457 research outputs found
Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information
Millimeter wave (mmWave) communication is one feasible solution for high
data-rate applications like vehicular-to-everything communication and next
generation cellular communication. Configuring mmWave links, which can be done
through channel estimation or beam-selection, however, is a source of
significant overhead. In this paper, we propose to use spatial information
extracted at sub-6 GHz to help establish the mmWave link. First, we review the
prior work on frequency dependent channel behavior and outline a simulation
strategy to generate multi-band frequency dependent channels. Second, assuming:
(i) narrowband channels and a fully digital architecture at sub-6 GHz; and (ii)
wideband frequency selective channels, OFDM signaling, and an analog
architecture at mmWave, we outline strategies to incorporate sub-6 GHz spatial
information in mmWave compressed beam selection. We formulate compressed
beam-selection as a weighted sparse signal recovery problem, and obtain the
weighting information from sub-6 GHz channels. In addition, we outline a
structured precoder/combiner design to tailor the training to out-of-band
information. We also extend the proposed out-of-band aided compressed
beam-selection approach to leverage information from all active OFDM
subcarriers. The simulation results for achievable rate show that out-of-band
aided beam-selection can reduce the training overhead of in-band only
beam-selection by 4x.Comment: 30 pages, 11 figure
Millimeter Wave communication with out-of-band information
Configuring the antenna arrays is the main source of overhead in millimeter
wave (mmWave) communication systems. In high mobility scenarios, the problem is
exacerbated, as achieving the highest rates requires frequent link
reconfiguration. One solution is to exploit spatial congruence between signals
at different frequency bands and extract mmWave channel parameters from side
information obtained in another band. In this paper we propose the concept of
out-of-band information aided mmWave communication. We analyze different
strategies to leverage information derived from sensors or from other
communication systems operating at sub-6 GHz bands to help configure the mmWave
communication link. The overhead reductions that can be obtained when
exploiting out-of-band information are characterized in a preliminary study.
Finally, the challenges associated with using out-of-band signals as a source
of side information at mmWave are analyzed in detail.Comment: 14 pages, 6 figure
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
Directional Modulation: A Secure Solution to 5G and Beyond Mobile Networks
Directional modulation (DM), as an efficient secure transmission way, offers
security through its directive property and is suitable for line-of-propagation
(LoP) channels such as millimeter wave (mmWave) massive multiple-input
multiple-output (MIMO), satellite communication, unmanned aerial vehicle (UAV),
and smart transportation. If the direction angle of the desired received is
known, the desired channel gain vector is obtainable. Thus, in advance, the DM
transmitter knows the values of directional angles of desired user and
eavesdropper, or their direction of arrival (DOAs) because the beamforming
vector of confidential messages and artificial noise (AN) projection matrix is
mainly determined by directional angles of desired user and eavesdropper. For a
DM transceiver, working as a receiver, the first step is to measure the DOAs of
desired user and eavesdropper. Then, in the second step, using the measured
DOAs, the beamforming vector of confidential messages and AN projection matrix
is designed. In this paper, we describe the DOA measurement methods, power
allocation, and beamforming in DM networks. A machine learning-based DOA
measurement method is proposed to make a substantial SR performance gain
compared to single-snapshot measurement without machine learning for a given
null-space projection beamforming scheme. However, for a conventional DM
network, there still exists a serious secure issue: the eavesdropper moves
inside the main beam of the desired user and may intercept the confidential
messages intended to the desired users because the beamforming vector of
confidential messages and AN projection matrix are only angle-dependence. To
address this problem, we present a new concept of secure and precise
transmission, where the transmit waveform has two-dimensional even
three-dimensional dependence by using DM, random frequency selection, and phase
alignment at DM transmitter
Machine-Learning-based High-resolution DOA Measurement and Robust DM for Hybrid Analog-Digital Massive MIMO Transceiver
At hybrid analog-digital (HAD) transceiver, an improved HAD rotational
invariance techniques (ESPRIT), called I-HAD-ESPRIT, is proposed to measure the
direction of arrival (DOA) of desired user, where the phase ambiguity due to
HAD structure is addressed successfully. Subsequently, a machine-learning (ML)
framework is proposed to improve the precision of measuring DOA. In the
training stage, the HAD transceiver works as a receiver and repeatedly measures
the values of DOA via I-HAD-ESPRIT to form a slightly large training data set
(TDS) of DOA. From TDS, we find that the probability density function (PDF) of
DOA measurement error (DOAME) is approximated as a Gaussian distribution by the
histogram method in ML. This TDS is used to learn the mean of DOA and the
variance of DOAME, which are utilized to infer their values and improve their
precisions in the real-time stage. The HAD transceiver rapidly measures the
real-time value of DOA some times to form a relatively small real-time set
(RTS), which is used to learn the real-time mean and variance of DOA/ DOAME.
