2020/10/22 by Kürşat Tekbıyık, Tekbıyık, Kürşat, Güneş Karabulut Kurt +7 · 1 citation
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Technologies #Cooperative Communication and Network Coding #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2010.12004
openalex publication_date 2020/10/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, graph attention network (GAT) is firstly utilized for the\nchannel estimation. In accordance with the 6G expectations, we consider a\nhigh-altitude platform station (HAPS) mounted reconfigurable intelligent\nsurface-assisted two-way communications and obtain a low overhead and a high\nnormalized mean square error performance. The performance of the proposed\nmethod is investigated on the two-way backhauling link over the RIS-integrated\nHAPS. The simulation results denote that the GAT estimator overperforms the\nleast square in full-duplex channel estimation. Contrary to the previously\nintroduced methods, GAT at one of the nodes can separately estimate the\ncascaded channel coefficients. Thus, there is no need to use time-division\nduplex mode during pilot signaling in full-duplex communication. Moreover, it\nis shown that the GAT estimator is robust to hardware imperfections and changes\nin small-scale fading characteristics even if the training data do not include\nall these variations.\n