementioned observations: (i) The proposed model incorporates two channel estimators to analyze the SE, EE, and area throughput of the CF M-MIMO network under the Weichselberger Rician fading channel with multi-antenna APs and UE, i.e., phase-aware MMSE and phase-aware EW-MMSE; (ii) the performance of the developed estimator with the existing  
Figure 1: Comparison of CF M-MIMO and cellular M-MIMO systems [5]. channel estimator based on parameters such as SE, EE, and area throughput is analyzed; (iii) the impact of the pilot reuse factor and the number of users on SE, EE, and area throughput with multi-antenna access point and multi-antenna user equipment studied in details are determined. (iv) The uplink phase-aware MMSE and EW-MMSE channel estimator with MMSE combining is also studied under multi-antenna APs with multi-antenna UE over the Weichselberger Rician fading channel.