Then, three weight combiners are proposed to combine
the-maximum-likelihood-learning outputs of TDS and RTS. Their weight factors
depend intimately on the numbers of elements in TDS and RTS, and
signal-to-noise ratios during the two stages. Using the mean and variance of
DOA/DOAME, their PDFs can be given directly, and we propose a robust beamformer
for directional modulation (DM) transmitter with HAD by fully exploiting the
PDF of DOA/DOAME, especially a robust analog beamformer on RF chain. Simulation
results show that: 1) The proposed I-HAD-ESPRIT can achieve the HAD CRLB; 2)
The proposed ML framework performs much better than the corresponding real-time
one without training stage, 3) The proposed robust DM transmitter can perform
better than the corresponding non-robust ones in terms of bit error rate and
secrecy rate.Comment: 14 pages, 11 figure
Time Varying Channel Tracking with Spatial and Temporal BEM for Massive MIMO Systems
In this paper, we propose a channel tracking method for massive multi-input
and multi-output systems under both time-varying and spatial-varying
circumstance. Exploiting the characteristics of massive antenna array, a
spatial-temporal basis expansion model is designed to reduce the effective
dimensions of up-link and down-link channel, which decomposes channel state
information into the time-varying spatial information and gain information. We
firstly model the users movements as a one-order unknown Markov process, which
is blindly learned by the expectation and maximization (EM) approach. Then, the
up-link time varying spatial information can be blindly tracked by Taylor
series expansion of the steering vector, while the rest up-link channel gain
information can be trained by only a few pilot symbols. Due to angle
reciprocity (spatial reciprocity), the spatial information of the down-link
channel can be immediately obtained from the up-link counterpart, which greatly
reduces the complexity of down-link channel tracking. Various numerical results
are provided to demonstrate the effectiveness of the proposed method
Beam Tracking for UAV Mounted SatCom on-the-Move with Massive Antenna Array
Unmanned aerial vehicle (UAV)-satellite communication has drawn dramatic
attention for its potential to build the integrated space-air-ground network
and the seamless wide-area coverage. The key challenge to UAV-satellite
communication is its unstable beam pointing due to the UAV navigation, which is
a typical SatCom on-the-move scenario. In this paper, we propose a blind beam
tracking approach for Ka-band UAVsatellite communication system, where UAV is
equipped with a large-scale antenna array. The effects of UAV navigation are
firstly released through the mechanical adjustment, which could approximately
point the beam towards the target satellite through beam stabilization and
dynamic isolation. Specially, the attitude information can be realtimely
derived from data fusion of lowcost sensors. Then, the precision of the beam
pointing is blindly refined through electrically adjusting the weight of the
massive antennas, where an array structure based simultaneous perturbation
algorithm is designed. Simulation results are provided to demonstrate the
superiority of the proposed method over the existing ones
Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions
The new demands for high-reliability and ultra-high capacity wireless
communication have led to extensive research into 5G communications. However,
the current communication systems, which were designed on the basis of
conventional communication theories, signficantly restrict further performance
improvements and lead to severe limitations. Recently, the emerging deep
learning techniques have been recognized as a promising tool for handling the
complicated communication systems, and their potential for optimizing wireless
communications has been demonstrated. In this article, we first review the
development of deep learning solutions for 5G communication, and then propose
efficient schemes for deep learning-based 5G scenarios. Specifically, the key
ideas for several important deep learningbased communication methods are
presented along with the research opportunities and challenges. In particular,
novel communication frameworks of non-orthogonal multiple access (NOMA),
massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are
investigated, and their superior performances are demonstrated. We vision that
the appealing deep learning-based wireless physical layer frameworks will bring
a new direction in communication theories and that this work will move us
forward along this road.Comment: Submitted a possible publication to IEEE Wireless Communications
Magazin
Spatial- and Frequency-Wideband Effects in Millimeter-Wave Massive MIMO Systems
When there are a large number of antennas in massive MIMO systems, the
transmitted wideband signal will be sensitive to the physical propagation delay
of electromagnetic waves across the large array aperture, which is called the
spatial-wideband effect. In this scenario, transceiver design is different from
most of the existing works, which presume that the bandwidth of the transmitted
signals is not that wide, ignore the spatial-wideband effect, and only address
the frequency selectivity. In this paper, we investigate spatial- and
frequency-wideband effects, called dual-wideband effects, in massive MIMO
systems from array signal processing point of view. Taking mmWave-band
communications as an example, we describe the transmission process to address
the dual-wideband effects. By exploiting the channel sparsity in the angle
domain and the delay domain, we develop the efficient uplink and downlink
channel estimation strategies that require much less amount of training
overhead and cause no pilot contamination. Thanks to the array signal
processing techniques, the proposed channel estimation is suitable for both TDD
and FDD massive MIMO systems. Numerical examples demonstrate that the proposed
transmission design for massive MIMO systems can effectively deal with the
dual-wideband effects.Comment: 13 pages, 10 figures. Index terms: Massive MIMO, mmWave, array signal
processing, wideband, spatial-wideband, beam squint, angle reciprocity, delay
reciprocity. Submitted to IEEE Transactions on Signal Processin
Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last Five Years
Timing and carrier synchronization is a fundamental requirement for any
wireless communication system to work properly. Timing synchronization is the
process by which a receiver node determines the correct instants of time at
which to sample the incoming signal. Carrier synchronization is the process by
which a receiver adapts the frequency and phase of its local carrier oscillator
with those of the received signal. In this paper, we survey the literature over
the last five years (2010-2014) and present a comprehensive literature review
and classification of the recent research progress in achieving timing and
carrier synchronization in single-input-single-output (SISO),
multiple-input-multiple-output (MIMO), cooperative relaying, and
multiuser/multicell interference networks. Considering both single-carrier and
multi-carrier communication systems, we survey and categorise the timing and
carrier synchronization techniques proposed for the different communication
systems focusing on the system model assumptions for synchronization, the
synchronization challenges, and the state-of-the-art synchronization solutions
and their limitations. Finally, we envision some future research directions.Comment: submitted for journal publicatio
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