421 research outputs found
Joint Symbol-Level Precoding and Reflecting Designs for IRS-Enhanced MU-MISO Systems
Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms
Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication
The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink
multi-user communication from a multi-antenna base station is investigated in
this paper. We develop energy-efficient designs for both the transmit power
allocation and the phase shifts of the surface reflecting elements, subject to
individual link budget guarantees for the mobile users. This leads to
non-convex design optimization problems for which to tackle we propose two
computationally affordable approaches, capitalizing on alternating
maximization, gradient descent search, and sequential fractional programming.
Specifically, one algorithm employs gradient descent for obtaining the RIS
phase coefficients, and fractional programming for optimal transmit power
allocation. Instead, the second algorithm employs sequential fractional
programming for the optimization of the RIS phase shifts. In addition, a
realistic power consumption model for RIS-based systems is presented, and the
performance of the proposed methods is analyzed in a realistic outdoor
environment. In particular, our results show that the proposed RIS-based
resource allocation methods are able to provide up to higher energy
efficiency, in comparison with the use of regular multi-antenna
amplify-and-forward relaying.Comment: Accepted by IEEE TWC; additional materials on the topic are included
in the 2018 conference publications at ICASSP
(https://ieeexplore.ieee.org/abstract/document/8461496) and GLOBECOM 2018
(arXiv:1809.05397
Secrecy Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Antenna Communications
We investigate transmission optimization for intelligent reflecting surface
(IRS) assisted multi-antenna systems from the physical-layer security
perspective. The design goal is to maximize the system secrecy rate subject to
the source transmit power constraint and the unit modulus constraints imposed
on phase shifts at the IRS. To solve this complicated non-convex problem, we
develop an efficient alternating algorithm where the solutions to the transmit
covariance of the source and the phase shift matrix of the IRS are achieved in
closed form and semi-closed forms, respectively. The convergence of the
proposed algorithm is guaranteed theoretically. Simulations results validate
the performance advantage of the proposed optimized design
Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks
Intelligent reflecting surface (IRS) is a promising solution to build a
programmable wireless environment for future communication systems. In
practice, an IRS consists of massive low-cost elements, which can steer the
incident signal in fully customizable ways by passive beamforming. In this
paper, we consider an IRS-aided multiuser multiple-input single-output (MISO)
downlink communication system. In particular, the weighted sum-rate of all
users is maximized by joint optimizing the active beamforming at the
base-station (BS) and the passive beamforming at the IRS. In addition, we
consider a practical IRS assumption, in which the passive elements can only
shift the incident signal to discrete phase levels. This non-convex problem is
firstly decoupled via Lagrangian dual transform, and then the active and
passive beamforming can be optimized alternatingly. The active beamforming at
BS is optimized based on the fractional programming method. Then, three
efficient algorithms with closed-form expressions are proposed for the passive
beamforming at IRS. Simulation results have verified the effectiveness of the
proposed algorithms as compared to different benchmark schemes.Comment: 13 pages, 8 figure
Parallel Factor Decomposition Channel Estimation in RIS-Assisted Multi-User MISO Communication
Reconfigurable Intelligent Surfaces (RISs) have been recently considered as
an energy-efficient solution for future wireless networks due to their fast and
low power configuration enabling massive connectivity and low latency
communications. Channel estimation in RIS-based systems is one of the most
critical challenges due to the large number of reflecting unit elements and
their distinctive hardware constraints. In this paper, we focus on the downlink
of a RIS-assisted multi-user Multiple Input Single Output (MISO) communication
system and present a method based on the PARAllel FACtor (PARAFAC)
decomposition to unfold the resulting cascaded channel model. The proposed
method includes an alternating least squares algorithm to iteratively estimate
the channel between the base station and RIS, as well as the channels between
RIS and users. Our selective simulation results show that the proposed
iterative channel estimation method outperforms a benchmark scheme using
genie-aided information. We also provide insights on the impact of different
RIS settings on the proposed algorithm.Comment: This work is already submitted to 2020 SAM conferenc
Reconfigurable Intelligent Surfaces: Principles and Opportunities
Reconfigurable intelligent surfaces (RISs), also known as intelligent
reflecting surfaces (IRSs), or large intelligent surfaces (LISs), have received
