The assumption of CF mMIMO, in which each UE can be served by all APs, resulted in massive fronthaul signaling, high computational complexity, and power consumption. For that reason, many studies have evaluated their systems under limited fronthaul capacity constraints [25,26,27,28,29,30,31,32,33,34,35], while others adopted different AP–UE selection techniques [36,37,38,39,40,41,42,43,44,45,46,47]. Based on that, the following subsections present in detail different solutions to build scalable systems with less fronthaul requirements, as shown in Figure 2, where each user can be served by a group of APs for more practical implementation.

In the following, research on CF mMIMO systems with limited fronthaul capacity links that connect the APs to the CPU are described. Table 1 compares the research [25,26,27,28,29,30,31,32,33,34,35], in terms of the data transmission, the channel model, the coordinated beamforming schemes, and finally the performance of the proposed systems. More precisely, the authors of [25] solved the joint problem of power weight allocation and quantization distortion under the capacity fronthaul links constraint by considering three different joint optimization problems with the zero-forcing (ZF) beamforming scheme. In [26], the closed-form achievable rates for three different transmission techniques at the APs were evaluated. Then, they proposed an efficient low-complexity fronthaul-rate-allocation algorithm in order to share the capacity of fronthaul connections for transmitting channel state information (CSI) and data signals from the APs to the CPU. Finally, data transmission power control was addressed using two optimization strategies to control the signal to interference and noise ratio (SINR). The same authors of [27,28] computed the closed-form max–min power allocation and fronthaul quantization problems, where in [27], the optimization problems were solved by integrating block coordinate descent (BCD) techniques with sequential linear optimization algorithms, resulting in a uniformly high QoS over the whole network coverage region, while in [28], the solution of the optimization expressions of both the achievable user rates and the fronthaul bandwidth consumption was found using low-resolution analog-to-digital converters (ADCs) with various AP–CPU functional splits to quantize the signals/samples shared among the APs and CPU during the transmission phase. The work of [29] designed two robust receivers to mitigate the impacts of limited capacity fronthaul by exploiting the knowledge of the heteroscedastic covariance of the associated effective noise. The high-complexity first receiver was built using an expectation propagation algorithm, which led to the best performance results, while the low-complexity second receiver used the effective noise heteroscedastic covariance in a generalized least squares variation of the maximum likelihood detection problem. [30] looked at another approach that considers point-to-multipoint fronthaul architecture, where the research is concentrated on developing a new optimization problem for joint power control and AP scheduling, aiming to achieve maximum power to each user and to a limited amount of shared fronthaul bandwidth. The authors of [31] addressed the achievable rate of a limited fronthaul system with a mean square error (MSE) receiver under the assumption of uncorrelated quantization distortion on two different scenarios, the exact uplink per-user rate and the uplink per-user rate, without taking into account the correlation between the inputs of the quantizers. In [32], the authors proposed an orthogonal frequency division multiplexing (OFDM)-based system that takes into consideration the transmitting power and fronthaul capacity constraints of each AP. Two quadratic transform optimization techniques are proposed to optimize the minimum rate of each user and the sum rate performance. In [33], the authors addressed the channel estimation error and the precoding processing, considering the low-ADC/digital-to-analog converter (DAC) resolution and low-capacity fronthaul connections. First, they reduced the channel estimation error by optimizing the minimum MSE-achieving codebook associated with the fronthaul compression. Then, they proposed an alternative optimization approach to alternate between two sub-problems, the power allocation and codebook design problems, in order to deal with the max–min fairness problem for the maximum ratio (MR) and ZF precoding schemes. Another approach was considered in [34], where the proposed architecture has a number of CPUs and APs, where the APs are connected to each CPU according to their respective distance. Based on this assumption, two expressions were derived, the density of the activated APs as a function of the blockage and CPU densities, and the achievable fronthaul capacity distribution, assuming an equal AP fronthaul bandwidth. Finally, Ref. [35] employed conjugate beamforming and stochastic geometry methods to analyze the proposed networks under a finite fronthaul capacity constraint. For conventional CF mMIMO architecture, they computed the coverage user rate using independent binomial point processes, while in the UC case, they determined the load for a subset of APs that serve a given user using independent homogeneous poisson point processes to model the locations of the APs and users in the network.

This subsection presents different AP–UE selection methods [36,37,38,39,40,41,42,43,44,45,46,47], considering UC approach that allows each user to be served by a subset of APs. Specifically, the authors of [36,39,40,42,47] adopted a simple AP–UE association method based on the largest large scale fading (LSF) coefficients or maximum channel gain. Besides the LSF-based approach, the authors of [36] proposed another method that is based on the received power from each AP to a particular UE. In [37] the authors proposed two different allocation algorithms, the effective channel gain from all UEs to all APs and the channel quality of each UE. Herein, each user is connected to the most appropriate AP in the geographical coverage area. The AP–UE connections in [38] are based on several switch-on/switch-off techniques to dynamically activate/deactivate some of the APs in the network. Namely, the pure random switching method, three selection algorithms that aimed to maintain consistent locations of the group of active APs, another strategy that used the availability of time-dynamic information in short-term traffic variations, and finally a greedy optimal EE selection strategy. In [41,43,44,45], the AP–UE connections are determined by considering the channels with the largest Frobenius norm, i.e., the users are connected to its nearest AP. Finally, in [46], each user is assigned to a limited number of APs based on a threshold received signal-to-noise ratio (SNR) method. First, the received SNR between each AP–UE pair is computed and then it is compared with a given threshold in order to limit the fronthaul load.