4,115 research outputs found
Millimeter Wave MIMO Channel Estimation Based on Adaptive Compressed Sensing
Multiple-input multiple-output (MIMO) systems are well suited for
millimeter-wave (mmWave) wireless communications where large antenna arrays can
be integrated in small form factors due to tiny wavelengths, thereby providing
high array gains while supporting spatial multiplexing, beamforming, or antenna
diversity. It has been shown that mmWave channels exhibit sparsity due to the
limited number of dominant propagation paths, thus compressed sensing
techniques can be leveraged to conduct channel estimation at mmWave
frequencies. This paper presents a novel approach of constructing beamforming
dictionary matrices for sparse channel estimation using the continuous basis
pursuit (CBP) concept, and proposes two novel low-complexity algorithms to
exploit channel sparsity for adaptively estimating multipath channel parameters
in mmWave channels. We verify the performance of the proposed CBP-based
beamforming dictionary and the two algorithms using a simulator built upon a
three-dimensional mmWave statistical spatial channel model, NYUSIM, that is
based on real-world propagation measurements. Simulation results show that the
CBP-based dictionary offers substantially higher estimation accuracy and
greater spectral efficiency than the grid-based counterpart introduced by
previous researchers, and the algorithms proposed here render better
performance but require less computational effort compared with existing
algorithms.Comment: 7 pages, 5 figures, in 2017 IEEE International Conference on
Communications Workshop (ICCW), Paris, May 201
Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities
Recently there has been a flurry of research on the use of reconfigurable
intelligent surfaces (RIS) in wireless networks to create smart radio
environments. In a smart radio environment, surfaces are capable of
manipulating the propagation of incident electromagnetic waves in a
programmable manner to actively alter the channel realization, which turns the
wireless channel into a controllable system block that can be optimized to
improve overall system performance. In this article, we provide a tutorial
overview of reconfigurable intelligent surfaces (RIS) for wireless
communications. We describe the working principles of reconfigurable
intelligent surfaces (RIS) and elaborate on different candidate implementations
using metasurfaces and reflectarrays. We discuss the channel models suitable
for both implementations and examine the feasibility of obtaining accurate
channel estimates. Furthermore, we discuss the aspects that differentiate RIS
optimization from precoding for traditional MIMO arrays highlighting both the
arising challenges and the potential opportunities associated with this
emerging technology. Finally, we present numerical results to illustrate the
power of an RIS in shaping the key properties of a MIMO channel.Comment: to appear in the IEEE Transactions on Cognitive Communications and
Networking (TCCN
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