# On Deploying the Internet of Energy with 5G Open RAN Technology including Beamforming Mechanism
## 4 Implementation of Beamforming in Open RAN Networks
### System Model and Simulation Results  
In O-RAN, zero-forcing may be used only in digital beamforming at the O-RU, however the augmented (in comparison to purpose-built 5G implementation) number of hardware operations due to the control of the open RAN interfaces increases the inaccuracies in orthogonality, which results in an increase in the noise of the channel. In order to simulate the benefits of zero-forcing in O-RAN implementation, we separate the component of the channel noise (including O-RU hardware impact) and the interference of the signal sent to other users. The interference will reduce the spectral efficiency (number of bits that may be transmitted per Herz of frequency bandwidth), which is a parameter of the capacity of the system to send information to the user's mobile device. In our simulations, we calculate the spectral efficiency for different values of Signal-to-Noise ratios (SNR), so that the channel noise is a parameter that may be estimated for the channel regardless of the use or not of the coding method. The fact that we use O-RAN instead of classical purpose-built 5G network implementation causes lower SNR, and this must be considered in our conclusions. In order to simulate the zero-forcing precoding in beamforming implementation (referred to as ZF BF precoding), we assume a downlink MIMO channel between a base station BS (Base Station) including \(Nt\) transmits antennas and \(K\) active mobile devices (users). The mobile device owns one antenna. Thus, there are \(Nb~{}=~{}K\) downlink streams from BS to each one of the \(K\) mobile devices. The maximum number of users \(K\) that can be served simultaneously by the BS is equivalent to the number of RF chains at the BS, that is \(K~{}<~{}Nt\). We assume the perfect information of the channel state (which is sent by the users to the BS through the Channel State Information message), so that the BS may send perfectly orthogonal signals to the users. As far as the air interface is concerned, we assume a Rayleigh fading channel, which is a valid model in urban scenarios [34]. The channel is frequency-flat (no frequency selectivity) and static (without Doppler effect). The Rayleigh fading model is set to each path independently with null standard deviation and an increasing value of Gaussian noise vector in the channel (SNR). For the channel simulation, Matlab comm.MIMOChannelfunction is used, which, on its part, makes use of comm.RayleighChannel function for the simulation of the Rayleigh channel. In our Matlab simulations, we apply digital zero-forcing beamforming (ZF-BF) and compare it with two reference models: the first one is without interferences (one single mobile device or user) and zero-forcing beamforming, the second reference is a transmission with interferences (number of mobile devices higher than one) and without zero-forcing beamforming. Our implementation assumed a variable number of transmitting antennas and a variable number of users (mobile devices) that communicated through the MIMO channel. The ZF-BF allows receiving a separate received signal at each user, which reduces the total power of the noise and, as a result, increases the signal-to-noise-to-interference ratio (SNIR). This method minimizes the inter-symbol interference (ISI) components in the output (making the ISI zero in a theoretical noise-free cause interference). This means that higher values of SNR (lower noise) should result in better response of the system in the case of zero-forcing precoding. In our results, we assume a SNR range from \(-10\) to \(30\) dB. We use Matlab to simulate signal quality by determining the throughput for the \(k\)-th user and assuming a downlink massive MIMO system with various transmit antennas and active mobile users; each user has one RF chain. The throughput divided by the bandwidth is the spectral efficiency presented in the following figures. Figure 4 shows the impact of precoding in a noisy channel with multiple antennas and multiple paths.