The Experts below are selected from a list of 204 Experts worldwide ranked by ideXlab platform

Mounir Ghogho - One of the best experts on this subject based on the ideXlab platform.

  • frame training sequence synchronization and dc offset removal for data dependent superimposed training based channel estimation
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: E Alamedahernandez, Des Mclernon, A G Orozcolugo, M M Lara, Mounir Ghogho
    Abstract:

    Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-Bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-Bearing Signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity

  • frame training sequence synchronization and dc offset removal for data dependent superimposed training based channel estimation
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: E Alamedahernandez, Des Mclernon, A G Orozcolugo, M M Lara, Mounir Ghogho
    Abstract:

    Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-Bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-Bearing Signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity

E Alamedahernandez - One of the best experts on this subject based on the ideXlab platform.

  • frame training sequence synchronization and dc offset removal for data dependent superimposed training based channel estimation
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: E Alamedahernandez, Des Mclernon, A G Orozcolugo, M M Lara, Mounir Ghogho
    Abstract:

    Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-Bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-Bearing Signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity

  • frame training sequence synchronization and dc offset removal for data dependent superimposed training based channel estimation
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: E Alamedahernandez, Des Mclernon, A G Orozcolugo, M M Lara, Mounir Ghogho
    Abstract:

    Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-Bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-Bearing Signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity

Xiangyun Zhou - One of the best experts on this subject based on the ideXlab platform.

  • secure communication with a wireless powered friendly jammer
    IEEE Transactions on Wireless Communications, 2016
    Co-Authors: Wanchun Liu, Xiangyun Zhou, Salman Durrani, Petar Popovski
    Abstract:

    In this paper, we propose using a wireless-powered friendly jammer to enable secure communication between a source node and destination node, in the presence of an eavesdropper. We consider a two-phase communication protocol with fixed-rate transmission. In the first phase, wireless power transfer is conducted from the source to the jammer. In the second phase, the source transmits the information-Bearing Signal under the protection of a jamming Signal sent by the jammer using the harvested energy in the first phase. We analytically characterize the long-term behavior of the proposed protocol and derive a closed-form expression for the throughput. We further optimize the rate parameters for maximizing the throughput subject to a secrecy outage probability constraint. Our analytical results show that the throughput performance differs significantly between the single-antenna jammer case and the multiantenna jammer case. For instance, as the source transmit power increases, the throughput quickly reaches an upper bound with single-antenna jammer, while the throughput grows unbounded with multiantenna jammer. Our numerical results also validate the derived analytical results.

  • secure communication with a wireless powered friendly jammer
    arXiv: Information Theory, 2014
    Co-Authors: Wanchun Liu, Xiangyun Zhou, Salman Durrani, Petar Popovski
    Abstract:

    In this paper, we propose to use a wireless-powered friendly jammer to enable secure communication between a source node and destination node, in the presence of an eavesdropper. We consider a two-phase communication protocol with fixed-rate transmission. In the first phase, wireless power transfer is conducted from the source to the jammer. In the second phase, the source transmits the information-Bearing Signal under the protection of a jamming Signal sent by the jammer using the harvested energy in the first phase. We analytically characterize the long-time behavior of the proposed protocol and derive a closed-form expression for the throughput. We further optimize the rate parameters for maximizing the throughput subject to a secrecy outage probability constraint. Our analytical results show that the throughput performance differs significantly between the single-antenna jammer case and the multi-antenna jammer case. For instance, as the source transmit power increases, the throughput quickly reaches an upper bound with single-antenna jammer, while the throughput grows unbounded with multi-antenna jammer. Our numerical results also validate the derived analytical results.

