y Optimization of Beamforming Weights Simulation Results and Discussion 3.6 Conclusion References INTRODUCTION Massive multiple-input multiple-out (mMIMO) constitutes a base station (BS) having a much higher number of antenna elements compared to the total number of users in its vicinity [1]. mMIMO systems are efficient in spectral and power fronts, where the infrastructure change is required mainly at the BS instead of mobile stations (MS) [1], [2]. Hence, a mMIMO system renders a better network capable of delivering the performance objectives of the emerging generation of communication systems [3], [4]. In the design of mMIMO systems, a consideration of partial channel state infor- mation (CSI) is often considered following the exact closed-form expression of key performance indicators (KPIs) under maximum ratio transmission (MRT), or zero for- cing (ZF) transmit beamforming schemes [5-7], or by considering perfect CSI acces- sibility [8]. Also, several approximate results for KPIs are given in [9] by employing asymptotic schemes using order statistics and extreme-value theory referred in [10]. Furthermore, in the downlink system and along these lines, the impact of transmis- sion antenna spatial correlation on sum rate capacity is postulated in [11]. A closed- form expression of capacity of the multipath independent Rayleigh fading channel is given in [12]. A model without consideration of co-channel interference is analyzed in [13] using the characteristic function of MIMO with the assumption of no instant- aneous CSI at the transmission side. DOI: 10.1201/9781003368311-3 5G Wireless Communication System in Healthcare Informatics Motivated by the discussion above, in this chapter we provide a more comprehen- sive framework for analysis and design of beamforming weights based on the recently introduced approach of indefinite quadratic forms [14]. The content of this chapter in mainly based on these works given in [14-18]. More specifically, we provide a frame- work for the canonical formulation of signal-to-in