t identifies the kth user, as follows (3.2) To simplify, we express s as the ratio of indefinite quadratic form which yields (3.3) Next, we consider a whitened version of the channel, that is, Consequently, s simplifies to (3.4) Note that the length of the channels is dependent on the number of antennas at the transmit side. 3.3 CHARACTERIZATION OF OUTAGE PROBABILITY In this section, we employ IQF methodology to characterize the OP of the Kth user in the K user scenario. Again, the content of this section is adopted from [16] where we employ the IQF approach, and hence characterize the OP of the Kth user in the down- link Gaussian broadcast scenario. Now, using Equation 3.5 and for a given threshold 8th, the CDF, that is, Pr (xx<<) is expressed, for notational shortness, as Pk.out (r) and is given by: 5G Wireless Communication System in Healthcare Informatics hill2 Pk,out (3.5) Hence, using the residue theory approach, the closed-form solution of OP for the kth user in the mMIMO downlink environment is given by (3.6) where An denotes the nth eigenvalue of - given that all eigenvalues are distinct. On the otherhand, if the eigenvalues are repeated, then (3.7) where As,1 denotes the partial fraction expansion coefficients of which can be evaluated using the following approach: (3.8) 3.4 OPTIMIZATION OF BEAMFORMING WEIGHTS The outage probability derived in Equation 3.6 is a non-convex function which is difficult to optimize. Thus, it is converted into a convex problem using linear scalarization (L-S). Such formulation results in Pareto optimal solutions for each Beamforming in Massive MIMO Communications of the single objective functions. This is achieved by defining a single objective function J which is formed by the linear scalar sum of the outage probabilities of all users, that is, (3.9) where the objective function shows that it is a function of beamforming weights. To obtain an optimized solution, we propose to minimize the following constrained objective function (3.10) subject to 11 w 2 11=1 Thus, the propo