The upcoming generation of cellular networks is going to make extensive use of mmWave for communication. Hence, there will be a need for small cells to counter the loss incurred due to the more energy dissipation of mmWave. Small cells contain transmitters and receivers and hence there will be the need to balance the load efficiently so that no one is overwhelmed with functions to perform while others are relatively idle. In this paper, the performance of the user association algorithm is analysed when it is subjected to different scenarios like micro-urban, macro urban, suburban and rural. these scenarios are subjected to different frequency bands of mmWave and are compared for the values of the load balancing index. The load balancing algorithm is subjected to different small cell deployment techniques and the comparison is made for best deployment strategy among Quadrature based Approach (QBA) and Random deployment. Simulation results show that the sub-urban scenario has the maximum load-balancing index. On comparing QBA and random deployment approach QBA has a higher load balancing index in the suburban and rural scenarios and random deployment has a higher load balancing index in Indoor environments.

As an innovative implementation, Cell-Free Massive Multiple Input Multiple Output (MIMO) has appeared in typical Cellular Massive MIMO Networks. This protocol doesn’t recognize cells, as shown by its name, even though a significant number of APs operate on the same frequency/time resources. Connection from multiple distributed access points through joint signal processing is called Cell-Free Massive MIMO. The Cell-Free Massive MIMO System, a contrast between Cell-Free Massive MIMO Systems and Distributed Massive MIMO, the prime focus in this thesis is on Cell-free Massive MIMO and, along with this discussion, on Cell-free Massive MIMO signal processing, Channel Estimation, Uplink Signal Detection, Cumulative Distribution, Spectral Efficiency & Ubiquitous Cell-Free Massive MIMO Model. Ubiquitous Cell-free Massive MIMO contributes to a Massive MIMO system, a distributed system that implements consistent user-centre distribution to solve that constraint of mobile phone interferences as well as to introduce macro-diversity. We investigated the Cell Radius at different locations in CDF with Spectral Efficiency [bits/s/hertz]. Cell-Free Massive MIMO is an evidence-based preventive of massive MIMOs with distributed high percentage APs that serve even lower margins. The cell-free model is not segregated into cells and any individual is concurrently represented by every Access point.