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. | â–ª Optimization techniques not presented. â–ª Channel estimation not clearly outlined. | â–ª This paper outlines useful techniques to improve the overall EE of CF-mMIMO. â–ª 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. | â–ª A perfect hardware transceiver that is not satisfied in practice is considered. | â–ª 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. | â–ª â–ª Challenges with the use of multi-antenna are not clearly outlined. â–ª Security and privacy issues of multi-antenna users are not discussed elaborately. | â–ª Presents a detailed outline of the application of CF-mMIMO in improving the SE. â–ª 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. | â–ª Applications of power control in CF-mMIMO not clearly outlined. â–ª Power optimization techniques are limited to just a deep learning approach. | â–ª Presents a robust discussion on the application of power control in CF-mMIMO. â–ª 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. | â–ª Channel estimation not clearly outlined. â–ª Open research issues and future research directions are not discussed elaborately. | â–ª This paper examines the most recent research trends and future directions in CF-mMIMO. â–ª 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. | â–ª Open research issues and future research directions in CF-mMIMO are not discussed clearly. | â–ª 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. | â–ª The security and privacy threats to multi-antenna users are not considered. â–ª Pilot contamination effects were not discussed. | â–ªSecurity and privacy issues of wireless networks are captured. Future research directions and lessons learned are outlined. â–ª 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. | â–ª Although future works are outlined, they are not discussed comprehensively. â–ª Power control algorithms are not developed. | â–ª Future directions in the areas of channel estimation and pilot contamination are reported. â–ª 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. | â–ª The practical application of the proposed model is totally is not elaborated. | â–ª 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. | â–ª Open research issues are not discussed. â–ª Signal detection was not discussed in this paper. | â–ª Up-to-date review of past findings, most recent research activities, and lessons learned are outlined. â–ª 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. | â–ª The open research issues and future research directions are not clearly outlined. Practical implementation of CF-mMIMO is not covered. | â–ª 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. |