Performance Evaluation of Channel Estimation and Detection Algorithms for 5G Massive MIMO System

This thesis introduces full filled description for fifth-generation technology, here concentrated on massive MIMO technology in detailed at channel detection. However, channel illustrated carefully, described how to estimate and detect the channel. By using both algorithms least square and minimum mean square error for channel estimation while zero forcing and minimum mean square error for channel detection by analyzing and measure their performance using BER for MIMO 2x2, also it shows these algorithms on massive MIMO but it offers high BER and latency. In addition, simple algorithms used for equalizing the channel are Gauss-Jordan Elimination, Gaussian Elimination, RQ Decomposition and LU Decomposition. In which MATLAB simulation used to analyzed and applied mathematical models. After that measured the BER, delay for each algorithm and evaluate the capacity and throughput, by way, found that the Gaussian Elimination has better delay about 49% when RQ Decomposition about 95% while LU Decomposition about 98% compared by Gauss-Jordan Elimination. In addition, show their performance at capacity and throughput for various modulation and coding rate, while the deliverables average capacity about 10 M bit and affected by the situation of the channel, LU has the best performance than other.