# Hybrid Beamforming in Massive MIMO for Next-Generation Communication Technology
## 5 Hybrid Beamforming and Massive MIMO  
The requirement of unique RF chains for each antenna leads to an unmanageable level of complexity. In this context, suboptimal beamforming algorithms are employed that are based on a hybrid beamforming approach. Hybrid beamforming is introduced to achieve good flexibility, control over amplitude and power consumption, and cost [7]. Hybrid beamforming systems adjust digital and analog beamformers to boost the achievable  
Figure 5: Massive MIMO with AWGN.  
rate. Hybrid beamforming was invented almost 15 years ago but has seen a dramatic upward trend over the past 5 years due to its importance in making massive MIMO cost- and energy-efficient [38]. All hybrid architectures aim to provide performance close to digital beamforming while reducing hardware and precoding complexity. The two primary types of hybrid beamforming designs are partially connected and fully connected hybrid beamforming. In partially connected hybrid beamforming, a group of antennas is coupled with an RF chain and completely connected, in which every antenna is connected to every RF chain. The two architectures pay off complexity for gain in different ways. The number of signal processing paths is (\(\xi\times\psi^{2}\)) for fully linked design with \(\xi\) transmit antennas and \(\psi\) RF chains, whereas it is (\(\xi\times\psi\)) for sub-connected architecture. However, the fully linked architecture's beamforming gain is \(\psi\) times bigger than the sub-connected architecture. For employing beamforming to enhance efficiency, the hybrid regularized zero-forcing (HRZF) algorithm is applied [8].  
To compute the EE, a precise power model is required. This is not simple because BSs come in various forms and are typically manufactured by multiple suppliers using different execution skills. The following straightforward power model is utilized in this section:  
\[\mathrm{P_{total}}=\mathrm{NP_{static}}+\mathrm{NP_{0}}+\mathrm{P_{common}}+ \mathrm{NMP_{rf-circuit}}, \tag{8}\]  
\(\mathrm{P_{static}}\) is the base station's static power, \(\mathrm{NP_{0}}\) is a portion of power that varies with the number of transceivers, \(\mathrm{P_{common}}\) is similar for any transceiver number, and \(\mathrm{MNP_{rf-circuit}}\) scales with the total number of antennas.  
_Hybrid regularized zero-forcing_ (HRZF) is a precoding technique used in wireless communication with multiple antennas. It combines the regularized zero-forcing (RZF) and maximum ratio transmission methods (MRT). The HRZF precoding technique uses both RZF and MRT to achieve a trade-off between signal alignment and noise suppression while leveraging the advantages of both techniques. The RZF component helps to suppress noise and interference, while the MRT component enhances the signal power. This results in a more efficient and robust communication system and is expressed as follows.  
\[\mathcal{W}=\alpha(H^{H}H+\eta I)^{-1}H^{H}+(1-\alpha)H^{H}(HH^{H}+\mathbb{B}I) ^{-1}, \tag{9}\]  
where \(\alpha\) is the weighting factor for RZF, \(\mathbb{B}\) is the weighting factor for MRT, and \(\eta\) is the regularization parameter for RZF. First, the RZF precoding matrix is estimated, and in the second step, the MRT precoding matrices are estimated; finally, the hybrid of both is applied, which is more convenient than both of its contributors.  
_System Model:_ The system representation for hybrid beamforming in MIMO is shown in Figure 6, showing \(\xi_{t}\) number of transmitting elements and, at the receiver side, each user with \(\xi_{t}\) elements.  
Figure 6: System model for hybrid beamforming in massive MIMO.