575 research outputs found
Pilot Optimization and Power Allocation for OFDM-based Full-duplex Relay Networks with IQ-imbalances
In OFDM relay networks with IQ imbalances and full-duplex relay station (RS),
how to optimize pilot pattern and power allocation using the criterion of
minimizing the sum of mean square errors (Sum-MSE) for the frequency-domain
least-squares channel estimator has a heavy impact on self-interference
cancellation. Firstly, the design problem of pilot pattern is casted as a
convex optimization. From the KKT conditions, the optimal analytical expression
is derived given the fixed source power and RS power. Subsequently, an optimal
power allocation (OPA) strategy is proposed and presented to further alleviate
the effect of Sum-MSE under the total transmit power sum constraint of source
node and RS. Simulation results show that the proposed OPA performs better than
equal power allocation (EPA) in terms of Sum-MSE, and the Sum-MSE performance
gain grows with deviating from the value of minimizing the
Sum-MSE, where is defined as the average ratio of the residual SI
channel at RS to the intended channel from source to RS. For example, the OPA
achieves about 5dB SNR gain over EPA by shrinking or stretching with a
factor . More importantly, as decreases or increases more, the
performance gain becomes more significant.Comment: 7 pages, 7 figure
Complex support vector machines regression for robust channel estimation in LTE downlink system
In this paper, the problem of channel estimation for LTE Downlink system in
the environment of high mobility presenting non-Gaussian impulse noise
interfering with reference signals is faced. The estimation of the frequency
selective time varying multipath fading channel is performed by using a channel
estimator based on a nonlinear complex Support Vector Machine Regression (SVR)
which is applied to Long Term Evolution (LTE) downlink. The estimation
algorithm makes use of the pilot signals to estimate the total frequency
response of the highly selective fading multipath channel. Thus, the algorithm
maps trained data into a high dimensional feature space and uses the structural
risk minimization principle to carry out the regression estimation for the
frequency response function of the fading channel. The obtained results show
the effectiveness of the proposed method which has better performance than the
conventional Least Squares (LS) and Decision Feedback methods to track the
variations of the fading multipath channel.Comment: 13 pages Vol.4, IJCNC (2012) No.1, January 2012. arXiv admin note:
substantial text overlap with arXiv:1109.089
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
Compressive Massive Random Access for Massive Machine-Type Communications (mMTC)
In future wireless networks, one fundamental challenge for massive
machine-type communications (mMTC) lies in the reliable support of massive
connectivity with low latency. Against this background, this paper proposes a
compressive sensing (CS)-based massive random access scheme for mMTC by
leveraging the inherent sporadic traffic, where both the active devices and
their channels can be jointly estimated with low overhead. Specifically, we
consider devices in the uplink massive random access adopt pseudo random
pilots, which are designed under the framework of CS theory. Meanwhile, the
massive random access at the base stations (BS) can be formulated as the sparse
signal recovery problem by leveraging the sparse nature of active devices.
Moreover, by exploiting the structured sparsity among different receiver
antennas and subcarriers, we develop a distributed multiple measurement vector
approximate message passing (DMMV-AMP) algorithm for further improved
performance. Additionally, the state evolution (SE) of the proposed DMMV-AMP
algorithm is derived to predict the performance. Simulation results demonstrate
the superiority of the proposed scheme, which exhibits a good tightness with
the theoretical SE.Comment: This paper has been accepted by 2018 IEEE GlobalSI
Efficient Beam Alignment for mmWave Single-Carrier Systems with Hybrid MIMO Transceivers
Communication at millimeter wave (mmWave) bands is expected to become a key
ingredient of next generation (5G) wireless networks. Effective mmWave
communications require fast and reliable methods for beamforming at both the
User Equipment (UE) and the Base Station (BS) sides, in order to achieve a
sufficiently large Signal-to-Noise Ratio (SNR) after beamforming. We refer to
the problem of finding a pair of strongly coupled narrow beams at the
transmitter and receiver as the Beam Alignment (BA) problem. In this paper, we
propose an efficient BA scheme for single-carrier mmWave communications. In the
proposed scheme, the BS periodically probes the channel in the downlink via a
pre-specified pseudo-random beamforming codebook and pseudo-random spreading
codes, letting each UE estimate the Angle-of-Arrival / Angle-of-Departure
(AoA-AoD) pair of the multipath channel for which the energy transfer is
maximum. We leverage the sparse nature of mmWave channels in the AoA-AoD domain
to formulate the BA problem as the estimation of a sparse non-negative vector.
