sed beamforming method tries to find an optimal solution by min- imizing a linearized sum of all users' outage probabilities while constraining their beamforming powers to unity. 3.5 SIMULATION RESULTS AND DISCUSSION For the simulation task of this work, we consider the scenario of massive MIMO communications where an N element transmit antenna BS is communicating through a Rayleigh channel with prior knowledge of the respective channel correlation matrix R.. The correlation matrices are based on correlation coefficient p such that R = piki and 0p1 The extreme values of P (0 and 1) show uncorrelated and fully correlated channel scenarios, respectively. In Figure 3.2, the closed-form expression of OP is matched by with wide-ranging simulation means. We have considered mMIMO with N = 128, as well as a smaller diversity order of N = 4. We consider two cases of total users, namely, K = 6, and K = 12. Other parameters are set as O2=10 = dBm, and p = 0.15 Now, for predefined threshold 8th, the OPof a given user is directly proportional to the total number of cochannel interferes. It can be noted that, initially at the low value of 8th, the OP is higher for a high number of transmit antennas, though this situation overturns as 8th increases for both cases of users, namely, K = 6, and K = 12. A good degree of match exists between the analytical and the simulated results, thus validating the closed- form expressions. Next in Figure 3.3, the impact of Aon OP is shown by considering N = 128, and for K = 4, and K = 12. Now, for the whitened scenario, namely, p=0, and for the fully correlated case, namely, p = 1, we convey the OP with respect to 8th As per our prior observation, for lower 8th, the OP is higher for the p= 1 case, however this pattern reverses with increasing value of 8th. Lastly, in Figure 3.4, the proposed optimization method is implemented using the MATLAB built-in optimization function "fmincon" via the "Sequential Quadratic Programming" method. The results show that the proposed method minimizes the 5G Wir