14,112 research outputs found
Channel Estimation with Dynamic Metasurface Antennas via Model-Based Learning
Dynamic Metasurface Antenna (DMA) is a cutting-edge antenna technology
offering scalable and sustainable solutions for large antenna arrays. The
effectiveness of DMAs stems from their inherent configurable analog signal
processing capabilities, which facilitate cost-limited implementations.
However, when DMAs are used in multiple input multiple output (MIMO)
communication systems, they pose challenges in channel estimation due to their
analog compression. In this paper, we propose two model-based learning methods
to overcome this challenge. Our approach starts by casting channel estimation
as a compressed sensing problem. Here, the sensing matrix is formed using a
random DMA weighting matrix combined with a spatial gridding dictionary. We
then employ the learned iterative shrinkage and thresholding algorithm (LISTA)
to recover the sparse channel parameters. LISTA unfolds the iterative shrinkage
and thresholding algorithm into a neural network and trains the neural network
into a highly efficient channel estimator fitting with the previous channel. As
the sensing matrix is crucial to the accuracy of LISTA recovery, we introduce
another data-aided method, LISTA-sensing matrix optimization (LISTA-SMO), to
jointly optimize the sensing matrix. LISTA-SMO takes LISTA as a backbone and
embeds the sensing matrix optimization layers in LISTA's neural network,
allowing for the optimization of the sensing matrix along with the training of
LISTA. Furthermore, we propose a self-supervised learning technique to tackle
the difficulty of acquiring noise-free data. Our numerical results demonstrate
that LISTA outperforms traditional sparse recovery methods regarding channel
estimation accuracy and efficiency. Besides, LISTA-SMO achieves better channel
accuracy than LISTA, demonstrating the effectiveness in optimizing the sensing
matrix
Analysis of data-aided channel tracking for hybrid massive MIMO systems in millimeter wave communications
As the data traffic in future wireless communications will explosively grow up to 1000
folds by the deployment of 5G, several technologies are emerging to satisfy this demand, including
massive multiple-input multiple-output (MIMO), millimeter wave(mmWave) communications,
Non-Orthogonal Multiple Access (NOMA), etc. The combination of millimeter
wave communication and massive MIMO is a promising solution since it can provide tens
of GHz bandwidth by fundamentally exploring higher unoccupied spectrum resources. As
the wavelength of higher frequency shrinks, it is possible to design more compact antenna
array with a very large number of antennas. However, this will cause enormous hardware
cost, energy consumption and computation complexity of decent RF(Radio Frequency)
chains. To this end, spatial sparsity is widely explored to enable hybrid mmWave massive
MIMO systems with limited RF chains to achieve high spectral and energy efficiency.
On the other hand, channel estimation problem for systems with limited RF chains
is quite challenging due to the unaffordable overhead. To be specific, the conventional
pilot-based channel estimation requires to repeatedly transmit the same pilot because only
a limited number of antennas will be activated for each time slot. Therefore, it consumes
a huge amount of temporal and spectral resources. To overcome this problem, channel
estimation for mmWave massive MIMO systems is still an on-going research area. Among
plenty of candidates, channel tracking is the most promising one. To achieve the extremely
low cost and complexity, which is also the greatest motivation of this thesis, data-aided
channel tracking method is thoroughly investigated with closed-form CRLB(CramΒ΄er-Rao
lower bound). In this thesis, data-aided channel tracking systems with different types of
antenna, including ULA(Uniform Linear Antenna array), DLA(Discrete Lens Antenna ar
ray) and UPA(Uniform Planar Antenna array), are comprehensively studied and proposed,
and the closed-form expressions of the corresponding CRLBs are carefully derived. The
numerical results of the simulations for each case are shown respectively, and they reveal
that the performance of the proposed data-aided channel tracking system approaches the
CRLB very well.
