247 research outputs found
DL-based CSI Feedback and Cooperative Recovery in Massive MIMO
In this paper, we exploit the correlation between nearby user equipment (UE)
and develop a deep learning-based channel state information (CSI) feedback and
cooperative recovery framework, CoCsiNet, to reduce the feedback overhead. The
CSI information can be divided into two parts: shared by nearby UE and owned by
individual UE. The key idea of exploiting the correlation is to reduce the
overhead used to repeatedly feedback shared information. Unlike in the general
autoencoder framework, an extra decoder and a combination network are added at
the base station to recover the shared information from the feedback CSI of two
nearby UE and combine the shared and individual information, respectively, but
no modification is performed at the UEs. For a UE with multiple antennas, we
also introduce a baseline neural network architecture with long short-term
memory modules to extract the correlation of nearby antennas. Given that the
CSI phase is not sparse, we propose two magnitude-dependent phase feedback
strategies that introduce statistical and instant CSI magnitude information to
the phase feedback process, respectively. Simulation results on two different
channel datasets show the effectiveness of the proposed CoCsiNet.Comment: This work has been submitted to the IEEE for possible publication.
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Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of
resources to facilitate the design and implementation, from user access to data
transmission. Emerging applications and scenarios in the sixth generation (6G)
wireless systems will require massive connectivity and transmission of a deluge
of data, which calls for more flexibility in the design concept that goes
beyond orthogonality. Furthermore, recent advances in signal processing and
learning have attracted considerable attention, as they provide promising
approaches to various complex and previously intractable problems of signal
processing in many fields. This article provides an overview of research
efforts to date in the field of signal processing and learning for
next-generation multiple access, with an emphasis on massive random access and
non-orthogonal multiple access. The promising interplay with new technologies
and the challenges in learning-based NGMA are discussed
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