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Sparse signal processing has extensively been used in various fields of communications. Most of the communicational signals possess the property of being sparse in some domain which can be leveraged to process them more efficiently and accurately. Using the modern techniques of sparse signal processing, we can make a great progress in the communication areas such as: sparse channel estimation, compressive spectrum sensing and wireless parameter estimation, distributed networks, smart antennas and MIMO systems, wireless sensor networks, radar systems, cognitive radio, smart green. This symposium on “ sparse signal processing for communications” aims to discuss some of the recent advances in this area.

征稿信息

重要日期

2016-06-22
初稿截稿日期
2016-09-30
终稿截稿日期

征稿范围

  • Sparsity for Smart antennas, MIMO systems, large scale MIMO, channel estimation, power allocation and beam forming

  • Sparse signal processing for Big data applications and distributed networks

  • Sparsity and compressive sensing in co-located/distributed radars

  • Applications of Statistical sparsity models and algorithms (such as Bayesian, likelihood-based, entropy and variational Bayes) in communications

  • Compressive sensing and learning in communications and wireless networks

  • Sparse network theory and analysis, including dynamic (time-varying) networks and large networks

  • Compressed sensing in cognitive radio, spectrum estimation, Ultra-wideband radio

  • Compressive Sensing in Wireless Sensor Networks, energy harvesting, and green communications

  • Sparsity-based techniques for inverse problems in different fields such as Microwave Imaging and Magnetic Resonance Imaging systems

  • Sparsity for signal sampling, data compression and Analog to Digital converters

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重要日期
  • 会议日期

    12月07日

    2016

    12月09日

    2016

  • 06月22日 2016

    初稿截稿日期

  • 09月30日 2016

    终稿截稿日期

  • 12月09日 2016

    注册截止日期

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IEEE
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