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Q T Zhang - One of the best experts on this subject based on the ideXlab platform.

  • channel estimation for ofdm systems over doubly selective channels a distributed compressive sensing based approach
    IEEE Transactions on Communications, 2013
    Co-Authors: Peng Cheng, Zhuo Chen, Yun Rui, Jay Y Guo, Lin Gui, Meixia Tao, Q T Zhang
    Abstract:

    Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system over a doubly selective channel is very challenging. This is mainly due to the significant Doppler shift, which results in a time-frequency doubly-selective (DS) channel. The DS channel features a large number of channel coefficients, which introduces inter-carrier interference (ICI) and forces the need for allocating a large number of Pilot Subcarriers. To tackle this problem, in this paper we propose a novel channel estimation scheme based on distributed compressive sensing (DCS) theory. Taking advantage of the basis expansion model (BEM) and the channel sparsity in the delay domain, we transform the original DS channel into a novel two-dimensional channel model, where several jointly sparse BEM coefficient vectors become the estimation goal. Then a special decoupling form originating from a novel sparse Pilot pattern is designed for such estimation, which results in an ICI-free structure and enables the DCS application to make joint estimation of these vectors accurately. Combined with a smoothing treatment process, the proposed scheme can achieve significantly higher estimation accuracy than the existing ones, although with a much smaller number of Pilot Subcarriers. Theoretical analysis and simulation results both confirm its performance merits.

Pucha-cabrera Christian - One of the best experts on this subject based on the ideXlab platform.

  • Estimador de canal basado en sensado compresivo y LDPC para OFDM usando SDR
    2020
    Co-Authors: Yanza-verdugo Anthony, Pucha-cabrera Christian, Inga-ortega Juan
    Abstract:

    Este trabajo propone la aplicación de un estimador de canal basado en sensado compresivo (CS, del inglés Compressive Sensing) sobre un sistema que usa multiplexación por división de frecuencias ortogonales (OFDM, del inglés Orthogonal Frequency Division Multiplexing) usando dispositivos de radio definido por software (SDR, del inglés Software Defined Radio). La aplicación de la teoría de CS se da a través del uso de algoritmos de reconstrucción dispersa como Orthogonal Matching Pursuit (OMP) y Compressive Sampling Matching Pursuit (CoSaMP) con el fin de aprovechar la naturaleza dispersa de las subportadoras Piloto usadas en OFDM optimizando el ancho de banda del sistema. Además, para mejorar el rendimiento de estos algoritmos, se utiliza el concepto de la matriz de comprobación de paridad dispersa que se implementa en el despliegue de códigos de comprobación de paridad de baja densidad (LDPC, del inglés Low Density Parity Check) para obtener una matriz de sensado que mejore la propiedad de restricción isométrica (RIP, del inglés Isometric Restriction Property) perteneciente al paradigma de CS. El documento muestra el modelo implementado en los equipos SDR analizando la tasa de error de bit y la cantidad de símbolos Piloto usados.//This work proposes the application of a channel estimator based on Compressive Sensing (CS) over a system that uses Orthogonal Frequency Division Multiplexing (OFDM) using Software Defined Radio (SDR) devices. The application of the CS theory is given through the use of sparse reconstruction algorithms such as Orthogonal Matching Pursuit (OMP) and Compressive Sampling Matching Pursuit (CoSaMP) in order to take advantage of the sparse nature of the Pilot Subcarriers used in OFDM, optimizing the bandwidth of system. In addition, to improve the performance of these algorithms, the sparse parity checking matrix concept is used, which is implemented in the deployment of low density parity check codes (LDPC) to obtain a sensing matrix that improves the isometric restriction property (RIP) belonging to the CS paradigm. The document shows the model implemented in the SDR equipment and analyze the bit error rate and the number of Pilot symbols used

  • Estimador de canal basado en sensado compresivo y LDPC para OFDM usando SDR
    'Salesian Polytechnic University of Ecuador', 2019
    Co-Authors: Inga Ortega, Juan Paúl, Yanza-verdugo Anthony, Pucha-cabrera Christian
    Abstract:

