8 research outputs found
Optimal Hybrid Beamforming for Multiuser Massive MIMO Systems With Individual SINR Constraints
In this letter, we consider optimal hybrid beamforming design to minimize the
transmission power under individual signal-to-interference-plus-noise ratio
(SINR) constraints in a multiuser massive multiple-input-multiple-output (MIMO)
system. This results in a challenging non-convex optimization problem. We
consider two cases. In the case where the number of users is smaller than or
equal to that of radio frequency (RF) chains, we propose a low-complexity
method to obtain a globally optimal solution and show that it achieves the same
transmission power as an optimal fully-digital beamformer. In the case where
the number of users is larger than that of RF chains, we propose a
low-complexity globally convergent alternating algorithm to obtain a stationary
point.Comment: 4 pages, 3 figures, to be published in IEEE Wireless Communications
Letter
Wideband Hybrid Precoding for Next-Generation Backhaul/Fronthaul Based on mmWave FD-MIMO
Millimeter-wave (mmWave) communication is considered as an indispensable
technique for the next-generation backhaul/fronthaul network thanks to its
large transmission bandwidth. Especially for heterogeneous network (HetNet),
the mmWave full-dimension (FD)-MIMO is exploited to establish the
backhaul/fronthaul link between phantom-cell base stations (BSs) and macro-cell
BSs, where an efficient precoding is prerequisite. Against this background,
this paper proposes a principle component analysis (PCA)-based hybrid precoding
for wideband mmWave MIMO backhaul/fronthaul channels. We first propose an
optimal hybrid precoder by exploiting principal component analysis (PCA),
whereby the optimal high dimensional frequency-selective precoder are projected
to the low-dimensional frequency-flat precoder. Moreover, the combiner is
designed by leveraging the weighted PCA, where the covariance of received
signal is taken into account as weight to the optimal minimum mean square error
(MMSE) fully-digital combiner for further improved performance. Simulations
have confirmed that the proposed scheme outperforms conventional schemes in
spectral efficiency (SE) and bit-error-rate (BER) performance.Comment: This paper has been accepted by 2018 GLOBECOM worksho
Machine Learning Based Hybrid Precoding for MmWave MIMO-OFDM with Dynamic Subarray
Hybrid precoding design can be challenging for broadband millimeter-wave
(mmWave) massive MIMO due to the frequency-flat analog precoder in radio
frequency (RF). Prior broadband hybrid precoding work usually focuses on
fully-connected array (FCA), while seldom considers the energy-efficient
partially-connected subarray (PCS) including the fixed subarray (FS) and
dynamic subarray (DS). Against this background, this paper proposes a machine
learning based broadband hybrid precoding for mmWave massive MIMO with DS.
Specifically, we first propose an optimal hybrid precoder based on principal
component analysis (PCA) for the FS, whereby the frequency-flat RF precoder for
each subarray is extracted from the principle component of the optimal
frequency-selective precoders for fully-digital MIMO. Moreover, we extend the
PCA-based hybrid precoding to DS, where a shared agglomerative hierarchical
clustering (AHC) algorithm developed from machine learning is proposed to group
the DS for improved spectral efficiency (SE). Finally, we investigate the
energy efficiency (EE) of the proposed scheme for both passive and active
antennas. Simulations have confirmed that the proposed scheme outperforms
conventional schemes in both SE and EE.Comment: This paper has been accepted by 2018 GLOBECOM workshop. arXiv admin
note: text overlap with arXiv:1809.0336
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO
In this paper, we propose a data-driven deep learning (DL) approach to
jointly design the pilot signals and channel estimator for wideband massive
multiple-input multiple-output (MIMO) systems. By exploiting the angular-domain
compressibility of massive MIMO channels, the conceived DL framework can
reliably reconstruct the high-dimensional channels from the under-determined
measurements. Specifically, we design an end-to-end deep neural network (DNN)
architecture composed of dimensionality reduction network and reconstruction
network to respectively mimic the pilot signals and channel estimator, which
can be acquired by data-driven deep learning. For the dimensionality reduction
network, we design a fully-connected layer by compressing the high-dimensional
massive MIMO channel vector as input to low-dimensional received measurements,
where the weights are regarded as the pilot signals. For the reconstruction
network, we design a fully-connected layer followed by multiple cascaded
convolutional layers, which will reconstruct the high-dimensional channel as
the output. By defining the mean square error between input and output as loss
function, we leverage Adam algorithm to train the end-to-end DNN aforementioned
with extensive channel samples. In this way, both the pilot signals and channel
estimator can be simultaneously obtained. The simulation results demonstrate
that the superiority of the proposed solution over state-of-the-art compressive
sensing approaches.Comment: 6 pages 4 fugures;accepted by IEEE Transactions on Vehicular
Technolog
GMD-Based Hybrid Beamforming for Large Reconfigurable Intelligent Surface Assisted Millimeter-Wave Massive MIMO
Reconfigurable intelligent surface (RIS) is considered to be an
energy-efficient approach to reshape the wireless environment for improved
throughput. Its passive feature greatly reduces the energy consumption, which
makes RIS a promising technique for enabling the future smart city. Existing
beamforming designs for RIS mainly focus on optimizing the spectral efficiency
for single carrier systems. To avoid the complicated bit allocation on
different spatial domain subchannels in MIMO systems, in this paper, we propose
a geometric mean decomposition-based beamforming for RIS-assisted millimeter
wave (mmWave) hybrid MIMO systems so that multiple parallel data streams in the
spatial domain can be considered to have the same channel gain. Specifically,
by exploiting the common angular-domain sparsity of mmWave massive MIMO
channels over different subcarriers, a simultaneous orthogonal match pursuit
algorithm is utilized to obtain the optimal multiple beams from an oversampling
2D-DFT codebook. Moreover, by only leveraging the angle of arrival and angle of
departure associated with the line of sight (LoS) channels, we further design
the phase shifters for RIS by maximizing the array gain for LoS channel.