In addition, to further explore the data-aided channel tracking system, the multi-user
scenario is investigated in this thesis. This is motivated by the highway and high-speed
railway application, where overtaking operation happens frequently. In this case, the users
in the same beam suffer from high channel interference, thus degrading the channel estimation
performance or even causing outage. To deal with this issue, we proposed an
estimated SER(Symbol Error Rate) metric to indicate if a scheduling operation is necessary
to be taken place and restart of the whole channel tracking system is required. This
metric is included as the Update phase in the proposed channel tracking method for multiuser
scenario with DLA. The theoretical SER closed-form expression is also derived for
multi-user data detection. The numerical results of the simulations verified the theoretical
SER expression, and the scheduling metric based on the estimated SER performance is
also discussed
Two-tier channel estimation aided near-capacity MIMO transceivers relying on norm-based joint transmit and receive antenna selection
We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains, while dispensing with the idealised simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximumlikelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process
A Data-Aided Channel Estimation Scheme for Decoupled Systems in Heterogeneous Networks
Uplink/downlink (UL/DL) decoupling promises more flexible cell association
and higher throughput in heterogeneous networks (HetNets), however, it hampers
the acquisition of DL channel state information (CSI) in time-division-duplex
(TDD) systems due to different base stations (BSs) connected in UL/DL. In this
paper, we propose a novel data-aided (DA) channel estimation scheme to address
this problem by utilizing decoded UL data to exploit CSI from received UL data
signal in decoupled HetNets where a massive multiple-input multiple-output BS
and dense small cell BSs are deployed. We analytically estimate BER performance
of UL decoded data, which are used to derive an approximated normalized mean
square error (NMSE) expression of the DA minimum mean square error (MMSE)
estimator. Compared with the conventional least square (LS) and MMSE, it is
shown that NMSE performances of all estimators are determined by their
signal-to-noise ratio (SNR)-like terms and there is an increment consisting of
UL data power, UL data length and BER values in the SNR-like term of DA method,
which suggests DA method outperforms the conventional ones in any scenarios.
Higher UL data power, longer UL data length and better BER performance lead to
more accurate estimated channels with DA method. Numerical results verify that
the analytical BER and NMSE results are close to the simulated ones and a
remarkable gain in both NMSE and DL rate can be achieved by DA method in
multiple scenarios with different modulations
Multiuser MIMO-OFDM for Next-Generation Wireless Systems
This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base stationβs or radio portβs coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems
A Coordinated Approach to Channel Estimation in Large-scale Multiple-antenna Systems
This paper addresses the problem of channel estimation in multi-cell
interference-limited cellular networks. We consider systems employing multiple
antennas and are interested in both the finite and large-scale antenna number
regimes (so-called "massive MIMO"). Such systems deal with the multi-cell
interference by way of per-cell beamforming applied at each base station.
Channel estimation in such networks, which is known to be hampered by the pilot
contamination effect, constitute a major bottleneck for overall performance. We
present a novel approach which tackles this problem by enabling a low-rate
coordination between cells during the channel estimation phase itself. The
coordination makes use of the additional second-order statistical information
about the user channels, which are shown to offer a powerful way of
discriminating across interfering users with even strongly correlated pilot
sequences. Importantly, we demonstrate analytically that in the
large-number-of-antennas regime, the pilot contamination effect is made to
vanish completely under certain conditions on the channel covariance. Gains
over the conventional channel estimation framework are confirmed by our
simulations for even small antenna array sizes.Comment: 10 pages, 6 figures, to appear in IEEE Journal on Selected Areas in
Communication
Robust Pilot Decontamination Based on Joint Angle and Power Domain Discrimination
We address the problem of noise and interference corrupted channel estimation
in massive MIMO systems. Interference, which originates from pilot reuse (or
contamination), can in principle be discriminated on the basis of the
distributions of path angles and amplitudes. In this paper we propose novel
robust channel estimation algorithms exploiting path diversity in both angle
and power domains, relying on a suitable combination of the spatial filtering
and amplitude based projection. The proposed approaches are able to cope with a
wide range of system and topology scenarios, including those where, unlike in
previous works, interference channel may overlap with desired channels in terms
of multipath angles of arrival or exceed them in terms of received power. In
particular we establish analytically the conditions under which the proposed
channel estimator is fully decontaminated. Simulation results confirm the
overall system gains when using the new methods.Comment: 14 pages, 5 figures, accepted for publication in IEEE Transactions on
Signal Processin
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