|Ref.||Focus and coverage||Limitations||Contributions|
|||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.
|||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.|
|||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.
|||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 .
|||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.
|||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.
|||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.|
|||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.
|||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.
|||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.|
|||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.
|||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.|