  • secure transmission with artificial noise over fading channels achievable rate and optimal power allocation
    IEEE Transactions on Vehicular Technology, 2010
    Co-Authors: Xiangyun Zhou, Matthew R. Mckay
    Abstract:

    We consider the problem of secure communication with multiantenna transmission in fading channels. The transmitter simultaneously transmits an information-Bearing Signal to the intended receiver and artificial noise to the eavesdroppers. We obtain an analytical closed-form expression of an achievable secrecy rate and use it as the objective function to optimize the transmit power allocation between the information Signal and the artificial noise. Our analytical and numerical results show that equal power allocation is a simple yet near-optimal strategy for the case of noncolluding eavesdroppers. When the number of colluding eavesdroppers increases, more power should be used to generate the artificial noise. We also provide an upper bound on the SNR, above which, the achievable secrecy rate is positive and shows that the bound is tight at low SNR. Furthermore, we consider the impact of imperfect channel state information (CSI) at both the transmitter and the receiver and find that it is wise to create more artificial noise to confuse the eavesdroppers than to increase the Signal strength for the intended receiver if the CSI is not accurately obtained.

  • secure transmission with artificial noise over fading channels achievable rate and optimal power allocation
    arXiv: Information Theory, 2010
    Co-Authors: Xiangyun Zhou, Matthew R. Mckay
    Abstract:

    We consider the problem of secure communication with multi-antenna transmission in fading channels. The transmitter simultaneously transmits an information Bearing Signal to the intended receiver and artificial noise to the eavesdroppers. We obtain an analytical closed-form expression of an achievable secrecy rate, and use it as the objective function to optimize the transmit power allocation between the information Signal and the artificial noise. Our analytical and numerical results show that equal power allocation is a simple yet near optimal strategy for the case of non-colluding eavesdroppers. When the number of colluding eavesdroppers increases, more power should be used to generate the artificial noise. We also provide an upper bound on the Signal-to-noise ratio (SNR) above which the achievable secrecy rate is positive and show that the bound is tight at low SNR. Furthermore, we consider the impact of imperfect channel state information (CSI) at both the transmitter and the receiver and find that it is wise to create more artificial noise to confuse the eavesdroppers than to increase the Signal strength for the intended receiver if the CSI is not accurately obtained.

  • Physical layer security with artificial noise: Secrecy capacity and optimal power allocation
    3rd International Conference on Signal Processing and Communication Systems, ICSPCS'2009 - Proceedings, 2009
    Co-Authors: Xiangyun Zhou, Matthew R. Mckay
    Abstract:

    We consider the problem of secure communication in wireless fading channels in the presence of non-colluding passive eavesdroppers. The transmitter has multiple antennas and is able to simultaneously transmit an information Bearing Signal to the intended receiver and artificial noise to the eavesdroppers. We obtain an analytical closed-form lower bound for secrecy capacity, which is used as the objective function to optimize transmit power allocation between the information Signal and the artificial noise. Our analytical and numerical results show that equal power allocation is a simple and generic strategy which achieves near optimal capacity performance. We also find that adaptive power allocation based on each channel realization provides no or insignificant capacity improvement over equal power allocation.

M M Lara - One of the best experts on this subject based on the ideXlab platform.

  • frame training sequence synchronization and dc offset removal for data dependent superimposed training based channel estimation
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: E Alamedahernandez, Des Mclernon, A G Orozcolugo, M M Lara, Mounir Ghogho
    Abstract:

    Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-Bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-Bearing Signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity

  • frame training sequence synchronization and dc offset removal for data dependent superimposed training based channel estimation
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: E Alamedahernandez, Des Mclernon, A G Orozcolugo, M M Lara, Mounir Ghogho
    Abstract:

    Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-Bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-Bearing Signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity

Des Mclernon - One of the best experts on this subject based on the ideXlab platform.

  • frame training sequence synchronization and dc offset removal for data dependent superimposed training based channel estimation
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: E Alamedahernandez, Des Mclernon, A G Orozcolugo, M M Lara, Mounir Ghogho
    Abstract:

    Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-Bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-Bearing Signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity

  • frame training sequence synchronization and dc offset removal for data dependent superimposed training based channel estimation
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: E Alamedahernandez, Des Mclernon, A G Orozcolugo, M M Lara, Mounir Ghogho
    Abstract:

    Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-Bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-Bearing Signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity