205 research outputs found
Semi-blind Channel Estimation and Data Detection for Multi-cell Massive MIMO Systems on Time-Varying Channels
We study the problem of semi-blind channel estimation and symbol detection in
the uplink of multi-cell massive MIMO systems with spatially correlated
time-varying channels. An algorithm based on expectation propagation (EP) is
developed to iteratively approximate the joint a posteriori distribution of the
unknown channel matrix and the transmitted data symbols with a distribution
from an exponential family. This distribution is then used for direct
estimation of the channel matrix and detection of data symbols. A modified
version of the popular Kalman filtering algorithm referred to as KF-M emerges
from our EP derivation and it is used to initialize the EP-based algorithm.
Performance of the Kalman smoothing algorithm followed by KF-M is also
examined. Simulation results demonstrate that channel estimation error and the
symbol error rate (SER) of the semi-blind KF-M, KS-M, and EP-based algorithms
improve with the increase in the number of base station antennas and the length
of the transmitted frame. It is shown that the EP-based algorithm significantly
outperforms KF-M and KS-M algorithms in channel estimation and symbol
detection. Finally, our results show that when applied to time-varying
channels, these algorithms outperform the algorithms that are developed for
block-fading channel models.Comment: 28 pages, 13 figures, Submitted to IEEE Trans. on Vehicular
Technolog
Low-Rank Channel Estimation for Millimeter Wave and Terahertz Hybrid MIMO Systems
Massive multiple-input multiple-output (MIMO) is one of the fundamental technologies for 5G and beyond. The increased number of antenna elements at both the transmitter and the receiver translates into a large-dimension channel matrix. In addition, the power requirements for the massive MIMO systems are high, especially when fully digital transceivers are deployed. To address this challenge, hybrid analog-digital transceivers are considered a viable alternative. However, for hybrid systems, the number of observations during each channel use is reduced. The high dimensions of the channel matrix and the reduced number of observations make the channel estimation task challenging. Thus, channel estimation may require increased training overhead and higher computational complexity.
The need for high data rates is increasing rapidly, forcing a shift of wireless communication towards higher frequency bands such as millimeter Wave (mmWave) and terahertz (THz). The wireless channel at these bands is comprised of only a few dominant paths. This makes the channel sparse in the angular domain and the resulting channel matrix has a low rank. This thesis aims to provide channel estimation solutions benefiting from the low rankness and sparse nature of the channel. The motivation behind this thesis is to offer a desirable trade-off between training overhead and computational complexity while providing a desirable estimate of the channel
Tensor-based signal processing with applications to MIMO-ODFM systems and intelligent reflecting surfaces
Der Einsatz von Tensor-Algebra-Techniken in der Signalverarbeitung hat in den letzten zwei Jahrzehnten zugenommen. Anwendungen wie Bildverarbeitung, biomedizinische Signalverarbeitung, radar, maschinelles Lernen, deep Learning und Kommunikation im Allgemeinen verwenden weitgehend tensorbasierte Verarbeitungstechniken zur Wiederherstellung, Schätzung und Klassifizierung von Signalen. Einer der Hauptgründe für den Einsatz der Tensorsignalverarbeitung ist die Ausnutzung der mehrdimensionalen Struktur von Signalen, wobei die Einzigartigkeitseigenschaften der Tensor-Zerlegung profitieren. Bei der drahtlosen Kommunikation beispielsweise können die Signale mehrere "Dimensionen" haben, wie Raum, Zeit, Frequenz, Polarisation, usw. Diese Arbeit ist in zwei Teile gegliedert. Im ersten Teil betrachten wir die Anwendung von Tensor-basierten Algorithmen für multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) Systeme unter Berücksichtigung von Vorhandensein von Phasenrauschenstörungen. In diesem Teil schlagen wir einen zweistufigen tensorbasierten Empfänger für eine gemeinsame Kanal-, Phasenrausch- und Datenschätzung in MIMO-OFDM-Systemen vor. In der ersten Stufe zeigen wir, dass das empfangene Signal auf den Pilotunterträgern als PARAFAC-Tensor dritter Ordnung modelliert werden kann. Auf der Grundlage dieses Modells werden zwei Algorithmen für die Schätzung der Phasen- und Kanalrauschen in den Pilotton vorgeschlagen. In der zweiten Stufe werden die übertragenen Daten geschätzt. Zu diesem Zweck schlagen wir einen Zero Forcing (ZF)-Empfänger vor, der sich die Tensorstruktur des empfangenen Signals auf den Datenträgern zunutze macht, indem er den vorgeschlagenen selektiven Kronecker-Produkt-Operators (SKP) kapitalisiert. Die Simulationsergebnisse zeigen, dass der vorgeschlagene Empfänger sowohl bei der Symbolfehlerrate als auch beim normalisierten mittleren quadratischen Fehler des geschätzten Kanal- und Phasenrauschmatrizen eine bessere Leistung im Vergleich zum Stand der Technik erzielt. Der zweite Teil dieser Arbeit befasst sich mit der Anwendung der Tensormodellierung zur Reduzierung des Kontrollsignalisierungsoverhead