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Table 1 Limitations of some related works

From: Application of cell-free massive MIMO in 5G and beyond 5G wireless networks: a survey

Ref. Focus and coverage Limitations Contributions
[78] The work quantifies the improvement of CF-mMIMO over cellular massive MIMO in EE using max-min power control and CB. ▪ Optimization techniques not presented.
▪ Channel estimation not clearly outlined.
▪ This paper outlines useful techniques to improve the overall EE of CF-mMIMO.
▪ This paper examines the most recent research trends and future directions on EE in CF-mMIMO.
[79] The authors examine the performance of CF-mMIMO systems with limited fronthaul capacity. ▪ A perfect hardware transceiver that is not satisfied in practice is considered. ▪ An extensive analysis of transceiver HI, most recent research trends on the performance of CF-mMIMO systems with limited fronthaul capacity are presented.
[80] This paper analyzes the UL SE of CF massive MIMO with multiple antennas at the APs and users, zero-forcing (ZF) combining, and data power controls. ▪ ▪ Challenges with the use of multi-antenna are not clearly outlined.
▪ Security and privacy issues of multi-antenna users are not discussed elaborately.
▪ Presents a detailed outline of the application of CF-mMIMO in improving the SE.
▪ Security and privacy issues of wireless networks are clearly outlined. Also, UL SE of CF-mMIMO with multiple antennas at the APs and users are broached.
[81] The authors propose an ANN-based UL power control technique to assess power allocation in a CF-mMIMO network to maximize the sum rate or the min rate. ▪ Applications of power control in CF-mMIMO not clearly outlined.
▪ Power optimization techniques are limited to just a deep learning approach.
▪ Presents a robust discussion on the application of power control in CF-mMIMO.
▪ Power optimization techniques such as alternative optimization, second-order cone program (SOCP), and machine learning (ML)-based approaches are discussed [82].
[83] The work considers maximizing the total EE of CF massive MIMO using a well-established DL power consumption model. Additionally, AP selection schemes are covered to minimize the power consumption caused by the backhaul links. ▪ Channel estimation not clearly outlined.
▪ Open research issues and future research directions are not discussed elaborately.
▪ This paper examines the most recent research trends and future directions in CF-mMIMO.
▪ A robust discussion on AP selection schemes aimed at minimizing power consumption is presented.
[84] The authors examine a generalized UL CF-mMIMO in the presence of radio frequency (RF) impairments and ADC imperfections. Also, the study provides novel insights on implementing low-quality transceiver RF chains and low-resolution ADCs. ▪ Technical propositions to minimize hardware costs in practical systems are limited.
▪ Signal detection is not clearly outlined.
▪ Instructive insights to optimize the system’s performance in practical scenarios are clearly discussed.
▪ Sophisticated signal detection techniques such as ML, deep learning-based techniques, and sphere decoder are highlighted.
[69] The work characterizes the coexistence and underlying issues of SWIPT and CF massive MIMO. ▪ Open research issues and future research directions in CF-mMIMO are not discussed clearly. ▪ Research activities capturing the most recent research trends and lessons learned related to SWIPT in CF massive MIMO are outlined.
[85] The work presents the UL performance of CF-mMIMO systems with multiple antennas and least-square (LS) estimators considering the effects of spatially correlated fading channels. ▪ The security and privacy threats to multi-antenna users are not considered.
▪ Pilot contamination effects were not discussed.
▪Security and privacy issues of wireless networks are captured. Future research directions and lessons learned are outlined.
▪ This paper also provides a holistic review of pilot contamination and the UL performance of CF massive MIMO systems.
[86] The paper considers using an NCB scheme in CFm-MIMO subject to short-term average power constraints. The work also considers the effects of channel estimation errors and pilot contamination. ▪ Although future works are outlined, they are not discussed comprehensively.
▪ Power control algorithms are not developed.
▪ Future directions in the areas of channel estimation and pilot contamination are reported.
▪ Advanced power optimization techniques such as geometric programming (GP), SOCP, and ML-based approaches are highlighted.
[62] The work investigates the feasibility of observing channel hardening in CF massive MIMO using stochastic geometry. ▪ The practical application of the proposed model is totally is not elaborated. ▪ This paper discusses the application of CF-mMIMO in EE, SE, and SWIPT. Additionally, the research focus and directions related to channel hardening in CF-mMIMO systems are outlined.
[87] This survey focuses on integrating CF massive MIMO systems with a power-domain NOMA technique. ▪ Open research issues are not discussed.
▪ Signal detection was not discussed in this paper.
▪ Up-to-date review of past findings, most recent research activities, and lessons learned are outlined.
▪ This paper discusses the current research trends on NOMA in CF-mMIMO systems.
[88] This survey focuses on the EE of limited-backhaul CF massive MIMO. The authors introduced an efficient solution to address the EE maximization problem. ▪ The open research issues and future research directions are not clearly outlined. Practical implementation of CF-mMIMO is not covered. ▪ This paper provides a holistic discussion on EE and outlines key findings, current research trends, and future research directions in EE of CF-mMIMO systems.