Background:Single user Massive Multiple Input Multiple Output (MIMO) can be used to increase the spectral efficiency since the data is transmitted simultaneously from a large number of antennas located at both the base station and mobile. It is feasible to have a large number of antennas in the mobile, in the millimeter wave frequencies. However, the major drawback of single user massive MIMO is the high complexity of data recovery at the receiver.Methods:In this work, we propose a low complexity method of data detection with the help of re-transmissions. A turbo code is used to improve the Bit-Error-Rate (BER).Results and Conclusion:Simulation results indicate a significant improvement in BER with just two re-transmissions as compared to the single transmission case. We also show that the minimum average SNR per bit required for error-free propagation over a massive MIMO channel with re-transmissions is identical to that of the Additive White Gaussian Noise (AWGN) channel, which is equal to -1.6 dB.
Real-Valued Sparse Bayesian Learning Approach for Massive MIMO Channel Estimation
