LTE with the inclusion MIMO renders high data rate, we introduce a model for system performance evaluation considering typical coexistence of LAA LTE and WiFi. Currently, as the need for high bandwidth to satisfy high data rate demanding services increased significantly over the years, the operators are thinking about various answers for incrementing system limit. As the authorized range has been costly, utilizing LTE in the unlicensed spectrum will be a hopeful strategy to oblige trade requirement for versatile information. Expecting that LAA eNBs perform CCA as a similar way to WiFi CSMA, we utilize Markov chain to help assess the influenced execution of WiFi as well as the connection between the joint framework execution and LAA most extreme transmission time. Nonetheless, LTE needs to exist together with different heterogeneous system, particularly WiFi, prior in the unlicensed medium.

Massive MIMO has gained much attention with the increase in the high speed data communication. The problem of peak-to-average power ratio (PAPR) is considered, the detrimental aspects in OFDM based massive multiple-input multiple-output (MIMO) downlink systems. The previous works done in reduction of PAPR problem using convex optimization are computationally inefficient. We considered Bayesian approach to mitigate PAPR by utilizing the redundant degrees of freedom (DOF) of the transmit array, which effectively reduced the level of PAPR. The performance or numerical results indicate the applied algorithm achieved a good improvement over the existing techniques in terms of the PAPR reduction.

Massive MIMO systems are the key technology for evolution of 4G, 5G standards in telecommunication environment. One of the major limitations in OFDM based massive multiple-input multiple-output (MIMO) downlink system is peak to average power ratio (PAPR). Transmitting symbol vectors to different set of users, the main idea is estimate the low PAPR OFDM modulated signal with reduced multi user interference. Many techniques were used to mitigate the PAPR problem, but they consume more computational time, particularly in Massive MIMO systems. The proposed ETG (Expectation maximization Truncated Gaussian mixture Generalized approximate passing) employs truncated Gaussian mixture prior to get low PAPR signal. To understand the prior signal, expectation step helps in identifying hidden variables; maximization step helps in identifying deterministic parameters. Generalized approximate passing is applied to mitigate the computational complexity. Numerical simulated results in comparison with existing techniques suggests that desired level of PAPR is achieved with less computation time with minute degradation in symbol error rate(SER).By choosing proper normalization we can achieve same SER with reduced PAPR.