Simultaneous Channel Estimation and Data Detection Based on Superimposed Training for Many Access MIMO System in Uplink

Massive Multiple Input Multiple Output (MIMO) has great potential to improve spectrum efficiency in the fifth generation (5G) wireless communication systems. However, the efficiency was disastrously reduced by the heavy burden of overhead for device detection and channel estimation of the large amount of small data packets in the uplink channel. In the paper, we proposed a novel transmission scheme by superposing the training symbols for active device detection and channel estimation on the data symbols in the uplink transmission to improve the efficiency. More specifically, in order to mitigate the cross interference among the superposed signals, we proposed to split the transmission into the training phase and the traffic phase. Then we superposed the training phase in the next transmission with the traffic phase in the current transmission. Furthermore, we give the optimization of power allocation ratio between training phase and traffic phase to obtain the optimal overall performance. The analytic and simulation results show that, with the help of spatial isolation among devices, our proposed transmission scheme can significantly improve the transmission efficiency compared with the existing schemes.

Abstract licence: CC BY-NC-ND

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