PILOT CONTAMINATION BASED ENERGY EFFICIENT MODEL FOR MASSIVE MIMO SYSTEMS FOR 5G

Electronic and Communication

Our communication lot of antennas, they can get a lot more spectrum and use less energy. This will be a big part of 5G. For wireless communication, MIMO is a way to use a multicarrier transmission method called "multicarrier transmission." High data rates were used in this case, and they were used to send the data. This huge amount of data can be broken down into many different types of data. Because of this, MIMO is very sensitive to the receiver synchronisation errors, such as carrier Channel offset (CCO) and Doppler shifts and local oscillator instability, which make inter carrier interference (ICI) even worse. Existing methods for estimating carrier Channel offsets have a problem called "particle impoverishment." This means that statistically, heavier particles are chosen for Channel estimation because they are more likely to be. This choice is not good for improving performance. This paper comes up with a way to deal with the Particle impoverishment problem. It proposes using DFT Based Superimposed Pilot Aided Approach with Winner Filter for Carrier Channel Offset Estimation in MIMO Systems. The Winner filter is used in the proposed method to figure out the density and weight of the particles that have been filtered. Finally, based on the weight of filter particles, the proposed system can figure out the CCO for a MIMO signal. An analytic expression of the mean square error (MSE) and the SNR ratio of Channel offset synchronisation is shown, and simulation results show that the theoretical analysis is correct. The simulation results show that the MSE is reduced by about 1.99X, 1.56% in energy and the SNR ratio is increased by about 73%.

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