# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 1 Introduction  
To support the upcoming novel applications, such as IoT, self-driving cars, Industry 4.0, smart healthcare systems, AR/VR services, fifth generation (5G) and beyond fifth generation (B5G) wireless networks aim to achieve ultra-low latency and ultra-reliability with a multi-gigabit transmission rate [1]. The combination of heterogeneous network (HetNet) solutions and massive MIMO promises significant improvement in the physical layer performance by deploying an ultra-dense network with an ultra-high spectral efficiency [2, 3]. Massive MIMO is the next generation MIMO system that significantly enhances the spectral efficiency of the communication link compared with its conventional counterpart. With this technology, a few hundred antennas are deployed at the base-station (BS) to serve a few tens of active users in same time-frequency grid [4, 5]. Massive MIMO is considered a key technology for upcoming wireless generations to support 100\(\times\) data rates per user and per cell by implementing adaptive beamforming and spatial multiplexing technologies with large antenna arrays [6]. HetNet is a network densification technique in which several classes of low-powered transmitters are deployed with the existing macro cells sharing the same spectrum. The low-powered small-/pico-cells are deployed to target highly concentrated user groups. The network densification process significantly improves the coverage penetration and area spectral efficiency of the network with non-uniformlydistributed users [7]. By optimizing resource utilization and network performance, HetNets are going to be a principal candidate for the implementation of 5G and B5G networks [8]. For achieving customer satisfaction, which is the aim of network service providers, the latter continuously upgrade the network for maximizing the coverage probability and achievable rate for the end users. The deployment of massive MIMO HetNet systems for the upcoming network generation needs precise planning and high capital expenditure. Hence, before deployment, network service providers must optimize network parameters in order to meet the desired goal, which requires precise performance analysis. The analysis and estimation of massive MIMO system performance before practical deployment has been a major concern of research for the past few years. An asymptotic analysis of the coverage probability and sum-rate of a single-cell massive MIMO system has been presented in the literature [9]. The performance of single-cell downlink massive MIMO in terms of spectral efficiencies and link reliability using various precoding techniques has been analyzed and compared by the authors of [10]. [PERSON] et al. have proposed a performance evaluation technique for massive a MIMO system based on propagation data [11]. [PERSON] et al. have modeled the user-interference power distribution in a single-cell multi-user massive MIMO system using Gamma function, and formulated asymptotic deterministic equivalences for sum-rate and outage probability in terms of tight-form approximation of the model [12]. The performance of a massive MIMO system in a multi-cell scenario is limited because of the presence of inter-cell interference. A typical user in a multi-cell scenario associated with a given BS receives signals from other BSs as interference. [PERSON] et al. have done a statistical analysis of the interference present in a massive MIMO system, considering inter-cell interference as the dominant component [13]. [PERSON] et al. have derived a large-scale approximation of the downlink signal power to interference plus noise power ratio (SINR) in a multi-cell massive MIMO system with their proposed MMSE precoder [14]. [PERSON] et al. have done a tractable analysis of the interference in uplink for a large-scale antenna system [15].