5 research outputs found
Channel Estimation for Wireless Communication Systems Assisted by Large Intelligent Surfaces
In this letter, the channel estimation problem is studied for wireless
communication systems assisted by large intelligent surface. Due to features of
assistant channel, channel estimation (CE) problem for the investigated system
is shown as a constrained estimation error minimization problem, which differs
from traditional CE problems. A Lagrange multiplier and dual ascent-based
estimation scheme is then designed to obtain a closed-form solution for the
estimator iteratively. Moreover, the Cramer-Rao lower bounds are deduced for
performance evaluation. Simulation results show that the designed scheme could
improve estimation accuracy up to 18%, compared with least square method in low
signal-to-noise ratio regime.Comment: 4 pages, 3 figures, journa
Channel Estimation for Intelligent Reflecting Surface Assisted Wireless Communications
In this paper, the minimum mean square error (MMSE) channel estimation for
intelligent reflecting surface (IRS) assisted wireless communication systems is
investigated. In the considered setting, each row vector of the equivalent
channel matrix from the base station (BS) to the users is shown to be Bessel
distributed, and all these row vectors are independent of each other. By
introducing a Gaussian scale mixture model, we obtain a closed-form expression
for the MMSE estimate of the equivalent channel, and determine analytical upper
and lower bounds on the mean square error. Using the central limit theorem, we
conduct an asymptotic analysis of the MMSE estimate, and show that the upper
bound on the mean square error of the MMSE estimate is equal to the asymptotic
mean square error of the MMSE estimation when the number of reflecting elements
at the IRS tends to infinity. Numerical simulations show that the gap between
the upper and lower bounds are very small, and they almost overlap with each
other at medium signal-to-noise ratio (SNR) levels and moderate number of
elements at the IRS.Comment: 6 pages, 4 figures, conference pape
Beyond Max-SNR: Joint Encoding for Reconfigurable Intelligent Surfaces
A communication link aided by a Reconfigurable Intelligent Surface (RIS) is
studied, in which the transmitter can control the state of the RIS via a
finite-rate control link. Prior work mostly assumed a fixed RIS configuration
irrespective of the transmitted information. In contrast, this work derives
information-theoretic limits, and demonstrates that the capacity is achieved by
a scheme that jointly encodes information in the transmitted signal as well as
in the RIS configuration. In addition, a novel signaling strategy based on
layered encoding is proposed that enables practical successive
cancellation-type decoding at the receiver. Numerical experiments demonstrate
that the standard max-SNR scheme that fixes the configuration of the RIS as to
maximize the Signal-to-Noise Ratio (SNR) at the receiver is strictly
suboptimal, and is outperformed by the proposed strategies at all practical SNR
levels.Comment: To be submitted for conference publicatio
Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications: Framework, Algorithms, and Analysis
In intelligent reflecting surface (IRS) assisted communication systems, the
acquisition of channel state information (CSI) is a crucial impediment for
achieving the beamforming gain of IRS because of the considerable overhead
required for channel estimation. Specifically, under the current beamforming
design for IRS-assisted communications, channel coefficients should be
estimated, where , and denote the numbers of users, IRS reflecting
elements, and antennas at the base station (BS), respectively. To accurately
estimate such a large number of channel coefficients within a short time
interval, we propose a novel three-phase pilot-based channel estimation
framework in this paper for IRS-assisted uplink multiuser communications. Under
this framework, we analytically prove that a time duration consisting of
pilot symbols is sufficient for the BS
to perfectly recover all the channel coefficients for the case without
receiver noise at the BS. In contrast to the channel estimation for
conventional uplink communications without IRS where the minimum channel
estimation time is independent of the number of receive antennas at the BS, our
result reveals the crucial role of massive MIMO (multiple-input
multiple-output) in reducing the channel estimation time for IRS-assisted
communications. Further, for the case with receiver noise, the user pilot
sequences, IRS reflecting coefficients, and BS linear minimum mean-squared
error (LMMSE) channel estimators are characterized in closed-form, and the
corresponding estimation mean-squared error (MSE) is quantified.Comment: accepted by IEEE Transactions on Wireless Communications. arXiv admin
note: substantial text overlap with arXiv:1911.0308
Channel Estimation for IRS-aided Multiuser Communications with Reduced Error Propagation
Intelligent reflecting surface (IRS) has emerged as a promising paradigm to
improve the capacity and reliability of a wireless communication system by
smartly reconfiguring the wireless propagation environment. To achieve the
promising gains of IRS, the acquisition of the channel state information (CSI)
is essential, which however is practically difficult since the IRS does not
employ any transmit/receive radio frequency (RF) chains in general and it has
limited signal processing capability. In this paper, we study the uplink
channel estimation problem for an IRS-aided multiuser single-input multi-output
(SIMO) system, and propose a novel two-phase channel estimation (2PCE) strategy
which can alleviate the negative effects caused by error propagation in the
existing three-phase channel estimation approach, i.e., the channel estimation
errors in previous phases will deteriorate the estimation performance in later
phases, and enhance the channel estimation performance with the same amount of
channel training overhead as in the existing approach. Moreover, the asymptotic
mean squared error (MSE) of the 2PCE strategy is analyzed when the least-square
(LS) channel estimation method is employed, and we show that the 2PCE strategy
can outperform the existing approach. Finally, extensive simulation results are
presented to validate the effectiveness of the 2PCE strategy