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Block Partition

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Meng-lin Ku – 1st expert on this subject based on the ideXlab platform

  • Sparse Spectrum Sensing with Sub-Block Partition for Cognitive Radio Systems
    2014 IEEE 79th Vehicular Technology Conference (VTC Spring), 2014
    Co-Authors: Meng-lin Ku

    Abstract:

    Cognitive users are expected to be capable of exploring spectrum holes over a wide range of frequencies. Motivated by the sparse characteristic of underutilized spectrum, we consider sparse spectrum sensing using compressive sensing techniques for cognitive orthogonal frequency division multiplexing (OFDM) systems. The spectrum sensing problem is formulated as a multi-subcarrier detection problem, solved via the composite hypothesis testing and Neyman-Pearson criterion. Considering the availability of channel state information (CSI) at the cognitive device, two sparse spectrum sensing approaches are proposed for detecting the compressive received signals in time domain. For the purpose of complexity reduction, we further incorporate a sub-Block Partition scheme into the proposed approaches to leverage the spareness of the spectrum occupancy. The proposed approaches enable a flexible tradeoff between the implementation complexity and the sensing accuracy for wideband cognitive radios.

  • VTC Spring – Sparse Spectrum Sensing with Sub-Block Partition for Cognitive Radio Systems
    2014 IEEE 79th Vehicular Technology Conference (VTC Spring), 2014
    Co-Authors: Meng-lin Ku

    Abstract:

    Cognitive users are expected to be capable of exploring spectrum holes over a wide range of frequencies. Motivated by the sparse characteristic of underutilized spectrum, we consider sparse spectrum sensing using compressive sensing techniques for cognitive orthogonal frequency division multiplexing (OFDM) systems. The spectrum sensing problem is formulated as a multi-subcarrier detection problem, solved via the composite hypothesis testing and Neyman-Pearson criterion. Considering the availability of channel state information (CSI) at the cognitive device, two sparse spectrum sensing approaches are proposed for detecting the compressive received signals in time domain. For the purpose of complexity reduction, we further incorporate a sub-Block Partition scheme into the proposed approaches to leverage the spareness of the spectrum occupancy. The proposed approaches enable a flexible tradeoff between the implementation complexity and the sensing accuracy for wideband cognitive radios.

Zhilin Zhang – 2nd expert on this subject based on the ideXlab platform

  • Recovery of Block sparse signals using the framework of Block sparse Bayesian learning
    2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012
    Co-Authors: Zhilin Zhang

    Abstract:

    In this paper we study the recovery of Block sparse signals and extend conventional approaches in two important directions; one is learning and exploiting intra-Block correlation, and the other is generalizing signals’ Block structure such that the Block Partition is not needed to be known for recovery. We propose two algorithms based on the framework of Block sparse Bayesian learning (bSBL). One algorithm, directly derived from the framework, requires a priori knowledge of the Block Partition. Another algorithm, derived from an expanded bSBL framework using the generalization method, can be used when the Block Partition is unknown. Experiments show that they have superior performance to state-of-the-art algorithms.

Jian Song – 3rd expert on this subject based on the ideXlab platform

  • Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems
    IEEE Transactions on Communications, 2017
    Co-Authors: Fang Yang, Jian Song

    Abstract:

    Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, Block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The Block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical Block sparse NBI. A BSBL-based method, Partition estimated BSBL, is proposed. With the aid of the estimated Block Partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-Block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the Block Partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods.