Efficient Channel Estimator With Angle-Division Multiple Access

Massive multiple input multiple output (M-MIMO) is an enabling technology of 5G wireless communication. The performance of an M-MIMO system is highly dependent on the speed and accuracy of obtaining the channel-state information. The computational complexity of channel estimation for an M-MIMO system can be reduced by making use of the sparsity of the M-MIMO channel. In this paper, we propose the hardware-efficient channel estimator based on angle-division multiple access for the first time. Preamble, uplink, and downlink training are also implemented. For further hardware-efficiency consideration, optimization regarding quantization and approximation strategies has been discussed. Implementation techniques, such as pipelining and systolic processing, are also employed for hardware regularity. Numerical results and field-programmable gate array implementation have demonstrated the advantages of the proposed channel estimator.