17 research outputs found
Multi-user mmWave MIMO channel estimation with hybrid Beamforming over frequency selective fading channels
In multi-user millimeter wave (mmWave) multiple input multiple output (MIMO) systems, obtaining accurate information/knowledge regarding the channel state is crucial to achieving multi-user interference cancellation and reliable beamforming (BF)-to compensate for severe path loss. This knowledge is nonetheless very challenging to acquire in practice since large antenna arrays experience a low signal-to-noise ratio (SNR) before BF. In this paper, a multi-user channel estimation (CE) scheme namely generalized-block compressed sampling matching pursuit (G-BCoSaMP), is proposed for multi-user mmWave MIMO systems over frequency selective fading channels. This scheme exploits the cluster-structured sparsity in the angular and delay domain of mmWave channels determined by the actual spatial frequencies of each path. As the corresponding spatial frequencies of multi-user mmWave MIMO systems with Hybrid BF often fall between the discrete Fourier transform (DFT) bins due to the continuous Angle of Arrival (AoA)/Angle of Departure (AoD), the proposed G-BCoSaMP algorithm can address the resulting power leakage problem. Simulation results show that the proposed algorithm is effective and offer a better CE performance in terms of MSE when compared to the generalized block orthogonal matching pursuit (G-BOMP) algorithm that does not possess a pruning step
A Genetic Algorithm-based Beamforming Approach for Delay-constrained Networks
In this paper, we study the performance of initial access beamforming schemes
in the cases with large but finite number of transmit antennas and users.
Particularly, we develop an efficient beamforming scheme using genetic
algorithms. Moreover, taking the millimeter wave communication characteristics
and different metrics into account, we investigate the effect of various
parameters such as number of antennas/receivers, beamforming resolution as well
as hardware impairments on the system performance. As shown, our proposed
algorithm is generic in the sense that it can be effectively applied with
different channel models, metrics and beamforming methods. Also, our results
indicate that the proposed scheme can reach (almost) the same end-to-end
throughput as the exhaustive search-based optimal approach with considerably
less implementation complexity
Initial Access Frameworks for 3GPP NR at mmWave Frequencies
The use of millimeter wave (mmWave) frequencies for communication will be one
of the innovations of the next generation of cellular mobile networks (5G). It
will provide unprecedented data rates, but is highly susceptible to rapid
channel variations and suffers from severe isotropic pathloss. Highly
directional antennas at the transmitter and the receiver will be used to
compensate for these shortcomings and achieve sufficient link budget in wide
area networks. However, directionality demands precise alignment of the
transmitter and the receiver beams, an operation which has important
implications for control plane procedures, such as initial access, and may
increase the delay of the data transmission. This paper provides a comparison
of measurement frameworks for initial access in mmWave cellular networks in
terms of detection accuracy, reactiveness and overhead, using parameters
recently standardized by the 3GPP and a channel model based on real-world
measurements. We show that the best strategy depends on the specific
environment in which the nodes are deployed, and provide guidelines to
characterize the optimal choice as a function of the system parameters.Comment: 8 pages, 7 figures, 3 tables, accepted to the IEEE 17th Annual
Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net). arXiv admin note:
substantial text overlap with arXiv:1804.0190
Position and Orientation Estimation through Millimeter Wave MIMO in 5G Systems
Millimeter wave signals and large antenna arrays are considered enabling
technologies for future 5G networks. While their benefits for achieving
high-data rate communications are well-known, their potential advantages for
accurate positioning are largely undiscovered. We derive the Cram\'{e}r-Rao
bound (CRB) on position and rotation angle estimation uncertainty from
millimeter wave signals from a single transmitter, in the presence of
scatterers. We also present a novel two-stage algorithm for position and
rotation angle estimation that attains the CRB for average to high
signal-to-noise ratio. The algorithm is based on multiple measurement vectors
matching pursuit for coarse estimation, followed by a refinement stage based on
the space-alternating generalized expectation maximization algorithm. We find
that accurate position and rotation angle estimation is possible using signals
from a single transmitter, in either line-of- sight, non-line-of-sight, or
obstructed-line-of-sight conditions.Comment: The manuscript has been revised, and increased from 27 to 31 pages.
