Abstract
In a frequency division duplex (FDD) massive MIMO system, downlink channel estimation poses several challenges with limited training duration being one impediment. Our previous work developed an algorithm to learn a dictionary in which the channel can be sparsely represented, and then leveraged compressed sensing framework to estimate the downlink channel. In this work, we extend the sparse channel representation framework to joint uplink and downlink channel modeling exploiting the similar scattering environment experienced by the signal during uplink and downlink transmission. We develop a joint dictionary learning algorithm in which joint sparse pattern of the uplink and downlink channels is enforced. This structure is utilized to improve downlink channel estimation from uplink training, which usually has much less training overhead in massive MIMO systems. Experimental results show that the proposed joint channel estimation improves the mean squared error (MSE) compared to downlink estimation only.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.