Based on the recently developed Non-Negative Least Squares (NNLS) technique, we
efficiently find the strongest AoA-AoD pair connecting each UE to the BS. We
evaluate the performance of the proposed scheme under a realistic channel
model, where the propagation channel consists of a few multipath scattering
components each having different delays, AoAs-AoDs, and Doppler shifts.The
channel model parameters are consistent with experimental channel measurements.
Simulation results indicate that the proposed method is highly robust to fast
channel variations caused by the large Doppler spread between the multipath
components. Furthermore, we also show that after achieving BA the beamformed
channel is essentially frequency-flat, such that single-carrier communication
needs no equalization in the time domain
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
Compressed Channel Estimation with Position-Based ICI Elimination for High-Mobility SIMO-OFDM Systems
Orthogonal frequency-division multiplexing (OFDM) is widely adopted for
providing reliable and high data rate communication in high-speed train
systems. However, with the increasing train mobility, the resulting large
Doppler shift introduces intercarrier interference (ICI) in OFDM systems and
greatly degrades the channel estimation accuracy. Therefore, it is necessary
and important to investigate reliable channel estimation and ICI mitigation
methods in high-mobility environments. In this paper, we consider a typical HST
communication system and show that the ICI caused by the large Doppler shift
can be mitigated by exploiting the train position information as well as the
sparsity of the conventional basis expansion model (BEM) based channel model.
Then, we show that for the complex-exponential BEM (CE-BEM) based channel
model, the ICI can be completely eliminated to get the ICI-free pilots at each
receive antenna. After that, we propose a new pilot pattern design algorithm to
reduce the system coherence and hence can improve the compressed sensing (CS)
based channel estimation accuracy. The proposed optimal pilot pattern is
independent of the number of receive antennas, the Doppler shifts, the train
position, or the train speed. Simulation results confirms the performance
merits of the proposed scheme in high-mobility environments. In addition, it is
also shown that the proposed scheme is robust to the respect of high mobility
High-Dimensional CSI Acquisition in Massive MIMO: Sparsity-Inspired Approaches
Massive MIMO has been regarded as one of the key technologies for 5G wireless
networks, as it can significantly improve both the spectral efficiency and
energy efficiency. The availability of high-dimensional channel side
information (CSI) is critical for its promised performance gains, but the
overhead of acquiring CSI may potentially deplete the available radio
resources. Fortunately, it has recently been discovered that harnessing various
sparsity structures in massive MIMO channels can lead to significant overhead
reduction, and thus improve the system performance. This paper presents and
discusses the use of sparsity-inspired CSI acquisition techniques for massive
MIMO, as well as the underlying mathematical theory. Sparsity-inspired
approaches for both frequency-division duplexing and time-division duplexing
massive MIMO systems will be examined and compared from an overall system
perspective, including the design trade-offs between the two duplexing modes,
computational complexity of acquisition algorithms, and applicability of
sparsity structures. Meanwhile, some future prospects for research on
high-dimensional CSI acquisition to meet practical demands will be identified.Comment: 15 pages, 3 figures, 1 table, submitted to IEEE Systems Journal
Special Issue on 5G Wireless Systems with Massive MIM
Channel Estimation and ICI Cancelation in Vehicular Channels of OFDM Wireless Communication Systems
Orthogonal frequency division multiplexing (OFDM) scheme increases bandwidth efficiency (BE) of data transmission and eliminates inter symbol interference (ISI). As a result, it has been widely used for wideband communication systems that have been developed during the past two decades and it can be a good candidate for the emerging communication systems such as fifth generation (5G) cellular networks with high carrier frequency and communication systems of high speed vehicles such as high speed trains (HSTs) and supersonic unmanned aircraft vehicles (UAVs). However, the employment of OFDM for those upcoming systems is challenging because of high Doppler shifts. High Doppler shift makes the wideband communication channel to be both frequency selective and time selective, doubly selective (DS), causes inter carrier interference (ICI) and destroys the orthogonality between the subcarriers of OFDM signal. In order to demodulate the signal in OFDM systems and mitigate ICIs, channel state information (CSI) is required. In this work, we deal with channel estimation (CE) and ICI cancellation in DS vehicular channels. The digitized model of the DS channels can be short and dense, or long and sparse. CE methods that perform well for short and dense channels are highly inefficient for long and sparse channels. As a result, for the latter type of channels, we proposed the employment of compressed sensing (CS) based schemes for estimating the channel. In addition, we extended our CE methods for multiple input multiple output (MIMO) scenarios. We evaluated the CE accuracy and data demodulation fidelity, along with the BE and computational complexity of our methods and compared the results with the previous CE procedures in different environments. The simulation results indicate that our proposed CE methods perform considerably better than the conventional CE schemes
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
- …