Cell-free massive multiple-input multiple-output (CF mMIMO) can be considered as a potential physical layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, and link reliability.
1. Introduction
The vision of a networked society with unlimited access to information and data sharing at any time and anywhere, for everyone and everything, necessitated a re-evaluation of current cellular-based deployments to accomplish this vision [
1]. In the past,
bandwidth expansion (sub-6 GHz and millimeter Wave (mmWave) frequencies) and
network densification (ultra-dense networks (UDNs)) were the primary means to provide more sophisticated broadband services and improve network scalability, spectral efficiency (SE) and energy efficiency (EE) [
2].
1.1. Bandwidth Expansion
Nowadays, the demand for mobile data traffic is increasing rapidly each year due to the extensive use of smartphones and other wireless devices, and thus, sub-6 GHz bands are extremely crowded. Therefore, using a higher frequency spectrum, such as mmWave was one of the key physical layer solutions for 5G and beyond systems [
3]. With sub-6 GHz, the systems generally implement a full-digital baseband beamforming, i.e., each antenna element is connected to one dedicated radio frequency (RF) chain [
4]. However, the high cost and energy consumption of the large number of RF chains force mmWave systems to rely on hybrid analog–digital beamforming, where the number of RF chains is much less than the number of antennas. In such hybrid architectures, some signal processing is carried out at the digital domain and some left to the analog one [
5]. Indeed, different hybrid beamforming schemes have been presented in the literature for narrowband [
6,
7] and wideband [
8,
9,
10] mmWave-based systems.
1.2. Network Densification
Network densification (by deploying a large number of small cells (SCs)) allows for each user to be tracked by a base station (BS). However, the number of BSs must increase exponentially, leading to a significant increase in the investment required, and thus requires advancements in hardware miniaturization and cost reduction [
11]. On the other side, both inter-cell interference (ICI) and poor signal quality at cell-edges have caused notable performance degradation problems [
12]. Therefore, the improved interference mitigation techniques, such as distributed antenna systems (DASs) [
13], network multiple-input multiple-output (MIMO) [
14], coordinated beamforming [
15], and coordinated multi-point (CoMP) [
16] have been proposed to deal with the users near the cell borders (cell-edge users) by adding cooperation between the closely located access points (APs), and hence mitigating ICI. Although these techniques may boost system performance as well as provide micro- and macro-diversity gain, the distributed APs into cooperative clusters led to cluster interference [
16,
17]. In addition, despite the high peak data rates available to each user in the cell centers, the significant variations within each cell render quality of service (QoS) are unreliable [
18]. For that reason, the primary goal for future mobile networks (B5G/6G) is to guarantee rates to the vast majority of locations within the geographical coverage region rather than increase the peak rates [
19]. Therefore, it is crucial to start research on beyond 5G/6G wireless systems and design a non-cellular-based network architecture with intelligent cooperation and coordination capabilities to meet future performance requirements.
1.3. Cell-Free Massive MIMO Systems
By integrating the benefits of both DAS and MIMO technologies, a new disruptive network architecture, based on cell-less deployment, called cell-free massive MIMO (CF mMIMO) has been designed to fulfill the desired goals of consistently high data rates everywhere and uniform QoS, ultra-high reliability, and the avoidance of cells interference since the concept of cell boundaries does not exist [
20,
21]. As shown in
Figure 1, in CF mMIMO, a very large number of distributed APs connected to the central processing unit (CPU) via fronthaul connections serve, simultaneously and jointly, all user equipments (UEs)) that share the same resources [
22]. However, the conventional CF mMIMO system is not scalable and has some issues with fronthaul signaling, high computational complexity, and makes large-scale networks unfeasible [
23]. Therefore, the user-centric (UC) approach has been proposed to deal with the scalability problem and build a realistic deployment, where each UE is only served by a small number of cooperative APs, leading to a minimization in the amount of information sent to the CPU [
23,
24]. UC CF mMIMO topologies may serve as the basis for B5G networks since they achieve EE and robust connectivity to guarantee that all UEs enjoy consistent coverage and performance through the network area [
24].
Figure 1. Cellular Network Architecture vs Cell-Free and User-Centric CF massive MIMO.
2. Scalable Cell-Free mMIMO Systems
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.
Figure 2. Cell-Free Massive MIMO Systems with Limited Fronthaul Capacity.
2.1. Limited Fronthaul Capacity
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.
Table 1. A literature comparison for CF mMIMO systems with limited fronthaul capacity.
2.2. AP–UE Association Techniques
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.
This entry is adapted from the peer-reviewed paper 10.3390/electronics12041001