in zukünftigen drahtlosen Systemen, die durch intelligent reconfigurable surfaces (IRSs) unterstützt werden. Zu diesem Zweck schlagen wir eine Annäherung an die nahezu optimalen IRS-Phasenverschiebungen vor, die sonst einen prohibitiv hohen Kommunikationsoverhead auf den BS-IRS-Kontrollverbindungen verursachen würde. Die Hauptidee besteht darin, den optimalen Phasenvektor des IRSs, der Hunderte oder Tausende von Elementen haben kann, durch ein Tensormodell mit niedrigem Rang darzustellen. Dies wird erreicht durch Faktorisierung einer tensorisierten Version des IRS-Phasenverschiebungsvektors, wobei jede Komponente als Kronecker-Produkt einer vordefinierten Anzahl von Faktoren mit kleinerer Größe modelliert wird, die durch Tensor Zerlegungsalgorithmen erhaltet werden können. Wir zeigen, dass die vorgeschlagenen Low-Rank-Modelle die Rückkopplungsanforderungen für die BS-IRS-Kontrollverbindungen drastisch reduzieren. Die Simulationsergebnisse zeigen, dass die vorgeschlagene Methode besonders in Szenarien mit einer starken Sichtverbindung attraktiv sind. In diesem Fall wird fast die gleiche spektrale Effizienz erreicht wie in den Fällen mit nahezu optimalen Phasenverschiebungen, jedoch mit einem drastisch reduzierten Kommunikations-Overhead.The use of tensor algebra techniques in signal processing has been growing over the last two decades. Applications like image processing, biomedical signal processing, radar, machine/deep learning, and communications in general, largely employ tensor-based techniques for recovery, estimating, and classifying signals. One of the main reasons for using tensor signal processing is the exploitation of the multidimensional structure of signals, while benefiting from the uniqueness properties of tensor decomposition. For example, in wireless communications, the signals can have several “dimensions", e.g., space, time, frequency, polarization, beamspace, etc. This thesis is divided into two parts, first, in the application of a tensor-based algorithm in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems with the presence of phase-noise impairments. In this first part, we propose a two-stage tensor-based receiver for a joint channel, phase-noise, and data estimation in MIMO-OFDM systems. In the first stage, we show that the received signal at the pilot subcarriers can be modeled as a third-order PARAFAC tensor. Based on this model, we propose two algorithms for channel and phase-noise estimation at the pilot subcarriers. The second stage consists of data estimation, for which we propose a ZF receiver that capitalizes on the tensor structure of the received signal at the data subcarriers using the proposed SKP operator. Numerical simulations show that the proposed receivers achieves an improved performance compared to the state-of-art receivers in terms of symbol error rate (SER) and normalized mean square error (NMSE) of the estimated channel and phase-noise matrices. The second part of this thesis focuses on the application of tensor modeling to reduce the control signaling overhead in future wireless systems aided by intelligent reconfigurable surfaces (IRS). To this end, we propose a low-rank approximation of the near-optimal IRS phase-shifts, which would incur prohibitively high communication overhead on the BS-IRS controller links. The key idea is to represent the potentially large IRS phase-shift vector using a low-rank tensor model. This is achieved by factorizing a tensorized version of the IRS phase-shift vector, where each component is modeled as the Kronecker product of a predefined number of factors of smaller sizes, which can be obtained via tensor decomposition algorithms. We show that the proposed low-rank models drastically reduce the required feedback requirements associated with the BS-IRS control links. Simulation results indicate that the proposed method is especially attractive in scenarios with a strong line of sight component, in which case nearly the same spectral efficiency is reached as in the cases with near-optimal phase-shifts, but with a drastically reduced communication overhead
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G
Sixth-generation (6G) mobile communication networks are expected to have
dense infrastructures, large-dimensional channels, cost-effective hardware,
diversified positioning methods, and enhanced intelligence. Such trends bring
both new challenges and opportunities for the practical design of 6G. On one
hand, acquiring channel state information (CSI) in real time for all wireless
links becomes quite challenging in 6G. On the other hand, there would be
numerous data sources in 6G containing high-quality location-tagged channel
data, making it possible to better learn the local wireless environment. By
exploiting such new opportunities and for tackling the CSI acquisition
challenge, there is a promising paradigm shift from the conventional
environment-unaware communications to the new environment-aware communications
based on the novel approach of channel knowledge map (CKM). This article aims
to provide a comprehensive tutorial overview on environment-aware
communications enabled by CKM to fully harness its benefits for 6G. First, the
basic concept of CKM is presented, and a comparison of CKM with various