significant attention for their potential to enhance the capacity and coverage
of wireless networks by smartly reconfiguring the wireless propagation
environment. Therefore, RISs are considered a promising technology for the
sixth-generation (6G) of communication networks. In this context, we provide a
comprehensive overview of the state-of-the-art on RISs, with focus on their
operating principles, performance evaluation, beamforming design and resource
management, applications of machine learning to RIS-enhanced wireless networks,
as well as the integration of RISs with other emerging technologies. We
describe the basic principles of RISs both from physics and communications
perspectives, based on which we present performance evaluation of multi-antenna
assisted RIS systems. In addition, we systematically survey existing designs
for RIS-enhanced wireless networks encompassing performance analysis,
information theory, and performance optimization perspectives. Furthermore, we
survey existing research contributions that apply machine learning for tackling
challenges in dynamic scenarios, such as random fluctuations of wireless
channels and user mobility in RIS-enhanced wireless networks. Last but not
least, we identify major issues and research opportunities associated with the
integration of RISs and other emerging technologies for application to
next-generation networks.Comment: 66 pages, 18 figures, 8 table
Joint Symbol-Level Precoding and Reflecting Designs for IRS-Enhanced MU-MISO Systems
Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary
solution to enhance wireless communications by changing propagation environment
in a cost-effective and hardware-efficient fashion. In addition, symbol-level
precoding (SLP) has attracted considerable attention recently due to its
advantages in converting multiuser interference (MUI) into useful signal
energy. Therefore, it is of interest to investigate the employment of IRS in
symbol-level precoding systems to exploit MUI in a more effective way by
manipulating the multiuser channels. In this paper, we focus on joint
symbol-level precoding and reflecting designs in IRS-enhanced multiuser
multiple-input single-output (MU-MISO) systems. Both power minimization and
quality-of-service (QoS) balancing problems are considered. In order to solve
the joint optimization problems, we develop an efficient iterative algorithm to
decompose them into separate symbol-level precoding and block-level reflecting
design problems. An efficient gradient-projection-based algorithm is utilized
to design the symbol-level precoding and a Riemannian conjugate gradient
(RCG)-based algorithm is employed to solve the reflecting design problem.
Simulation results demonstrate the significant performance improvement
introduced by the IRS and illustrate the effectiveness of our proposed
algorithms.Comment: 13 pages, 13 figures, published on TW
Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial
Intelligent reflecting surface (IRS) is an enabling technology to engineer
the radio signal prorogation in wireless networks. By smartly tuning the signal
reflection via a large number of low-cost passive reflecting elements, IRS is
capable of dynamically altering wireless channels to enhance the communication
performance. It is thus expected that the new IRS-aided hybrid wireless network
comprising both active and passive components will be highly promising to
achieve a sustainable capacity growth cost-effectively in the future. Despite
its great potential, IRS faces new challenges to be efficiently integrated into
wireless networks, such as reflection optimization, channel estimation, and
deployment from communication design perspectives. In this paper, we provide a
tutorial overview of IRS-aided wireless communication to address the above
issues, and elaborate its reflection and channel models, hardware architecture
and practical constraints, as well as various appealing applications in
wireless networks. Moreover, we highlight important directions worthy of
further investigation in future work.Comment: IEEE TCOM EIC Invited Paper.A tutorial paper on IR
IRS-Enhanced Wideband MU-MISO-OFDM Communication Systems
Intelligent reflecting surface (IRS) is considered as an enabling technology
for future wireless communication systems since it can intelligently change the
wireless environment to improve the communication performance. In this paper,
an IRS-enhanced wideband multiuser multi-input single-output orthogonal
frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim
to jointly design the transmit beamformer and the reflection of IRS to maximize
the average sum-rate over all subcarriers. With the aid of the relationship
between sum-rate maximization and mean square error (MSE) minimization, an
efficient joint beamformer and IRS design algorithm is developed. Simulation
results illustrate that the proposed algorithm can offer significant average
sum-rate enhancement, which confirms the effectiveness of the use of the IRS
for wideband wireless communication systems.Comment: 6 pages, 5 figures, submit to WCNC 202
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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