Sparsity

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Robert W Heath - One of the best experts on this subject based on the ideXlab platform.

  • channel estimation for orthogonal time frequency space otfs massive mimo
    IEEE Transactions on Signal Processing, 2019
    Co-Authors: Wenqian Shen, Linglong Dai, Pingzhi Fan, Robert W Heath
    Abstract:

    Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the large number of base station antennas. In this paper, we propose a 3D-structured orthogonal matching pursuit algorithm based channel estimation technique to solve this problem. First, we show that the OTFS MIMO channel exhibits 3D-structured Sparsity: normal Sparsity along the delay dimension, block Sparsity along the Doppler dimension, and burst Sparsity along the angle dimension. Based on the 3D-structured channel Sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed algorithm can achieve accurate channel state information with low pilot overhead.

  • channel estimation for orthogonal time frequency space otfs massive mimo
    International Conference on Communications, 2019
    Co-Authors: Wenqian Shen, Linglong Dai, Shuangfeng Han, I Chihlin, Robert W Heath
    Abstract:

    Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the required high pilot overhead. In this paper, we propose a 3D structured orthogonal matching pursuit (3D-SOMP) algorithm based channel estimation technique. First, we show that the OTFS MIMO channel exhibits 3D structured Sparsity: normal Sparsity along the delay dimension, block Sparsity along the Doppler dimension, and burst Sparsity along the angle dimension. Based on the 3D structured channel Sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed 3D-SOMP algorithm can achieve accurate channel state information with low pilot overhead.

Wenqian Shen - One of the best experts on this subject based on the ideXlab platform.

  • channel estimation for orthogonal time frequency space otfs massive mimo
    IEEE Transactions on Signal Processing, 2019
    Co-Authors: Wenqian Shen, Linglong Dai, Pingzhi Fan, Robert W Heath
    Abstract:

    Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the large number of base station antennas. In this paper, we propose a 3D-structured orthogonal matching pursuit algorithm based channel estimation technique to solve this problem. First, we show that the OTFS MIMO channel exhibits 3D-structured Sparsity: normal Sparsity along the delay dimension, block Sparsity along the Doppler dimension, and burst Sparsity along the angle dimension. Based on the 3D-structured channel Sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed algorithm can achieve accurate channel state information with low pilot overhead.

  • channel estimation for orthogonal time frequency space otfs massive mimo
    International Conference on Communications, 2019
    Co-Authors: Wenqian Shen, Linglong Dai, Shuangfeng Han, I Chihlin, Robert W Heath
    Abstract:

    Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the required high pilot overhead. In this paper, we propose a 3D structured orthogonal matching pursuit (3D-SOMP) algorithm based channel estimation technique. First, we show that the OTFS MIMO channel exhibits 3D structured Sparsity: normal Sparsity along the delay dimension, block Sparsity along the Doppler dimension, and burst Sparsity along the angle dimension. Based on the 3D structured channel Sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed 3D-SOMP algorithm can achieve accurate channel state information with low pilot overhead.

Linglong Dai - One of the best experts on this subject based on the ideXlab platform.

  • channel estimation for orthogonal time frequency space otfs massive mimo
    IEEE Transactions on Signal Processing, 2019
    Co-Authors: Wenqian Shen, Linglong Dai, Pingzhi Fan, Robert W Heath
    Abstract:

    Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the large number of base station antennas. In this paper, we propose a 3D-structured orthogonal matching pursuit algorithm based channel estimation technique to solve this problem. First, we show that the OTFS MIMO channel exhibits 3D-structured Sparsity: normal Sparsity along the delay dimension, block Sparsity along the Doppler dimension, and burst Sparsity along the angle dimension. Based on the 3D-structured channel Sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed algorithm can achieve accurate channel state information with low pilot overhead.