Simulation results show that the proposed scheme can achieve better BER
performance than conventional approaches. Our work is an initial attempt to
discuss the broadband hybrid beamforming for RIS-assisted mmWave hybrid MIMO
systems.Comment: 8 pages, 6 figures, accepted by IEEE Access.This is an initial
attempt to discuss the broadband hybrid beamforming for RIS-assisted mmWave
hybrid MIMO system
Generalized Beamspace Modulation Using Multiplexing: A Breakthrough in mmWave MIMO
Spatial multiplexing (SMX) multiple-input multiple-output (MIMO) over the
best beamspace was considered as the best solution for millimeter wave (mmWave)
communications regarding spectral efficiency (SE), referred as the best
beamspace selection (BBS) solution. The equivalent MIMO water-filling (WF-MIMO)
channel capacity was treated as an unsurpassed SE upper bound. Recently,
researchers have proposed various schemes trying to approach the benchmark and
the performance bound. But, are they the real limit of mmWave MIMO systems with
reduced radio-frequency (RF) chains? In this paper, we challenge the benchmark
and the corresponding bound by proposing a better transmission scheme that
achieves higher SE, namely the Generalized Beamspace Modulation using
Multiplexing (GBMM). Inspired by the concept of spatial modulation, besides the
selected beamspace, the selection operation is used to carry information. We
prove that GBMM is superior to BBS in terms of SE and can break through the
well known `upper bound'. That is, GBMM renews the upper bound of the SE. We
investigate SE-oriented precoder activation probability optimization,
fully-digital precoder design, optimal power allocation and hybrid precoder
design for GBMM. A gradient ascent algorithm is developed to find the optimal
solution, which is applicable in all signal-to-noise-ratio (SNR) regimes. The
best solution is derived in the high SNR regime. Additionally, we investigate
the hybrid receiver design and deduce the minimum number of receive RF chains
configured to gain from GBMM in achievable SE. We propose a coding approach to
realize the optimized precoder activation. An extension to mmWave broadband
communications is also discussed. Comparisons with the benchmark (i.e., WF-MIMO
channel capacity) are made under different system configurations to show the
superiority of GBMM.Comment: Conference submitted to IC
Beam Codebook Design for 5G mmWave Terminals
A beam codebook of 5G millimeter wave (mmWave) for data communication
consists of multiple high-peak-gain beams to compensate the high pathloss at
the mmWave bands. These beams also have to point to different angular
directions, such that by performing beam searching over the codebook, a good
mmWave signal coverage over the full sphere around the terminal (spherical
coverage) can be achieved. A model-based beam codebook design that assumes
ideal omni-directional antenna pattern, and neglects the impact of terminal
housing around the antenna, does not work well because the radiation pattern of
a practical mmWave antenna combined with the impact of terminal housing is
highly irregular. In this paper, we propose a novel and efficient data-driven
method to generate a beam codebook to boost the spherical coverage of mmWave
terminals. The method takes as inputs the measured or simulated electric field
response data of each antenna and provides the codebook according to the
requirements on the codebook size, spherical coverage, etc. The method can be
applied in a straightforward manner to different antenna type, antenna array
configuration, placement and terminal housing design. Our simulation results
show that the proposed method generates a codebook better than the benchmark
and 802.15.3c codebooks in terms of the spherical coverage.Comment: 17 pages, 12 figures. Published by IEEE Acces
A New Path Division Multiple Access for the Massive MIMO-OTFS Networks
This paper focuses on a new path division multiple access (PDMA) for both
uplink (UL) and downlink (DL) massive multiple-input multiple-output network
over a high mobility scenario, where the orthogonal time frequency space (OTFS)
is adopted. First, the 3D UL channel model and the received signal model in the
angle-delay-Doppler domain are studied. Secondly, the 3D-Newtonized orthogonal
matching pursuit algorithm is utilized for the extraction of the UL channel
parameters, including channel gains, directions of arrival, delays, and Doppler
frequencies, over the antenna-time-frequency domain. Thirdly, we carefully
analyze energy dispersion and power leakage of the 3D angle-delay-Doppler
channels. Then, along UL, we design a path scheduling algorithm to properly
assign angle-domain resources at user sides and to assure that the observation
regions for different users do not overlap over the 3D cubic area, i.e.,
angle-delay-Doppler domain. After scheduling, different users can map their
respective data to the scheduled delay-Doppler domain grids, and simultaneously
send the data to base station (BS) without inter-user interference in the same
OTFS block. Correspondingly, the signals at desired grids within the 3D
resource space of BS are separately collected to implement the 3D channel
estimation and maximal ratio combining-based data detection over the
angle-delay-Doppler domain. Then, we construct a low complexity beamforming
scheme over the angle-delay-Domain domain to achieve inter-user interference
free DL communication. Simulation results are provided to demonstrate the
validity of our proposed unified UL/DL PDMA scheme.Comment: 30 pages, 12 figure