Massive MIMO channel estimation considering pilot contamination and spatially correlated channels

Figueiredo, Felipe Augusto Pereira de; Lemes, Dimas Augusto Mendes; Dias, Cláudio Ferreira; Fraidenraich, Gustavo

doi:10.1049/el.2019.3899

Cited by 18 publications

(7 citation statements)

References 5 publications

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“…To ensure wireless data transmission efficiency and minimize the pilot signaling overhead, pilot training sequences, either orthogonal or non-orthogonal, are reused in the neighboring cells in mobile network architecture. The sole reason is that pilot training sequences are restricted by the channel coherence time interval, and with the increase in the number of smart nodes, more pilot sequences need to be reassigned [36]. Thus, the reuse of pilot sequences in the neighboring cells becomes the reason for frequent interference issues.…”

Section: Pilot Contaminationmentioning

confidence: 99%

Multi-antenna Arrays Based Massive-MIMO for B5G/6G: State-of-the-Art, Challenges and Future Research Directions

Qamar,

Kazmi,

Ariffin

et al. 2024

Preprint

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This comprehensive article explores Massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) and 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, Cell-Free MIMO, and security considerations, the article delves into the intricacies of M-MIMO in the evolving landscape of B5G. Moreover, the emerging MIMO concepts in this article include AI-enabled M-MIMO three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, and THz spectrum utilization. This review highlights challenges and open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of Federated Learning in M-MIMO systems. By examining these cutting-edge developments, the article aims to contribute to advancing knowledge in the field and inspire future research directions in the exciting realm of B5G and 6G networks.

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Section: Pilot Contaminationmentioning

confidence: 99%

Multi-antenna Arrays Based Massive-MIMO for B5G/6G: State-of-the-Art, Challenges and Future Research Directions

Qamar,

Kazmi,

Ariffin

et al. 2024

Preprint

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“…Thus, guided by AI techniques, beam management can work based on context information, which is obtained as an alternative to the conventional use of pilot signals for channel estimation. Images, geopositioning coordinates, and data from other users are examples of context information that can be used to manage beams [23,24]. Simply put, for a given input dataset, AI models map this information into the beam domain; that is, they map several input pieces of information into the most appropriate beam.…”

Section: Of 50mentioning

confidence: 99%

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

Brilhante¹,

Manjares²,

Moreira³

et al. 2023

Preprint

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The performance of modern wireless communication systems is highly dependent on the adoption of multiple antennas and the associated signal processing. In 5G and 6G networks, beamforming and beam management become challenging tasks due to aspects such as user mobility, increased number of antennas, and the adoption of higher frequencies. Artificial intelligence, and more specifically, machine learning, are efficient tools to reduce the complexity involved in generating beams and the overhead associated with beam management without sacrificing system performance. Therefore, AI-aided beamforming and beam management have received a lot of attention recently. This article presents a complete survey on this topic, emphasizing open problems and promising directions. The discussion includes architectural and signal processing aspects of modern beamforming and beam management. The article presents communication problems and respective solutions using centralized/decentralized, supervised/unsupervised, semi-supervised, active, federated, and reinforcement learning.

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“…The suggested PE based MMSE estimator provides an almost-optimal Mean Square Error (MSE) even if a low degree of PE is chosen. The authors in [18] addressed the spatially correlated channel, which uses the exponential correlation model to describe the correlation across channels. An approximate MMSE estimator is suggested based on a sample covariance matrix, wherein the efficiency of the suggested estimator is attached to the number of samples used to estimate the actual covariance matrix.…”

Section: Related Workmentioning

confidence: 99%

“…indicates the Frobenius norm, where λ ik = Tr(λ H ik λ ik ) in which T r(•) indicates the Trace operator. E{λ H ik λ ik } = M Λ ik I P and according to equation(18), M Λik = actual value M Λ ik . By replacing Λ ik with the estimated expression (i.e., Λik ) in the MMSE estimator expression, yielding the suggested estimator expression, which written as follows…”

mentioning

confidence: 99%

On Channel Estimation and Power Control for Multi-cell Multi-path Massive MIMO TDD System

Amadid

Belhabib

Boulouird

et al. 2022

Preprint

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Some more practical channels that model the networks in a real environment is the multi-path communication channels. In order to investigate these communications channels. This work addressed Channel Estimation (CE) in the Uplink (UL) phase for a multi-cell multi-user massive multipleinput multiple-output (M-MIMO) system that studies multi-path communication between each user and its serving Base Station (BS). We suppose that the network operates under Time-Division Duplex (TDD) protocol. We studied and analyzed the multi-path channels and their benefit over CE since it presents a more realistic channel that displays a real propagation circumstance. on the flip side, we evaluated the CE quality using ideal MinimumMean Square Error (MMSE). This latter relies on an impractical property that can be explicated since the MMSE estimator considers foreknowledge on Large-Scale Fading (LSF) coefficients of interfering users. Thus, the suggested estimator is introduced to overcome this issue, where the suggested estimator tackled this problem and presented result asymptotic approaches to the performance of the MMSE estimator. Besides, we considered a more real communication in which the multi-path channels are either realized using Non-Line-of-Sight (NLoS) only or using both Line-of-Sight (LoS) and NLoS path depending on the distance at which the user is located from his serving BS. Otherwise, in numerous scenarios, users at the cell edge are strongly affected by Pilot Contamination (PC). Hence, we introduced a Power Control (PoC) policy so that the users at the cell edge are less affected by the PC problem. In the simulation results segment, the analytic and simulated results are introduced to assert our theoretical study.

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Massive MIMO channel estimation considering pilot contamination and spatially correlated channels

Abstract: In this Letter, the authors present a study on linear channel estimators and their respective mean square error expressions acknowledging spatially correlated channels and pilot contamination. They also investigate the impact of imperfect channel covariance matrix knowledge.

Cited by 18 publications

References 5 publications

Multi-antenna Arrays Based Massive-MIMO for B5G/6G: State-of-the-Art, Challenges and Future Research Directions

Multi-antenna Arrays Based Massive-MIMO for B5G/6G: State-of-the-Art, Challenges and Future Research Directions

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

On Channel Estimation and Power Control for Multi-cell Multi-path Massive MIMO TDD System

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