Abstract
Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. CS enables the recovery of high-dimensional sparse signals from much fewer samples than usually required. Further, quite a few recent channel measurement experiments show that many wireless channels also tend to exhibit sparsity. In this case, CS theory can be applicable to sparse channel estimation and its effectiveness has been validated in point-to-point (P2P) communication. In this work, we study sparse channel estimation for two-way relay networks (TWRN). Unlike P2P systems, applying CS theory to sparse channel estimation in TWRN is much more challenging. One issue is that the equivalent channels (terminal-relay-terminal) may be no longer sparse due to the linear convolutional operation. On this basis, novel schemes are proposed to solve this problem and effectively improve the accuracy of TWRN channel estimation when using CS theory. Extensive numerical results are provided to corroborate the proposed studies.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
