## 1 Introduction  
In this era, the requirement for systems that can support elevated data rates is becoming increasingly important due to the ever-increasing use of applications that allow multimedia transmission, such as virtual reality (VR), 3D videos, and internet of things (IoT) [1, 2]. The current pace of technological advancement in the field of image resolutions mandates urgent upgrade of the existing wireless network infrastructure to meet futuristic needs of high-end multimedia data transmission [3, 4]. Looking back into various critical evolution junctions in the wireless communication domain, we find the inclusion of OFDM methodology as an important step towards enhancing the data rate transmission. In fact, OFDM has been widely used by many popular standards such as wireless local-area network (WLAN) (e.g., 802.11 a up to 802.11 ax) [5], digital audio broadcasting (DAB) [6], digital video broadcasting (DVB) [7], wireless wide area network (WWAN) (e.g., 802.16 m) [8], and long-term evolution (LTE) [9, 10]. OFDM is essentially a multicarrier modulation (MCM) methodology in which the high data rate stream is bifurcated into lower data rate streams [11]. Each stream requires a bandwidth that is much smaller than the coherence bandwidth of the channel, which in turn makes the information in OFDM inherently immune to frequency selective fading. Additionally, the low complexity of OFDM schemes [12] has ensured that fourth-generation (4G) and fifth-generation (5G) mobile networks will also largely rely on OFDM for providing high-speed data access to the end-users. The LTE wireless systems have been implemented based on OFDM in the 4G, which provide various types of traffic, fair scheduling, and balanced Quality of Service (QoS). LTE offers good QoS delivery for distinct traffic classes. The strategy of QoS balancing scheduling rules in an LTE downlink is suggested to provide high QoS delivery for different traffic classes using MU-MIMO [13]. Recently, many enabling technologies such as massive MIMO [14] have been introduced to support data rates beyond 5G (B5G) and sixth generation (6G) networks [4]. In mMIMO base station (BS) is equipped with a huge number of antennas, much larger than the number of serviced users [15; 16]. mMIMO increases network capacity and data throughput by splitting data packets over multiple signal paths as well as allowing simultaneous multiple users by using multi-user MIMO (MU-MIMO). mMIMO is, therefore, an enhanced version of MU-MIMO, with advanced features such as increased spectrum efficacy and simplified signal processing, while MU-MIMO was used in the previous mobile generations due to its advantages over point-to-point MIMO, mMIMO is expected to be used in B5G and 6G due to its advantages over MU-MIMO [17; 18]. In mMIMO the base station (BS) dedicates a narrow beam towards the direction of each user. The angular beam can be made further narrower and more concentrated in the direction of each user by elevating the number of antennae in the BS. Increasing the number of antennas in the BS not only enhances the sum rate spectral efficiency per cell but also simplifies the required signal processing at both BS and end-users terminals in comparison with MU-MIMO which is largely attributed to channel hardening. Channel hardening precisely refers to a condition when the number of transmitting antennae is large enough to decimate the randomness in the communication channel. As a result, the channel between users and the BS becomes flat with an almost constant gain and additive white Gaussian noise (AWGN) with minimal impact of fading channels and frequency selective fading [19]. Additionally, channel hardening can simplify channel estimation at the users' terminals (i.e., downlink (DL)), while in uplink (UL) the BS runs the channel estimation with the aid of received pilots from each user relying on channel reciprocity between UL and DL in the time division duplexing (TDD) mode [20].