TY - GEN
T1 - Channel Estimation for Millimeter Wave MIMO System
T2 - 7th EAI International Conference on the Internet of Things as a Service, IoTaaS 2021
AU - Zhang, Jinduo
AU - Fan, Rongfei
AU - Liu, Peng
N1 - Publisher Copyright:
© 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Channel measurement
KW - Millimeter wave
KW - Multiple input multiple output (MIMO) system
KW - Semi-definite programming
KW - Sequential analysis
UR - http://www.scopus.com/inward/record.url?scp=85135016668&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-95987-6_3
DO - 10.1007/978-3-030-95987-6_3
M3 - Conference contribution
AN - SCOPUS:85135016668
SN - 9783030959869
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 39
EP - 53
BT - IoT as a Service - 7th EAI International Conference, IoTaaS 2021, Proceedings
A2 - Hussain, Walayat
A2 - Jan, Mian Ahmad
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 13 December 2021 through 14 December 2021
ER -