    This work proposes the application of a channel estimator based on Compressive Sensing (CS) over a system that uses Orthogonal Frequency Division Multiplexing (OFDM) using Software Defined Radio (SDR) devices. The application of the CS theory is given through the use of sparse reconstruction algorithms such as Orthogonal Matching Pursuit (OMP) and Compressive Sampling Matching Pursuit (CoSaMP) in order to take advantage of the sparse nature of the Pilot Subcarriers used in OFDM, optimizing the bandwidth of system. In addition, to improve the performance of these algorithms, the sparse parity checking matrix concept is used, which is implemented in the deployment of low density parity check codes (LDPC) to obtain a sensing matrix that improves the isometric restriction property (RIP) belonging to the CS paradigm. The document shows the model implemented in the SDR equipment and analyze the bit error rate and the number of Pilot symbols used.Este trabajo propone la aplicación de un estimador de canal basado en sensado compresivo (CS, del inglés Compressive Sensing) sobre un sistema que usa multiplexación por división de frecuencias ortogonales (OFDM, del inglés Orthogonal Frequency Division Multiplexing) usando dispositivos de radio definido por \emph{software} (SDR, del inglés Software Defined Radio). La aplicación de la teoría de CS se da a través del uso de algoritmos de reconstrucción dispersa como Orthogonal Matching Pursuit (OMP) y Compressive Sampling Matching Pursuit (CoSaMP) con el fin de aprovechar la naturaleza dispersa de las subportadoras Piloto usadas en OFDM optimizando el ancho de banda del sistema. Además, para mejorar el rendimiento de estos algoritmos, se utiliza el concepto de la matriz de comprobación de paridad dispersa que se implementa en el despliegue de códigos de comprobación de paridad de baja densidad (LDPC, del inglés Low Density Parity Check) para obtener una matriz de sensado que mejore la propiedad de restricción isométrica (RIP, del inglés Isometric Restriction Property) perteneciente al paradigma de CS. El documento muestra el modelo implementado en los equipos SDR analizando la tasa de error de bit y la cantidad de símbolos Piloto usados

Peng Cheng - One of the best experts on this subject based on the ideXlab platform.

  • channel estimation for ofdm systems over doubly selective channels a distributed compressive sensing based approach
    IEEE Transactions on Communications, 2013
    Co-Authors: Peng Cheng, Zhuo Chen, Yun Rui, Jay Y Guo, Lin Gui, Meixia Tao, Q T Zhang
    Abstract:

    Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system over a doubly selective channel is very challenging. This is mainly due to the significant Doppler shift, which results in a time-frequency doubly-selective (DS) channel. The DS channel features a large number of channel coefficients, which introduces inter-carrier interference (ICI) and forces the need for allocating a large number of Pilot Subcarriers. To tackle this problem, in this paper we propose a novel channel estimation scheme based on distributed compressive sensing (DCS) theory. Taking advantage of the basis expansion model (BEM) and the channel sparsity in the delay domain, we transform the original DS channel into a novel two-dimensional channel model, where several jointly sparse BEM coefficient vectors become the estimation goal. Then a special decoupling form originating from a novel sparse Pilot pattern is designed for such estimation, which results in an ICI-free structure and enables the DCS application to make joint estimation of these vectors accurately. Combined with a smoothing treatment process, the proposed scheme can achieve significantly higher estimation accuracy than the existing ones, although with a much smaller number of Pilot Subcarriers. Theoretical analysis and simulation results both confirm its performance merits.

Alexander Duncan - One of the best experts on this subject based on the ideXlab platform.

  • joint channel estimation and impulsive noise mitigation in underwater acoustic ofdm communication systems
    IEEE Transactions on Wireless Communications, 2017
    Co-Authors: Peng Chen, Yue Rong, Sven Nordholm, Alexander Duncan
    Abstract:

    Impulsive noise occurs frequently in underwater acoustic (UA) channels and can significantly degrade the performance of UA orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we propose two novel compressed sensing based algorithms for joint channel estimation and impulsive noise mitigation in UA OFDM systems. The first algorithm jointly estimates the channel impulse response and the impulsive noise by utilizing Pilot Subcarriers. The estimated impulsive noise is then converted to the time domain and removed from the received signals. We show that this algorithm reduces the system bit-error-rate through improved channel estimation and impulsive noise mitigation. In the second proposed algorithm, a joint estimation of the channel impulse response and the impulsive noise is performed by exploiting the initially detected data. Then, the estimated impulsive noise is removed from the received signals. The proposed algorithms are evaluated and compared with existing methods through numerical simulations and on real data collected during a UA communication experiment conducted in the estuary of the Swan River, WA, Australia, during December 2015. The results show that the proposed approaches consistently improve the accuracy of channel estimation and the performance of impulsive noise mitigation in UA OFDM communication systems.

Yanza-verdugo Anthony - One of the best experts on this subject based on the ideXlab platform.

