FD-MIMO via Pilot-Data Superposition: Tensor-Based DOA Estimation and System Performance

Increased pilot overhead is one of the major issues for 5G full-dimension MIMO (FD-MIMO) systems. In this paper, we introduce the FD-MIMO system using pilot-data superposition to reduce the uplink pilot overhead and investigate the impact of superimposed pilots on the overall network performance. The pilot-data superposition can relatively increase the resources allocated to the uplink data transmission. However, in time division duplex (TDD) systems, it also negatively impacts the downlink throughput since the relatively reduced pilot power can affect the estimation of the channel state information (CSI) which is used for the downlink precoding. To improve the CSI estimation, the intrinsic tensor feature of the FD-MIMO channel is exploited in our method. The introduced CSI estimation algorithm is designed through the expectation-maximization (EM) framework via tensor as the processing data structure. Furthermore, Cramér-Rao lower bound (CRLB) is utilized as a metric in our evaluation. The overall system achievable rate which is a weighted sum of the uplink and downlink throughput is investigated to reveal the fundamental trade-off between the uplink and downlink transmission in the TDD-based FD-MIMO system. In our simulation, the results demonstrate the superior performance of the introduced strategy as opposed to the conventional orthogonal pilot-data approach. Meanwhile, the pilot power allocation, as well as different transmitting and receiving strategies are investigated to offer various trade-off points between the uplink and downlink transmission.