BROWSE
Detail
Journal Article
An Efficient Calibration of MIMO Channel Sounders With Internal Crosstalk
Cited 1 time in 
Share 
Share
- Authors
- Junseok Kim, Eun Ae Lee, Chung-Sup Kim, Young-Jun Chong, Joon Ho Cho
- Issue Date
- 2020-09
- Citation
- IEEE Transactions on Vehicular Technology, v.69, no.9, pp.9445-9458
- ISSN
- 0018-9545
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- In this paper, a calibration of multiple-input multiple-output (MIMO) channel sounders with internal crosstalk is considered.The objective is to minimize the number of back-to-back (B2B) connections required to estimate the transmitter (Tx) and receiver (Rx) response matrices that convey the information about linear distortion and internal crosstalk. A signal and system model is developed for the B2B measurements, where only some pairs of the Tx and Rx ports of the sounder are utilized among all pairs of the ports. Using the measurement model, a least-square estimation problem is then formulated and converted in the frequency domain to weighted rank-one approximation problems. The notion of system identifiability of a MIMO channel sounder is introduced and some optimal sets of B2B connections are proposed. Given a proposed optimal set of B2B connections, the alternate convex search (ACS) algorithm with a proper initialization is also proposed to solve the weighted rank-one approximation problems. Finally, it is shown how to calibrate field measurement data by using the estimated response matrices. Numerical results show that, only after a couple of iterations, the ACS algorithm with the proposed initialization achieves a comparable identification and calibration performance to the conventional method that requires the B2B connections of all port pairs.
- KSP Keywords
- Back to Back(BTB), Channel Sounder, Conventional methods, Field measurement data, Frequency domain(FD), Least Square(LS), Linear distortion, Numerical results, measurement model, multiple-input multiple-output (MIMO) channel, rank-one approximation