Abstract
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.
Highlights
The fifth generation (5G) system is expected to provide high quality communication service
Compared with the joint channel estimation and active device detection scheme [11]–[14], we proposed a scheme combined with superimposed training, so that data recovery and channel estimation can be conducted simultaneously
THE PERFORMANCE OF ACTIVE DEVICE DETECTION According to the above Block Orthogonal Matching Pursuit (BOMP) algorithm, the correlation between the received signal and the training sequence of devices is a key factor to decide whether the device is active
Summary
The fifth generation (5G) system is expected to provide high quality communication service. The block sparse structure of massive MIMO is especially well suited for active device detection, the length of training symbols can be further reduced. The number of active devices is large and growing for many access system, the time frequency resources reserved for training is unavoidably high and lead to low overall efficiency of data transmission. The main contributions of our work are listed as follows: 1) Proposing the cross transmission superimposed training scheme for mitigation of the cross interference between data and training symbols in uplink massive connection scenario. Compared with the joint channel estimation and active device detection scheme [11]–[14], we proposed a scheme combined with superimposed training, so that data recovery and channel estimation can be conducted simultaneously. The operators (.)T , (.)H and ⊗ are the transpose, conjugate transpose and the Kronecker product of matrix respectively. ||A||F and ||a||2 denote the Frobenius norm of matrix A and Euclidean norm of vector a respectively
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