  • channel estimation for orthogonal time frequency space otfs massive mimo
    International Conference on Communications, 2019
    Co-Authors: Wenqian Shen, Linglong Dai, Shuangfeng Han, I Chihlin, Robert W Heath
    Abstract:

    Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the required high pilot overhead. In this paper, we propose a 3D structured orthogonal matching pursuit (3D-SOMP) algorithm based channel estimation technique. First, we show that the OTFS MIMO channel exhibits 3D structured Sparsity: normal Sparsity along the delay dimension, block Sparsity along the Doppler dimension, and burst Sparsity along the angle dimension. Based on the 3D structured channel Sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed 3D-SOMP algorithm can achieve accurate channel state information with low pilot overhead.

Pingzhi Fan - One of the best experts on this subject based on the ideXlab platform.

  • channel estimation for orthogonal time frequency space otfs massive mimo
    IEEE Transactions on Signal Processing, 2019
    Co-Authors: Wenqian Shen, Linglong Dai, Pingzhi Fan, Robert W Heath
    Abstract:

    Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the large number of base station antennas. In this paper, we propose a 3D-structured orthogonal matching pursuit algorithm based channel estimation technique to solve this problem. First, we show that the OTFS MIMO channel exhibits 3D-structured Sparsity: normal Sparsity along the delay dimension, block Sparsity along the Doppler dimension, and burst Sparsity along the angle dimension. Based on the 3D-structured channel Sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed algorithm can achieve accurate channel state information with low pilot overhead.

Al Swindlehurst - One of the best experts on this subject based on the ideXlab platform.

  • Secure Communication for Spatially Sparse Millimeter-Wave Massive MIMO Channels via Hybrid Precoding
    eScholarship University of California, 2020
    Co-Authors: Xu J, Xu W, Ng Dwk, Al Swindlehurst
    Abstract:

    © 2019 IEEE. In this paper, we investigate secure communication over sparse millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) channels by exploiting the spatial Sparsity of legitimate user's channel. We propose a secure communication scheme in which information data is precoded onto dominant angle components of the sparse channel through a limited number of radio-frequency (RF) chains, while artificial noise (AN) is broadcast over the remaining nondominant angles interfering only with the eavesdropper with a high probability. It is shown that the channel Sparsity plays a fundamental role analogous to secret keys in achieving secure communication. Hence, by defining two statistical measures of the channel Sparsity, we analytically characterize its impact on secrecy rate. In particular, a substantial improvement on secrecy rate can be obtained by the proposed scheme due to the uncertainty, i.e., 'entropy', introduced by the channel Sparsity which is unknown to the eavesdropper. It is revealed that Sparsity in the power domain can always contribute to the secrecy rate. In contrast, in the angle domain, there exists an optimal level of Sparsity that maximizes the secrecy rate. The effectiveness of the proposed scheme and derived results are verified by numerical simulations

  • Secure Communication for Spatially Sparse Millimeter-Wave Massive MIMO Channels via Hybrid Precoding
    eScholarship University of California, 2020
    Co-Authors: Xu J, Xu W, Ng Dwk, Al Swindlehurst
    Abstract:

    In this paper, we investigate secure communication over sparse millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) channels by exploiting the spatial Sparsity of legitimate user's channel. We propose a secure communication scheme in which information data is precoded onto dominant angle components of the sparse channel through a limited number of radio-frequency (RF) chains, while artificial noise (AN) is broadcast over the remaining nondominant angles interfering only with the eavesdropper with a high probability. It is shown that the channel Sparsity plays a fundamental role analogous to secret keys in achieving secure communication. Hence, by defining two statistical measures of the channel Sparsity, we analytically characterize its impact on secrecy rate. In particular, a substantial improvement on secrecy rate can be obtained by the proposed scheme due to the uncertainty, i.e., 'entropy', introduced by the channel Sparsity which is unknown to the eavesdropper. It is revealed that Sparsity in the power domain can always contribute to the secrecy rate. In contrast, in the angle domain, there exists an optimal level of Sparsity that maximizes the secrecy rate. The effectiveness of the proposed scheme and derived results are verified by numerical simulations

Sparsity - 15 days free trial to Access Experts & Abstracts