358 research outputs found
Compressive sensing based differential channel feedback for massive MIMO
Massive multiple-input multiple-output (MIMO) is becoming a key technology
for future 5G wireless communications. Channel feedback for massive MIMO is
challenging due to the substantially increased dimension of MIMO channel
matrix. In this letter, we propose a compressive sensing (CS) based
differential channel feedback scheme to reduce the feedback overhead.
Specifically, the temporal correlation of time-varying channels is exploited to
generate the differential channel impulse response (CIR) between two CIRs in
neighboring time slots, which enjoys a much stronger sparsity than the original
sparse CIRs. Thus, the base station can recover the differential CIR from the
highly compressed differential CIR under the framework of CS theory.
Simulations show that the proposed scheme reduces the feedback overhead by
about 20\% compared with the direct CS-based scheme
Joint Channel Training and Feedback for FDD Massive MIMO Systems
Massive multiple-input multiple-output (MIMO) is widely recognized as a
promising technology for future 5G wireless communication systems. To achieve
the theoretical performance gains in massive MIMO systems, accurate channel
state information at the transmitter (CSIT) is crucial. Due to the overwhelming
pilot signaling and channel feedback overhead, however, conventional downlink
channel estimation and uplink channel feedback schemes might not be suitable
for frequency-division duplexing (FDD) massive MIMO systems. In addition, these
two topics are usually separately considered in the literature. In this paper,
we propose a joint channel training and feedback scheme for FDD massive MIMO
systems. Specifically, we firstly exploit the temporal correlation of
time-varying channels to propose a differential channel training and feedback
scheme, which simultaneously reduces the overhead for downlink training and
uplink feedback. We next propose a structured compressive sampling matching
pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the
structured sparsity of wireless MIMO channels. Simulation results demonstrate
that the proposed scheme can achieve substantial reduction in the training and
feedback overhead
An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
Communication at millimeter wave (mmWave) frequencies is defining a new era
of wireless communication. The mmWave band offers higher bandwidth
communication channels versus those presently used in commercial wireless
systems. The applications of mmWave are immense: wireless local and personal
area networks in the unlicensed band, 5G cellular systems, not to mention
vehicular area networks, ad hoc networks, and wearables. Signal processing is
critical for enabling the next generation of mmWave communication. Due to the
use of large antenna arrays at the transmitter and receiver, combined with
radio frequency and mixed signal power constraints, new multiple-input
multiple-output (MIMO) communication signal processing techniques are needed.
Because of the wide bandwidths, low complexity transceiver algorithms become
important. There are opportunities to exploit techniques like compressed
sensing for channel estimation and beamforming. This article provides an
overview of signal processing challenges in mmWave wireless systems, with an
emphasis on those faced by using MIMO communication at higher carrier
frequencies.Comment: Submitted to IEEE Journal of Selected Topics in Signal Processin
Limited Feedback Massive MISO Systems with Trellis Coded Quantization for Correlated Channels
In this paper, we propose trellis coded quantization (TCQ) based limited
feedback techniques for massive multiple-input single-output (MISO) frequency
division duplexing (FDD) systems in temporally and spatially correlated
channels. We exploit the correlation present in the channel to effectively
quantize channel direction information (CDI). For multiuser (MU) systems with
matched-filter (MF) precoding, we show that the number of feedback bits
required by the random vector quantization (RVQ) codebook to match even a small
fraction of the perfect CDI signal-to-interference-plus-noise ratio (SINR)
performance is large. With such large numbers of bits, the exhaustive search
required by conventional codebook approaches make them infeasible for massive
MISO systems. Motivated by this, we propose a differential TCQ scheme for
temporally correlated channels that transforms the source constellation at each
stage in a trellis using 2D translation and scaling techniques. We derive a
scaling parameter for the source constellation as a function of the temporal
correlation and the number of BS antennas. We also propose a TCQ based limited
feedback scheme for spatially correlated channels where the channel is
quantized directly without performing decorrelation at the receiver. Simulation
results show that the proposed TCQ schemes outperform the existing noncoherent
TCQ (NTCQ) schemes, by improving the spectral efficiency and beamforming gain
of the system. The proposed differential TCQ also reduces the feedback overhead
of the system compared to the differential NTCQ method.Comment: 13 pages, 18 figures, IEEE Transactions on Vehicular Technology,
accepted for publicatio
Hardware-In-the-Loop Measurements of the Multi-Carrier Compressed Sensing Multi-User Detection (MCSM) System
MCSM is a recently proposed novel system concept to solve the massive access
problem envisioned in future communication systems like 5G and industry 4.0
systems. This work focuses on the practical verification of the theoretical
gains that MCSM provides using a Hardware-In-the-Loop (HIL) measurement setup.
