## 2 Related Work  
In this section, the research work on the channel estimation technique of the CF M-MIMO network under 5G and beyond is described. The contribution and drawback of each paper are discussed. Almost all papers considered in this work are on the uplink session of the CF M-MIMO system under fading channels. In [15], CF M-MIMO with a multi-antenna AP and UE under a Rayleigh fading channel is investigated. The closed-form SE expression with MMSE successive interference cancelation (SIC) and a maximum ratio (MR) combiner is derived. Additionally, MMSE combiner schemes are derived at both the AP and the central processing unit (CPU), which can maximize the achievable SE for the fully centralized and distributed implementations, respectively. Also calculated is the closed-form expression of SE for the two-layer decoding scheme with MR combining. Similarly, the work mentioned in [16; 17; 18] used a two-layer decoding scheme for further interference reduction in order to enhance the performance of the cell-free massive MIMO system. The author showed the SE performance of different implementations such as fully centralized, distributed, and two-layer decoding with MMSE and MR combining under fading channels that only consider the non-line-of-sight (LoS) path. The author does not address the signal that came in the line-of-sight path, and also the user mobility that leads to the phase shift is not considered. The EE and area throughput is not addressed. The channel estimation in this work is estimated by the MMSE estimator, which does not consider the phase shift or mobility of user equipment. In addition to that, this estimator has high computational complexity. In [19], the channel estimation of UL CF M-MIMO under the Rician fading channel is analyzed. The channel consists of both the LoS and non-line-of-sight (NLoS) paths. The dynamic cooperative cluster (DCC) is illustrated, where only Aps with the optimal channel conditions with the intended user is permitted to serve it. The DCC in [14; 20; 21] is also illustrated to make the CF M-MIMO scalable and improve the performance of the CF M-MIMO network. The author proposed a partial channel estimator in which a set of APs with the best channel condition can evaluate the channel estimates. The normalized MSE and the computational complexity for the partial MMSE and partial EW-MMSE are derived. In addition, the author showed that the PEW-MMSE is almost the same as result as of the PMMSE in terms of the normalized MSE. However, the computational complexity of the PEW-MMSE is low compared to that of the PMMSE channel estimator. The author demonstrated that when the number of users increases, the channel estimation reduces. In this paper the analysis is limited to channel estimations only; the energy efficiency, area throughput, and spectral efficiency of the paper are not addressed. The author did not show the channel estimation when the number of antennas per user equipment is more than one. CF M-MIMO with multiple antennas on both the AP and user devices under Rayleigh fading channels is analyzed [22]. The author investigated the SE for the large-scale fading decoding (LSFD) scheme and fully centralized processing schemes. Iteratively weighted sum-minimum mean square error (I-WMMSE) algorithms have been proposed for two processing schemes in the literature to improve the UL SE in CF M-MIMO systems. The closed-form SE expression is derived. The I-WMMSE precoding scheme has been shown to achieve outstanding performance in CF M-MIMO systems, even with a large number of UE antennas. Importantly, the scheme has been shown to become even more effective with a larger number of UE antennas. The author did not consider the LoS path. The EE and area throughput are not addressed. The MMSE channel estimator used in this work has high computational complexity. In [23], the multi-antenna AP and multi-antenna UE under the Weichselberger Rician fading channel with random phase shifts are studied.