
Research Article
Channel Estimation for Millimeter Wave MIMO System: A Sequential Analysis Approach
@INPROCEEDINGS{10.1007/978-3-030-95987-6_3, author={Jinduo Zhang and Rongfei Fan and Peng Liu}, title={Channel Estimation for Millimeter Wave MIMO System: A Sequential Analysis Approach}, proceedings={IoT as a Service. 7th EAI International Conference, IoTaaS 2021, Sydney, Australia, December 13--14, 2021, Proceedings}, proceedings_a={IOTAAS}, year={2022}, month={7}, keywords={Millimeter wave Channel measurement Multiple input multiple output (MIMO) system Sequential analysis Semi-definite programming}, doi={10.1007/978-3-030-95987-6_3} }
- Jinduo Zhang
Rongfei Fan
Peng Liu
Year: 2022
Channel Estimation for Millimeter Wave MIMO System: A Sequential Analysis Approach
IOTAAS
Springer
DOI: 10.1007/978-3-030-95987-6_3
Abstract
Channel estimation is crucial for a millimeter wave MIMO system. Due to the existence of massive antenna elements, the overhead to perform channel estimation with traditional methods would be huge, which will degrade the throughput severely. Thanks to the sparsity of channel model on millimeter wave band, most existing literature make use of this feature to compress the number of signaling based on the technique of compressive sensing. In this paper, by making use of the fact that the angle of arrival (AoA) and angle of departure (AoD) vary much slower than the channel coefficients, we go one step forward on saving the number of signaling for channel measurement. Specifically, with a consideration of channel sparsity feature, we design a set of methods to detect the variation of AoA and AoD in time, which includes the case of appearance of new path and disappearance of existing path, through sequential analysis approach. Moreover, to enhance the performance of our proposed method, procoder and combiner are designed respectively to generate beam on anticipated directions, through semi-definite programming method. With the above operations, we only need to measure channel coefficients when the AoA and AoD are not detected to change, which does not require much signaling. Through this way, the overhead for channel measurement is further saved compared with the methods based on compressive sensing.