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Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO

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arxiv 1905.07948 v2 pith:SZKFU3KF submitted 2019-05-20 cs.IT eess.SPmath.IT

classification cs.ITeess.SPmath.IT
keywords channelestimationlargemassivemimoassistedcascadedintelligent
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In this letter, we consider the problem of channel estimation for large intelligent metasurface (LIM) assisted massive multiple-input multiple-output (MIMO) systems. The main challenge of this problem is that the LIM integrated with a large number of low-cost metamaterial antennas can only passively reflect the incident signal by a certain phase shift, and does not have any signal processing capability. To deal with this, we introduce a general framework for the estimation of the transmitter-LIM and LIM-receiver cascaded channel, and propose a two-stage algorithm that includes a sparse matrix factorization stage and a matrix completion stage. Simulation results illustrate that the proposed method can achieve accurate channel estimation for LIM-assisted massive MIMO systems.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Intelligent Reflecting Surface Aided MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer

    eess.SP 2019-08 conditional novelty 5.0 of 10

    Joint optimization of BS precoding and IRS phase shifts for weighted sum rate maximization in SWIPT MIMO systems is solved by a convergent BCD algorithm, with simulations showing IRS expands the energy-harvesting range.

  2. Reconfigurable Intelligent Surface Assisted UAV Communication: Joint Trajectory Design and Passive Beamforming

    cs.IT 2019-08 conditional novelty 5.0 of 10

    A UAV and a reconfigurable intelligent surface can be jointly optimized, with a closed-form phase alignment and a successive-convex-approximation trajectory design, to raise the average downlink rate above trajectory-...

  3. Optimizations with Intelligent Reflecting Surfaces (IRSs) in 6G Wireless Networks: Power Control, Quality of Service, Max-Min Fair Beamforming for Unicast, Broadcast, and Multicast with Multi-antenna Mobile Users and Multiple IRSs

    cs.IT 2019-08 conditional novelty 4.0 of 10

    The paper extends IRS beamforming optimization to broadcast, multicast, max-min fairness, and multi-IRS/multi-antenna settings using standard SDR alternating optimization, without numerical validation.

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