Also, Fig.2, Fig. 10 and Table I are adde
Subspace Tracking and Least Squares Approaches to Channel Estimation in Millimeter Wave Multiuser MIMO
The problem of MIMO channel estimation at millimeter wave frequencies, both
in a single-user and in a multi-user setting, is tackled in this paper. Using a
subspace approach, we develop a protocol enabling the estimation of the right
(resp. left) singular vectors at the transmitter (resp. receiver) side; then,
we adapt the projection approximation subspace tracking with deflation and the
orthogonal Oja algorithms to our framework and obtain two channel estimation
algorithms. We also present an alternative algorithm based on the least squares
approach. The hybrid analog/digital nature of the beamformer is also explicitly
taken into account at the algorithm design stage. In order to limit the system
complexity, a fixed analog beamformer is used at both sides of the
communication links. The obtained numerical results, showing the accuracy in
the estimation of the channel matrix dominant singular vectors, the system
achievable spectral efficiency, and the system bit-error-rate, prove that the
proposed algorithms are effective, and that they compare favorably, in terms of
the performance-complexity trade-off, with respect to several competing
alternatives.Comment: To appear on the IEEE Transactions on Communication
A Comparison of Beam Refinement Algorithms for Millimeter Wave Initial Access
Initial access (IA) is identified as a key challenge for the upcoming 5G mobile communication system operating at high carrier frequencies, and several techniques are currently being proposed. In this paper, we extend our previously proposed genetic algorithm (GA)-based beam refinement scheme to include beamforming at both the transmitter and the receiver, and compare the performance with alternative approaches in the millimeter wave multi-user multiple-input-multiple-output (MU-MIMO) networks. Taking the millimeter wave communications characteristics and various metrics into account, we investigate the effect of different parameters such as the number of transmit antennas/users/per-user receive antennas, beamforming resolution as well as hardware impairments on the system performance employing different beam refinement algorithms. As shown, our proposed GA-based approach performs well in delay-constrained networks with multi-antenna users. Compared to the considered state-of-the-art schemes, our method reaches the highest service outage-constrained end-to-end throughput with considerably less implementation complexity. Moreover, taking the users\u27 mobility into account, GA-based approach can remarkably reduce the beam refinement delay at low/moderate speeds when the spatial correlation is taken into account
Genetic Algorithm-Based Beam Refinement for Initial Access in Millimeter Wave Mobile Networks
Initial access (IA) is identified as a key challenge for the upcoming 5G mobile communication system operating at high carrier frequencies, and several techniques are currently being proposed. In this paper, we extend our previously proposed efficient genetic algorithm-(GA-) based beam refinement scheme to include beamforming at both the transmitter and the receiver and compare the performance with alternative approaches in the millimeter wave multiuser multiple-input-multiple-output (MU-MIMO) networks. Taking the millimeter wave communications characteristics and various metrics into account, we investigate the effect of different parameters such as the number of transmit antennas/users/per-user receive antennas, beamforming resolutions, and hardware impairments on the system performance employing different beam refinement algorithms. As shown, our proposed GA-based approach performs well in delay-constrained networks with multiantenna users. Compared to the considered state-of-the-art schemes, our method reaches the highest service outage-constrained end-to-end throughput with considerably less implementation complexity. Moreover, taking the users\u27 mobility into account, our GA-based approach can remarkably reduce the beam refinement delay at low/moderate speeds when the spatial correlation is taken into account. Finally, we compare the cases of collaborative users and noncollaborative users and evaluate their difference in system performance
Joint Analog Beam Selection and Digital Beamforming in Millimeter Wave Cell-Free Massive MIMO Systems
Cell-free massive MIMO systems consist of many distributed access points with
simple components that jointly serve the users. In millimeter wave bands, only
a limited set of predetermined beams can be supported. In a network that
consolidates these technologies, downlink analog beam selection stands as a
challenging task for the network sum-rate maximization. Low-cost digital
filters can improve the network sum-rate further. In this work, we propose
low-cost joint designs of analog beam selection and digital filters. The
proposed joint designs achieve significantly higher sum-rates than the disjoint
design benchmark. Supervised machine learning (ML) algorithms can efficiently
approximate the input-output mapping functions of the beam selection decisions
of the joint designs with low computational complexities. Since the training of
ML algorithms is performed off-line, we propose a well-constructed joint design
that combines multiple initializations, iterations, and selection features, as
well as beam conflict control, i.e., the same beam cannot be used for multiple
users. The numerical results indicate that ML algorithms can retain 99-100% of
the original sum-rate results achieved by the proposed well-constructed
designs.Comment: 14 pages, 11 figures. First submission date: August 19th, 2020. To be
published at IEEE Open Journal of the Communications Societ