Second-Order Statistics-Aided Channel Estimation for Multipath Massive MIMO-OFDM Systems

Mehrabani, Mohammad Reza; Abolhassani, Bahman; Haddadi, Farzan; Tellambura, Chintha

doi:10.1109/access.2023.3247662

Cited by 3 publications

(5 citation statements)

References 43 publications

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“…• In the first stage, the user broadcasts pilot signals towards the IRSs. We exploit the beam pattern of the IRSs to propose an iterative algorithm for the azimuth and elevation AOA estimation of the LOS path between the user and each IRS [48]. For the near-field region, we partition the IRS into tiles to ensure the far-field condition holds for each tile.…”

Section: B Main Contributionsmentioning

confidence: 99%

“…To accomplish this, the user broadcasts T pilot symbols, x t , with t = 1, 2, • • • , T . The baseband received signal during the t-th time slot at the k-th IRS's receiver RF chain, denoted by y k,t , can be expressed as [48]…”

Section: A System Modelmentioning

confidence: 99%

“…Additionally, h k,q denotes the complex channel gain associated with the q-th signal propagation path. To maintain generality, we assume that when q = 1, we are referring to the channel parameters of the LOS path [48].…”

Section: B Far-field Channel Modelmentioning

confidence: 99%

“…where λ is the wavelength of the carrier frequency. In (3), the channel coefficient of the LOS path, h k,1 , is given by [48]…”

Section: B Far-field Channel Modelmentioning

confidence: 99%

“…In this subsection, we present an iterative algorithm to estimate the azimuth and elevation AOA of the LOS path between the user and each IRS. By following the same approach as [48], we present an iterative algorithm to estimate α k,1,1 and α k,1,2 , where k = 1, 2, • • • , K. The solution of the AOA parameter estimation is given by the following theorem.…”

Section: A First Stage: Aoa Estimation For Each Irsmentioning

confidence: 99%

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User Localization via Multiple Intelligent Reflecting Surfaces for LOS-Dominated Channels

Ghermezcheshmeh,

Zlatanov

2023

IEEE Access

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Intelligent reflecting surfaces (IRSs) in the millimeter-wave (mmWave) and terahertz (THz) frequency bands offer precise wireless localization capabilities for future networks. This paper introduces a localization scheme leveraging multiple IRSs with a single receive radio frequency chain and basic sensing capabilities. The scheme employs a parametric channel model for line-of-sight (LoS) dominated communication in these frequency bands. Specifically, it utilizes the unique beam structure at each IRS to estimate azimuth and elevation angles of arrivals between the user and IRSs. Numerical results demonstrate the scheme's accuracy, even in environments with limited scattering conditions.INDEX TERMS Localization, intellignet reflecting surrface, positioning, parametric channel model, millimeter-wave and terahertz communication, beyond massive MIMO.

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Section: B Main Contributionsmentioning

confidence: 99%

Section: A System Modelmentioning

confidence: 99%

Section: B Far-field Channel Modelmentioning

confidence: 99%

“…where λ is the wavelength of the carrier frequency. In (3), the channel coefficient of the LOS path, h k,1 , is given by [48]…”

Section: B Far-field Channel Modelmentioning

confidence: 99%

Section: A First Stage: Aoa Estimation For Each Irsmentioning

confidence: 99%

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User Localization via Multiple Intelligent Reflecting Surfaces for LOS-Dominated Channels

Ghermezcheshmeh,

Zlatanov

2023

IEEE Access

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An Automated Sparse Channel Estimation Framework for MU‐MIMO‐OFDM System With Adaptive Extreme Learning and Heuristic Mechanism

Roji,

Jayasankar,

Nirmala Devi

2024

Trans Emerging Tel Tech

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Compressed sensing is used for channel estimation in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems, but large‐scale networks face challenges regarding the antenna elements and spatial non‐stationarities. To enhance spectral efficiency in Multi User‐MIMO (MU‐MIMO) systems, essential signals are collected by Quadrature Phase Shift Keying (QPSK) in the transformer phase. Further, the modulated signals are provided to the “Pulse Shaping Algorithm (PSA)” for neglecting the inter‐symbol interference rate along with inter‐carrier interference. Subsequently, in the transmitter phase, the “Inverse Fast Fourier Transforms (IFFT)” technique is performed to map the symbols and then provide the signal to the receiver side. The spare channel is validated using a semi‐blind sparse algorithm, with parameters tuned using the Opposition Mud Ring Algorithm (OMRA). Then, the estimated sparse channel values are used for generating the new data, and the Adaptive Extreme Learning Model (AELM) is trained to predict the spare channel outcome. The main objective is to reduce the Minimum Mean Square Error (MMSE) in the channel. Thus, the spare channel outcome is predicted automatically using the AELM. Then, diverse evaluations are executed in the suggested spare channel estimation approach over the different mechanisms to observe their effectualness rate.

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Enhancing Intelligent Transport Systems Through Decentralized Security Frameworks in Vehicle-to-Everything Networks

Tariq,

Ahanger

2025

WEVJ

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Vehicle Ad hoc Networks (VANETs) play an essential role in intelligent transportation systems (ITSs) by improving road safety and traffic management through robust decentralized communication between vehicles and infrastructure. Yet, decentralization introduces security vulnerabilities, including spoofing, tampering, and denial-of-service attacks, which can compromise the reliability and safety of vehicular communications. Traditional centralized security mechanisms are often inadequate in providing the real-time response and scalability required by such dispersed networks. This research promotes a shift toward distributed and real-time technologies, including blockchain and secure multi-party computation, to enhance communication integrity and privacy, ultimately strengthening system resilience by eliminating single points of failure. A core aspect of this study is the novel D-CASBR framework, which integrates three essential components. First, it employs hybrid machine learning methods, such as ElasticNet and Gradient Boosting, to facilitate real-time anomaly detection, identifying unusual activities as they occur. Second, it utilizes a consortium blockchain to provide secure and transparent information exchange among authorized participants. Third, it implements a fog-enabled reputation system that uses distributed fog computing to effectively manage trust within the network. This comprehensive approach addresses latency issues found in conventional systems while significantly improving the reliability and efficacy of threat detection, achieving 95 percent anomaly detection accuracy with minimal false positives. The result is a substantial advancement in securing vehicular networks.

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Second-Order Statistics-Aided Channel Estimation for Multipath Massive MIMO-OFDM Systems

Cited by 3 publications

References 43 publications

User Localization via Multiple Intelligent Reflecting Surfaces for LOS-Dominated Channels

User Localization via Multiple Intelligent Reflecting Surfaces for LOS-Dominated Channels

An Automated Sparse Channel Estimation Framework for MU‐MIMO‐OFDM System With Adaptive Extreme Learning and Heuristic Mechanism

Enhancing Intelligent Transport Systems Through Decentralized Security Frameworks in Vehicle-to-Everything Networks

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