7 research outputs found
Deep Learning-based Limited Feedback Designs for MIMO Systems
We study a deep learning (DL) based limited feedback methods for
multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an
end-to-end limited feedback procedure including pilot-aided channel training
process, channel codebook design, and beamforming vector selection. The DNNs
are trained to yield binary feedback information as well as an efficient
beamforming vector which maximizes the effective channel gain. Compared to
conventional limited feedback schemes, the proposed DL method shows an 1 dB
symbol error rate (SER) gain with reduced computational complexity.Comment: to appear in IEEE Wireless Commun. Let
Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance
We investigate the performance of multi-user multiple-antenna downlink
systems in which a BS serves multiple users via a shared wireless medium. In
order to fully exploit the spatial diversity while minimizing the passive
energy consumed by radio frequency (RF) components, the BS is equipped with M
RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain
the channel state information, the BS determines the best subset of M antennas
for serving the users. We propose a joint antenna selection and precoding
design (JASPD) algorithm to maximize the system sum rate subject to a transmit
power constraint and QoS requirements. The JASPD overcomes the non-convexity of
the formulated problem via a doubly iterative algorithm, in which an inner loop
successively optimizes the precoding vectors, followed by an outer loop that
tries all valid antenna subsets. Although approaching the (near) global
optimality, the JASPD suffers from a combinatorial complexity, which may limit
its application in real-time network operations. To overcome this limitation,
we propose a learning-based antenna selection and precoding design algorithm
(L-ASPA), which employs a DNN to establish underlaying relations between the
key system parameters and the selected antennas. The proposed L-ASPD is robust
against the number of users and their locations, BS's transmit power, as well
as the small-scale channel fading. With a well-trained learning model, it is
shown that the L-ASPD significantly outperforms baseline schemes based on the
block diagonalization and a learning-assisted solution for broadcasting systems
and achieves higher effective sum rate than that of the JASPA under limited
processing time. In addition, we observed that the proposed L-ASPD can reduce
the computation complexity by 95% while retaining more than 95% of the optimal
performance.Comment: accepted to the IEEE Transactions on Wireless Communication
Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback
We propose a deep learning-based channel estimation, quantization, feedback,
and precoding method for downlink multiuser multiple-input and multiple-output
systems. In the proposed system, channel estimation and quantization for
limited feedback are handled by a receiver deep neural network (DNN). Precoder
selection is handled by a transmitter DNN. To emulate the traditional channel
quantization, a binarization layer is adopted at each receiver DNN, and the
binarization layer is also used to enable end-to-end learning. However, this
can lead to inaccurate gradients, which can trap the receiver DNNs at a poor
local minimum during training. To address this, we consider knowledge
distillation, in which the existing DNNs are jointly trained with an auxiliary
transmitter DNN. The use of an auxiliary DNN as a teacher network allows the
receiver DNNs to additionally exploit lossless gradients, which is useful in
avoiding a poor local minimum. For the same number of feedback bits, our
DNN-based precoding scheme can achieve a higher downlink rate compared to
conventional linear precoding with codebook-based limited feedback.Comment: 6 pages, 4 figures, submitted to IEEE Transactions on Vehicular
Technolog
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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