## 5 Massive MIMO  
The Multi-input multi-output technology (MIMO) has been almost a decade, but equipping base stations (BS) with multiple antennas is a new concept in 5G and beyond 5G (B5G), called massive MIMO (mMIMO) [95; 112; 113; 114]. The mMIMO has proven an efficient player for industrial Internet of things (IIoT) networks [115], mobile edge computing [116], virtual reality [117], 5G wireless communication networks [118], autonomous driving [119], augmented reality [120] and wireless sensor networks [121; 122; 123]. The advantage of mMIMO includes a massive shoot-up in SE, a minimized latency, and an expandable air interface arrangement. It is a multi-subscriber technology, in which every BS is equipped with a large number of active antennas M, arranged in an array and simultaneously communicates a set of single-element K customers on a similar frequency and time resource such that \(M>>K\) making the signal pre-coding process simple and increase SE [124; 125]. The difference between the 4G sector antenna and 5G massive-MIMO geometry is shown in Figure 5 which consists of \(>\)100 elements. The huge nature of mMIMO itself is the cause of reduced latency as it removes the channel frequency dependency which stops the effect of frequency selective fading on the signal strength. Therefore, every transmitted signal safely arrives at the receiver without suffering channel distortion with decreased latency. The mMIMO can be implemented by two feasible approaches: full digital configuration and hybrid configuration. In fully digital mMIMO a wide-range digital signal processing unit is required along with a digital-to-analog converter for every an  
Figure 5: Difference between 4G and 5G massive-MIMO antenna. tenna element [126; 127]. The hybrid (analog-digital) mMIMO system that merges analog beamforming and digital MIMO signal which overcomes the amount of digital-to-analog converters [128; 129; 130]. The first implementation of mMIMO in real time and public implementation is, with \(M=100\) and \(K=10\)[131]. In mMIMO, an antenna array of the base station contains \(M\) dipole antennas with \(\lambda\) wavelength, each consisting of an effective size of \(\lambda/2\times\lambda/2\). This implies that an array of 1 m\({}^{2}\) can fit a hundred antennas at 1.5 GHz and 400 antennas at 3 GHz carrier frequency [132]. The array can be of any geometry; cylindrical, distributed arrays, linear or rectangular. Table 4, describes the unique features of mMIMO which make it prominent from other key enabling technologies effectively. The SE of a specific group j is impacted by the model signaling completed in different cells. Assume, the pilot reuse factor f is an integer with the distribution of \(L\) sections into \(f\) split cell groups: \(f=\tau_{p}\) / \(K\). The global pilot reuse \(f=1\) and non-global pilot reuse \(f>1\) are very well known. As the hexagonal cell topology contains six cells in each stage, the lowest pilot reuse factors which increase to symmetric pilot reuse patterns are \(f=1\), 3, 4. Consider an mMIMO scenario with base station antennas M = 200 and \(\tau_{c}=400\) symbols a coherence interval, where customers are homogeneously distributed in the cell except for the 10% cell center. These channels show uncorrelated Rayleigh fading by a distant dependent channel attenuation having a path loss exponent of 3.7. By taking \(f=1\), 3, 4 suppose an easy policy for power allocation. \[\rho_{j,k}=\frac{\delta}{\beta_{j,k}^{j}} \tag{2}\]  
where \(j=1,2,\cdots,L\) and \(k=1,2,\cdots,K\) Here \(\delta>0\) is a configuration framework that calculates the achieved SNR at every BS antenna and is known as statistical channel inversion power allocation:  
\[\rho_{j,k}\times\frac{\beta_{j,k}^{j}}{\sigma_{BS}^{2}}=\frac{\delta}{\sigma_ {BS}^{2}} \tag{3}\]  
The average SE is an outcome of the different users with non-identical pilot reuse factors and processing schemes by considering two separate SNR levels \(\delta/\sigma_{BS}^{2}=0\) and 20 dB.