Table 14 Open research issues in cell-free massive MIMO

Hardware impairments

Most literature on CF-mMIMO has centered on perfect hardware components, which are unrealistic in practice due to high financial cost, bulkiness, and energy-hungry devices [171]. Achieving ideal hardware in both transceiver and receiver under practical implementation remains a general challenge. In order to alleviate this detrimental imperfection, an insight into the impact of RF impairments and ADCs imperfections on CF massive MIMO systems is presented in [84]. Results obtained show that the hardware expenses can be minimized by limiting the quality of transceiver RF chains and the quantization bits of low-resolution ADCs, however, at the cost of significant performance degradation. The effect of HI on the performance of CF-mMIMO has been considered extensively [134, 135]. The adverse impact of HI at UE is not well accounted for, though the SE is shown to be significantly boosted as the hardware qualities increases. Further research s required to investigate the detrimental influence of HI.

Channel estimation

Acquiring CSI is an important part of any telecommunication system for resource allocation and detecting user signals. Unfortunately, the complex architecture of the transceiver and the plethora of distributed antennas in CF massive MIMO has made channel estimation quite challenging [172]. DL, ML, and compressed sensing have recently been proposed to predict statistical channel characteristics during channel estimation [173,174,175,176]. Thus, obtaining an efficient estimation method is one of the crucial areas requiring further investigation.

Security and privacy

The security and privacy of user data are essential in deploying 5G and B5G wireless networks due to the need to support billions of connected devices and deliver reliable Gigabit connection speeds [177]. As a result, investigations toward finding new approaches to improve trust and security of future wireless networks are exciting for research. More precisely, combining quantum communication (frequencies higher than 300 GHz) with CF massive MIMO is an excellent area worthy of further investigation.

Signal detection

In massive MIMO systems, signal detection requires sophisticated processing due to the massive number of antennas, thus incurring a high computational complexity. High-quality research aimed at obtaining an optimal trade-off between computational complexity and its performance has been proposed [178]. To further downplay this challenge, ML, deep learning-based techniques, sphere decoder, and SIC techniques have been explored for signal detection [9, 179, 180]. The design of cutting-edge signal detection algorithms is an exciting area calling for further research.

Pilot contamination

Here, non-orthogonal pilot sequences need to be employed by the users due to the limited length of the coherence interval in the UL training phase. This, in turn, causes a so-called severe effect identified as pilot contamination which presents a significant bottleneck to the performance of CF massive MIMO [85, 101, 181, 182]. Specifically, the channel hardening effect is primarily affected by pilot contamination, particularly for a small coverage area [64]. The authors in [128] demonstrated that pilot contamination severely impacts the performance of CF-mMIMO-NOMA considerably. A new insightful pilot assignment scheme utilizing graph coloring is investigated in [183]. Indeed, the throughput is improved dramatically with the proposed graph coloring-based pilot assignment scheme. At the same time, a modest throughput complexity trade-off is obtained. To further optimize the performance of CF massive MIMO, developing optimal models that reduce the effect of pilot contamination is an interesting area worthy of further investigation.

Energy efficiency

EE has become a major concern for network operators owing to increased power consumption, carbon emissions, and global warming related to wireless communication technologies. Although CF massive MIMO is a natively greener technology than its cellular counterpart, power consumption in 5G networks is relatively higher than 4G networks [78, 184]. In order to address the crucial demanding green specifications, simplified deployment, and efficient energy-saving in next-generation wireless systems; there is a need for more advanced, low complexity, and low-cost optimization models and algorithms for greener CF massive MIMO systems.