76 research outputs found
Decision-Directed Hybrid RIS Channel Estimation with Minimal Pilot Overhead
To reap the benefits of reconfigurable intelligent surfaces (RIS), channel
state information (CSI) is generally required. However, CSI acquisition in RIS
systems is challenging and often results in very large pilot overhead,
especially in unstructured channel environments. Consequently, the RIS channel
estimation problem has attracted a lot of interest and also been a subject of
intense study in recent years. In this paper, we propose a decision-directed
RIS channel estimation framework for general unstructured channel models. The
employed RIS contains some hybrid elements that can simultaneously reflect and
sense the incoming signal. We show that with the help of the hybrid RIS
elements, it is possible to accurately recover the CSI with a pilot overhead
proportional to the number of users. Therefore, the proposed framework
substantially improves the system spectral efficiency compared to systems with
passive RIS arrays since the pilot overhead in passive RIS systems is
proportional to the number of RIS elements times the number of users. We also
perform a detailed spectral efficiency analysis for both the pilot-directed and
decision-directed frameworks. Our analysis takes into account both the channel
estimation and data detection errors at both the RIS and the BS. Finally, we
present numerous simulation results to verify the accuracy of the analysis as
well as to show the benefits of the proposed decision-directed framework.Comment: submitted for journal publication, 13 pages, 7 figure
Intelligent Reflecting Surfaces and Next Generation Wireless Systems
Intelligent reflecting surface (IRS) is a potential candidate for massive
multiple-input multiple-output (MIMO) 2.0 technology due to its low cost, ease
of deployment, energy efficiency and extended coverage. This chapter
investigates the slot-by-slot IRS reflection pattern design and two-timescale
reflection pattern design schemes, respectively. For the slot-by-slot
reflection optimization, we propose exploiting an IRS to improve the
propagation channel rank in mmWave massive MIMO systems without need to
increase the transmit power budget. Then, we analyze the impact of the
distributed IRS on the channel rank. To further reduce the heavy overhead of
channel training, channel state information (CSI) estimation, and feedback in
time-varying MIMO channels, we present a two-timescale reflection optimization
scheme, where the IRS is configured relatively infrequently based on
statistical CSI (S-CSI) and the active beamformers and power allocation are
updated based on quickly outdated instantaneous CSI (I-CSI) per slot. The
achievable average sum-rate (AASR) of the system is maximized without excessive
overhead of cascaded channel estimation. A recursive sampling particle swarm
optimization (PSO) algorithm is developed to optimize the large-timescale IRS
reflection pattern efficiently with reduced samplings of channel samples.Comment: To appear as a chapter of the book "Massive MIMO for Future Wireless
Communication Systems: Technology and Applications", to be published by
Wiley-IEEE Press. arXiv admin note: text overlap with arXiv:2206.0727
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems
This paper studies the estimation of cascaded channels in passive intelligent
reflective surface (IRS)- aided multiple-input multiple-output (MIMO) systems
employing hybrid precoders and combiners. We propose a low-complexity solution
that estimates the channel parameters progressively. The angles of departure
(AoDs) and angles of arrival (AoAs) at the transmitter and receiver,
respectively, are first estimated using inductive matrix completion (IMC)
followed by root-MUSIC based super-resolution spectrum estimation.
Forward-backward spatial smoothing (FBSS) is applied to address the coherence
issue. Using the estimated AoAs and AoDs, the training precoders and combiners
are then optimized and the angle differences between the AoAs and AoDs at the
IRS are estimated using the least squares (LS) method followed by FBSS and the
root-MUSIC algorithm. Finally, the composite path gains of the cascaded channel
are estimated using on-grid sparse recovery with a small-size dictionary. The
simulation results suggest that the proposed estimator can achieve improved
channel parameter estimation performance with lower complexity as compared to
several recently reported alternatives, thanks to the exploitation of the
knowledge of the array responses and low-rankness of the channel using
low-complexity algorithms at all the stages.Comment: Submitted to IEE
Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiuser Communication Systems Exploiting Statistical CSI of Correlated RIS-User Channels
Reconfigurable intelligent surface (RIS) is a promising candidate technology
for the upcoming Sixth Generation (6G) communication system for its ability to
manipulate the wireless communication environment by controlling the
coefficients of reflection elements (REs). However, since the RIS usually
consists of a large number of passive REs, the pilot overhead for channel
estimation in the RIS-aided system is prohibitively high. In this paper, the
channel estimation problem for a RIS-aided multi-user
multiple-input-single-output (MISO) communication system with clustered users
is investigated. First, to describe the correlated feature for RIS-user
channels, a beam domain channel model is developed for RIS-user channels. Then,
a pilot reuse strategy is put forward to reduce the pilot overhead and
decompose the channel estimation problem into several subproblems. Finally, by
leveraging the correlated nature of RIS-user channels, an eigenspace projection
(EP) algorithm is proposed to solve each subproblem respectively. Simulation
results show that the proposed EP channel estimation scheme can achieve
accurate channel estimation with lower pilot overhead than existing schemes.Comment: 10 pages, 11 figure
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