We present results in two different scenarios: (i) a LoS lab setup and (ii) a
non-LoS machine hall. In both scenarios MCSM shows promising performance in
terms of the number of supported users and the achieved reliability
A Block Sparsity Based Estimator for mmWave Massive MIMO Channels with Beam Squint
Multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication
is a key technology for next generation wireless networks. One of the
consequences of utilizing a large number of antennas with an increased
bandwidth is that array steering vectors vary among different subcarriers. Due
to this effect, known as beam squint, the conventional channel model is no
longer applicable for mmWave massive MIMO systems. In this paper, we study
channel estimation under the resulting non-standard model. To that aim, we
first analyze the beam squint effect from an array signal processing
perspective, resulting in a model which sheds light on the angle-delay sparsity
of mmWave transmission. We next design a compressive sensing based channel
estimation algorithm which utilizes the shift-invariant block-sparsity of this
channel model. The proposed algorithm jointly computes the off-grid angles, the
off-grid delays, and the complex gains of the multi-path channel. We show that
the newly proposed scheme reflects the mmWave channel more accurately and
results in improved performance compared to traditional approaches. We then
demonstrate how this approach can be applied to recover both the uplink as well
as the downlink channel in frequency division duplex (FDD) systems, by
exploiting the angle-delay reciprocity of mmWave channels
On Channel State Feedback Model and Overhead in Theoretical and Practical Views
Channel state feedback plays an important role to the improvement of link
performance in current wireless communication systems, and even more in the
next generation. The feedback information, however, consumes the uplink
bandwidth and thus generates overhead. In this paper, we investigate the impact
of channel state feedback and propose an improved scheme to reduce the overhead
in practical communication systems. Compared with existing schemes, we
introduce a more accurate channel model to describe practical wireless channels
and obtain the theoretical lower bounds of overhead for the periodical and
aperiodical feedback schemes. The obtained theoretical results provide us the
guidance to optimise the design of feedback systems, such as the number of bits
used for quantizing channel states. We thus propose a practical feedback scheme
that achieves low overhead and improved performance over currently widely used
schemes such as zero holding. Simulation experiments confirm its advantages and
suggest its potentially wide applications in the next generation of wireless
systems.Comment: 13 pages, 14 figures, IEEE Transactions on Wireless Communication
Trellis-Extended Codebooks and Successive Phase Adjustment: A Path from LTE-Advanced to FDD Massive MIMO Systems
It is of great interest to develop efficient ways to acquire accurate channel
state information (CSI) for frequency division duplexing (FDD) massive
multiple-input multiple-output (MIMO) systems for backward compatibility. It is
theoretically well known that the codebook size for CSI quantization should be
increased as the number of transmit antennas becomes larger, and 3GPP long term
evolution (LTE) and LTE-Advanced codebooks also follow this trend. Thus, in
massive MIMO, it is hard to apply the conventional approach of using
pre-defined vector-quantized codebooks for CSI quantization mainly because of
codeword search complexity. In this paper, we propose a trellis-extended
codebook (TEC) that can be easily harmonized with current wireless standards
such as LTE or LTE-Advanced by extending standardized codebooks designed for 2,
4, or 8 antennas with trellis structures. TEC exploits a Viterbi decoder and
convolutional encoder in channel coding as the CSI quantizer and the CSI
reconstructer, respectively. By quantizing multiple channel entries
simultaneously using standardized codebooks in a state transition of trellis
search, TEC can achieve fractional bits per channel entry quantization to have
a practical feedback overhead. Thus, TEC can solve both the complexity and the
feedback overhead issues of CSI quantization in massive MIMO systems. We also
develop trellis-extended successive phase adjustment (TE-SPA) which works as a
differential codebook of TEC. This is similar to the dual codebook concept of
LTE-Advanced. TE-SPA can reduce CSI quantization error even with lower feedback
overhead in temporally correlated channels. Numerical results verify the
effectiveness of the proposed schemes in FDD massive MIMO systems.Comment: 10 pages, 11 figures, accepted to IEEE Transactions on Wireless
Communications, Nov. 201
Superimposed Coding Based CSI Feedback Using 1-Bit Compressed Sensing
In a frequency division duplex (FDD) massive multiple input multiple output
(MIMO) system, the channel state information (CSI) feedback causes a
significant bandwidth resource occupation. In order to save the uplink
bandwidth resources, a 1-bit compressed sensing (CS)-based CSI feedback method
assisted by superimposed coding (SC) is proposed. Using 1-bit CS and SC
techniques, the compressed support-set information and downlink CSI (DL-CSI)
are superimposed on the uplink user data sequence (UL-US) and fed back to base
station (BS). Compared with the SC-based feedback, the analysis and simulation
results show that the UL-US's bit error ratio (BER) and the DL-CSI's accuracy
can be improved in the proposed method, without using the exclusive uplink
bandwidth resources to feed DL-CSI back to BS.Comment: 5 pages, 4 figure
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
As a promising paradigm to reduce both capital and operating expenditures,
the cloud radio access network (C-RAN) has been shown to provide high spectral
efficiency and energy efficiency. Motivated by its significant theoretical
performance gains and potential advantages, C-RANs have been advocated by both
the industry and research community. This paper comprehensively surveys the
recent advances of C-RANs, including system architectures, key techniques, and
open issues. The system architectures with different functional splits and the
corresponding characteristics are comprehensively summarized and discussed. The
state-of-the-art key techniques in C-RANs are classified as: the fronthaul
compression, large-scale collaborative processing, and channel estimation in
the physical layer; and the radio resource allocation and optimization in the
upper layer. Additionally, given the extensiveness of the research area, open
issues and challenges are presented to spur future investigations, in which the
involvement of edge cache, big data mining, social-aware device-to-device,
cognitive radio, software defined network, and physical layer security for
C-RANs are discussed, and the progress of testbed development and trial test
are introduced as well.Comment: 27 pages, 11 figure
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