  • Estimador de canal basado en sensado compresivo y LDPC para OFDM usando SDR
    2020
    Co-Authors: Yanza-verdugo Anthony, Pucha-cabrera Christian, Inga-ortega Juan
    Abstract:

    Este trabajo propone la aplicación de un estimador de canal basado en sensado compresivo (CS, del inglés Compressive Sensing) sobre un sistema que usa multiplexación por división de frecuencias ortogonales (OFDM, del inglés Orthogonal Frequency Division Multiplexing) usando dispositivos de radio definido por software (SDR, del inglés Software Defined Radio). La aplicación de la teoría de CS se da a través del uso de algoritmos de reconstrucción dispersa como Orthogonal Matching Pursuit (OMP) y Compressive Sampling Matching Pursuit (CoSaMP) con el fin de aprovechar la naturaleza dispersa de las subportadoras Piloto usadas en OFDM optimizando el ancho de banda del sistema. Además, para mejorar el rendimiento de estos algoritmos, se utiliza el concepto de la matriz de comprobación de paridad dispersa que se implementa en el despliegue de códigos de comprobación de paridad de baja densidad (LDPC, del inglés Low Density Parity Check) para obtener una matriz de sensado que mejore la propiedad de restricción isométrica (RIP, del inglés Isometric Restriction Property) perteneciente al paradigma de CS. El documento muestra el modelo implementado en los equipos SDR analizando la tasa de error de bit y la cantidad de símbolos Piloto usados.//This work proposes the application of a channel estimator based on Compressive Sensing (CS) over a system that uses Orthogonal Frequency Division Multiplexing (OFDM) using Software Defined Radio (SDR) devices. The application of the CS theory is given through the use of sparse reconstruction algorithms such as Orthogonal Matching Pursuit (OMP) and Compressive Sampling Matching Pursuit (CoSaMP) in order to take advantage of the sparse nature of the Pilot Subcarriers used in OFDM, optimizing the bandwidth of system. In addition, to improve the performance of these algorithms, the sparse parity checking matrix concept is used, which is implemented in the deployment of low density parity check codes (LDPC) to obtain a sensing matrix that improves the isometric restriction property (RIP) belonging to the CS paradigm. The document shows the model implemented in the SDR equipment and analyze the bit error rate and the number of Pilot symbols used

  • Estimador de canal basado en sensado compresivo y LDPC para OFDM usando SDR
    'Salesian Polytechnic University of Ecuador', 2019
    Co-Authors: Inga Ortega, Juan Paúl, Yanza-verdugo Anthony, Pucha-cabrera Christian
    Abstract:

    This work proposes the application of a channel estimator based on Compressive Sensing (CS) over a system that uses Orthogonal Frequency Division Multiplexing (OFDM) using Software Defined Radio (SDR) devices. The application of the CS theory is given through the use of sparse reconstruction algorithms such as Orthogonal Matching Pursuit (OMP) and Compressive Sampling Matching Pursuit (CoSaMP) in order to take advantage of the sparse nature of the Pilot Subcarriers used in OFDM, optimizing the bandwidth of system. In addition, to improve the performance of these algorithms, the sparse parity checking matrix concept is used, which is implemented in the deployment of low density parity check codes (LDPC) to obtain a sensing matrix that improves the isometric restriction property (RIP) belonging to the CS paradigm. The document shows the model implemented in the SDR equipment and analyze the bit error rate and the number of Pilot symbols used.Este trabajo propone la aplicación de un estimador de canal basado en sensado compresivo (CS, del inglés Compressive Sensing) sobre un sistema que usa multiplexación por división de frecuencias ortogonales (OFDM, del inglés Orthogonal Frequency Division Multiplexing) usando dispositivos de radio definido por \emph{software} (SDR, del inglés Software Defined Radio). La aplicación de la teoría de CS se da a través del uso de algoritmos de reconstrucción dispersa como Orthogonal Matching Pursuit (OMP) y Compressive Sampling Matching Pursuit (CoSaMP) con el fin de aprovechar la naturaleza dispersa de las subportadoras Piloto usadas en OFDM optimizando el ancho de banda del sistema. Además, para mejorar el rendimiento de estos algoritmos, se utiliza el concepto de la matriz de comprobación de paridad dispersa que se implementa en el despliegue de códigos de comprobación de paridad de baja densidad (LDPC, del inglés Low Density Parity Check) para obtener una matriz de sensado que mejore la propiedad de restricción isométrica (RIP, del inglés Isometric Restriction Property) perteneciente al paradigma de CS. El documento muestra el modelo implementado en los equipos SDR analizando la tasa de error de bit y la cantidad de símbolos Piloto usados