152 research outputs found
Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems
To fully utilize the spatial multiplexing gains or array gains of massive
MIMO, the channel state information must be obtained at the transmitter side
(CSIT). However, conventional CSIT estimation approaches are not suitable for
FDD massive MIMO systems because of the overwhelming training and feedback
overhead. In this paper, we consider multi-user massive MIMO systems and deploy
the compressive sensing (CS) technique to reduce the training as well as the
feedback overhead in the CSIT estimation. The multi-user massive MIMO systems
exhibits a hidden joint sparsity structure in the user channel matrices due to
the shared local scatterers in the physical propagation environment. As such,
instead of naively applying the conventional CS to the CSIT estimation, we
propose a distributed compressive CSIT estimation scheme so that the compressed
measurements are observed at the users locally, while the CSIT recovery is
performed at the base station jointly. A joint orthogonal matching pursuit
recovery algorithm is proposed to perform the CSIT recovery, with the
capability of exploiting the hidden joint sparsity in the user channel
matrices. We analyze the obtained CSIT quality in terms of the normalized mean
absolute error, and through the closed-form expressions, we obtain simple
insights into how the joint channel sparsity can be exploited to improve the
CSIT recovery performance.Comment: 16 double-column pages, accepted for publication in IEEE Transactions
on Signal Processin
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Sensing-Assisted Sparse Channel Recovery for Massive Antenna Systems
This correspondence presents a novel sensing-assisted sparse channel recovery
approach for massive antenna wireless communication systems. We focus on a
fundamental configuration with one massive-antenna base station (BS) and one
single-antenna communication user (CU). The wireless channel exhibits sparsity
and consists of multiple paths associated with scatterers detectable via radar
sensing. Under this setup, the BS first sends downlink pilots to the CU and
concurrently receives the echo pilot signals for sensing the surrounding
scatterers. Subsequently, the CU sends feedback information on its received
pilot signal to the BS. Accordingly, the BS determines the sparse basis based
on the sensed scatterers and proceeds to recover the wireless channel,
exploiting the feedback information based on advanced compressive sensing (CS)
algorithms. Numerical results show that the proposed sensing-assisted approach
significantly increases the overall achievable rate than the conventional
design relying on a discrete Fourier transform (DFT)-based sparse basis without
sensing, thanks to the reduced training overhead and enhanced recovery accuracy
with limited feedback.Comment: 5 pages, 4 fig
A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback
Channel state information (CSI) plays a critical role in achieving the
potential benefits of massive multiple input multiple output (MIMO) systems. In
frequency division duplex (FDD) massive MIMO systems, the base station (BS)
relies on sustained and accurate CSI feedback from the users. However, due to
the large number of antennas and users being served in massive MIMO systems,
feedback overhead can become a bottleneck. In this paper, we propose a
model-driven deep learning method for CSI feedback, called learnable
optimization and regularization algorithm (LORA). Instead of using l1-norm as
the regularization term, a learnable regularization module is introduced in
LORA to automatically adapt to the characteristics of CSI. We unfold the
conventional iterative shrinkage-thresholding algorithm (ISTA) to a neural
network and learn both the optimization process and regularization term by
end-toend training. We show that LORA improves the CSI feedback accuracy and
speed. Besides, a novel learnable quantization method and the corresponding
training scheme are proposed, and it is shown that LORA can operate
successfully at different bit rates, providing flexibility in terms of the CSI
feedback overhead. Various realistic scenarios are considered to demonstrate
the effectiveness and robustness of LORA through numerical simulations
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
PRVNet: Variational Autoencoders for Massive MIMO CSI Feedback
In a frequency division duplexing multiple-input multiple-output (FDD-MIMO)
system, the user equipment (UE) send the downlink channel state information
(CSI) to the base station for performance improvement. However, with the
growing complexity of MIMO systems, this feedback becomes expensive and has a
negative impact on the bandwidth. Although this problem has been largely
studied in the literature, the noisy nature of the feedback channel is less
considered. In this paper, we introduce PRVNet, a neural architecture based on
variational autoencoders (VAE). VAE gained large attention in many fields
(e.g., image processing, language models, or recommendation system). However,
it received less attention in the communication domain generally and in CSI
feedback problem specifically. We also introduce a different regularization
parameter for the learning objective, which proved to be crucial for achieving
competitive performance. In addition, we provide an efficient way to tune this
parameter using KL-annealing. Empirically, we show that the proposed model
significantly outperforms state-of-the-art, including two neural network
approaches. The proposed model is also proved to be more robust against
different levels of noise
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