existing channel inference techniques is discussed. Next, the main techniques
for CKM construction are discussed, including both the model-free and
model-assisted approaches. Furthermore, a general framework is presented for
the utilization of CKM to achieve environment-aware communications, followed by
some typical CKM-aided communication scenarios. Finally, important open
problems in CKM research are highlighted and potential solutions are discussed
to inspire future work
Channel Estimation in Multi-user Massive MIMO Systems by Expectation Propagation based Algorithms
Massive multiple input multiple output (MIMO) technology uses large antenna arrays with tens or hundreds of antennas at the base station (BS) to achieve high spectral efficiency, high diversity, and high capacity. These benefits, however, rely on obtaining accurate channel state information (CSI) at the receiver for both uplink and downlink channels. Traditionally, pilot sequences are transmitted and used at the receiver to estimate the CSI. Since the length of the pilot sequences scale with the number of transmit antennas, for massive MIMO systems downlink channel estimation requires long pilot sequences resulting in reduced spectral efficiency and the so-called pilot contamination due to sharing of the pilots in adjacent cells.
In this dissertation we first review the problem of channel estimation in massive MIMO systems. Next, we study the problem of semi-blind channel estimation in the uplink in the case of spatially correlated time-varying channels. The proposed method uses the transmitted data symbols as virtual pilots to enhance channel estimation. An expectation propagation (EP) algorithm is developed to iteratively approximate the joint a posterior distribution of the unknown channel matrix and the transmitted data symbols with a distribution from an exponential family. The distribution is then used for direct estimation of the channel matrix and detection of the data symbols. A modified version of Kalman filtering algorithm referred to as KF-M emerges from our EP derivation and it is used to initialize our algorithm. Simulation results demonstrate that channel estimation error and the symbol error rate of the proposed algorithm improve with the increase in the number of BS antennas or the number of data symbols in the transmitted frame. Moreover, the proposed algorithms can mitigate the effects of pilot contamination as well as time-variations of the channel.
Next, we study the problem of downlink channel estimation in multi-user massive MIMO systems. Our approach is based on Bayesian compressive sensing in which the clustered sparse structure of the channel in the angular domain is exploited to reduce the pilot overhead. To capture the clustered structure, we employ a conditionally independent identically distributed Bernoulli-Gaussian prior on the sparse vector representing the channel, and a Markov prior on its support vector. An EP algorithm is developed to approximate the intractable joint distribution on the sparse vector and its support with a distribution from an exponential family. This distribution is then used for direct estimation of the channel. The EP algorithm requires the model parameters which are unknown. We estimate these parameters using the expectation maximization (EM) algorithm. Simulation results show that the proposed combination of EM and EP referred to as EM-EP algorithm outperforms several recently-proposed algorithms in the literature
Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications
Reconfigurable Intelligent Surfaces (RISs) have been recently considered as
an energy-efficient solution for future wireless networks due to their fast and
low-power configuration, which has increased potential in enabling massive
connectivity and low-latency communications. Accurate and low-overhead channel
estimation in RIS-based systems is one of the most critical challenges due to
the usually large number of RIS unit elements and their distinctive hardware
constraints. In this paper, we focus on the downlink of a RIS-empowered
multi-user Multiple Input Single Output (MISO) downlink communication systems
and propose a channel estimation framework based on the PARAllel FACtor
(PARAFAC) decomposition to unfold the resulting cascaded channel model. We
present two iterative estimation algorithms for the channels between the base
station and RIS, as well as the channels between RIS and users. One is based on
alternating least squares (ALS), while the other uses vector approximate
message passing to iteratively reconstruct two unknown channels from the
estimated vectors. To theoretically assess the performance of the ALS-based
algorithm, we derived its estimation Cram\'er-Rao Bound (CRB). We also discuss
the achievable sum-rate computation with estimated channels and different
precoding schemes for the base station. Our extensive simulation results show
that our algorithms outperform benchmark schemes and that the ALS technique
achieve the CRB. It is also demonstrated that the sum rate using the estimated
channels reached that of perfect channel estimation under various settings,
thus, verifying the effectiveness and robustness of the proposed estimation
algorithms
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