Multiple input multiple output (MIMO) is a multiple antenna technology used extensively in wireless communication systems. With the ever increasing demand in high data rates, MIMO system is the necessity of wireless communication. In MIMO wireless communication system, where the multiple antennas are placed on base station and mobile station, the major problem is the constant power of base station, which has to be allocated to data streams optimally. This problem is referred as a power allocation problem. In this research, singular value decomposition (SVD) is used to decouple the MIMO system in the presence of channel state information (CSI) at the base station and forms parallel channels between base station and mobile station. This practice parallel channel ensures the simultaneous transmission of parallel data streams between base station and mobile station. Along with this, water filling algorithm is used in this research to allocate power to each data stream optimally. Further the relationship between the channel capacity of MIMO wireless system and the number of antennas at the base station and the mobile station is derived mathematically. The performance comparison of channel capacity for MIMO systems, both in the presence and absence of CSI is done. Finally, the effect of channel correlation because of antennas at the base stations and the mobile stations in the MIMO systems is also measured.

Non-Orthogonal Multiple Access (NOMA) in the fifth generation (5G) system is one of the optimistic technologies for wireless radio access networks. Compared to orthogonal multiple accesses (OMA) reduce the spectral efficiency; NOMA provides the best solution by increasing the data rates. This study evaluates NOMA with a downlink in the automatic deployment of multiusers. The outage performance and ergotic sum-rate gain give the NOMA better performance can be concluded at the final results. NOMA provides the Quality of Service (QoS) to the multi-users by considering the power allocation and data rate factors. Here is considered the outage probability will be 1 when it identifies the different user and allocates the data rate and power.

Nowadays, there is a trend to employ adaptive solutions in mobile communication. The adaptive transmission systems seem to answer the need for highly reliable communication that serves high data rates. For efficient adaptive transmission, the future Channel State Information (CSI) has to be known. The various prediction methods can be applied to estimate the future CSI. However, each method has its bottlenecks. The task is even more challenging while considering the future 5G/6G communication where the employment of sub-6 GHz and millimetre waves (mmWaves) in narrow-band, wide-band and ultra-wide-band transmission is considered. Thus, author describes the differences between sub-6 GHz/mmWave and narrow-band/wide-band/ultrawide- band channel prediction, provide a comprehensive overview of available prediction methods, discuss its performance and analyse the opportunity to use them in sub-6 GHz and mmWave systems. We select Long Short-Term Memory Recurrent Neural Network (RNN) as the most promising technique for future CSI prediction and propose optimising two of its parameters - the number of input features, which was not yet considered as an opportunity to improve the performance of CSI prediction, and the number of hidden layers.