## 5 Results of the Proposed Power Allocation Scheme  
This section presents numerical findings that illustrate the DASR performance of the RZFP with different power allocations with respect to the training sequence phase and data phase. Hence, we compare the proposed method with optimized power allocation with the conventional method that uses uniform power allocation. Our objective is to maximize the DASR in MMIMO systems with the FDD operation. The lines represent an analytical expression for the SINR of the RZFP that has been developed in this paper and obtained based on the expression provided in Equation (36). The markers denote the simulation of the SINR that has been determined based on the expression in Equation (9).Both the lines and markers are combined in the figures for notation convenience. Figure 3 depicts the DASR with the correlation coefficient, thereby comparing the proposed power optimization method with the conventional method, with \(\tau_{\rm c}=50\), \(K=10\) users, \(\mathrm{SNR}=10\) dB, and a correlation coefficient of 0.1, which implies a strong correlation, while 0.9 implies an uncorrelated channel. The red and black lines with markers depict the BS antennas of \(N=200\), while the magenta color and blue lines with markers illustrate the BS antennas of \(N=500\). The results demonstrate that with short coherence time and relatively strong correlation, the DASR was significantly increased and higher than that obtained when \(N=500\). The results also demonstrate that an improvement of around 9 bit/s/Hz in the DASR was achieved. In addition, the results presented here show that a marked improvement in the DASR was achieved with the proposed power optimization method. For example, when \(N=200\), around 3 bit/s/Hz was achieved with the proposed method across all the correlation coefficient ranges considered. In addition, when \(N\) was increased to \(N=500\), around 4 bit/s/Hz of improvement in the DASR was achieved at relatively high correlated scenarios. Furthermore, the results demonstrate that in order to achieve 30 bit/s/Hz, the proposed optimization method achieved a gain in power of around 3 dB, which signifies the applicability of the proposed method in a realistic scenario. In order to observe the DASR performance with different total transmit power values, Figure 4 investigates the DASR performance of the proposed power allocation method in comparison with the conventional method using different SNR values. The configurations were chosen as \(K=10\) users, \(\tau_{\rm c}=50\), \(N=200\), and \(N=500\), and the correlation coefficient was 0.1. The results show that when the transmit power was reduced, i.e., \(\mathrm{SNR}=-5\) dB, there was a slight improvement in the DASR for both of the scenarios considered. However, upon increasing the power, i.e., \(\mathrm{SNR}=5\) dB, \(\mathrm{SNR}=10\) dB, and \(\mathrm{SNR}=15\) dB, a significant improvement in the DASR was achieved using the proposed power allocation method. Figure 5 investigates the DASR performance with varying amounts of \(N\) antennas comparing the proposed power optimization method with the conventional method when a different number of users \(K\) was considered. The configurations were \(\tau_{\rm c}=50\), the average transmit power was considered so that \(\mathrm{SNR}=10\) dB, and the correlation coefficient was 0.1. The results show that a considerable enhancement in the DASR was achieved when  
Figure 3: DASR with correlation coefficient comparing different power allocation schemes with different numbers of BS antennas: \(N=200\) and \(N=500\). ened residual interference. Additionally, in a scenario with limited coherence time, where the prelog fraction designates the time allocated to channel estimation, a considerable impact on the DASR arises, since the number of \(N\) far exceeds the considered coherence time, thereby leaving insufficient time for transmitting useful data after CSI estimation.