230 research outputs found
Exploiting Amplitude Control in Intelligent Reflecting Surface Aided Wireless Communication with Imperfect CSI
Intelligent reflecting surface (IRS) is a promising new paradigm to achieve
high spectral and energy efficiency for future wireless networks by
reconfiguring the wireless signal propagation via passive reflection. To reap
the potential gains of IRS, channel state information (CSI) is essential,
whereas channel estimation errors are inevitable in practice due to limited
channel training resources. In this paper, in order to optimize the performance
of IRS-aided multiuser systems with imperfect CSI, we propose to jointly design
the active transmit precoding at the access point (AP) and passive reflection
coefficients of IRS, each consisting of not only the conventional phase shift
and also the newly exploited amplitude variation. First, the achievable rate of
each user is derived assuming a practical IRS channel estimation method, which
shows that the interference due to CSI errors is intricately related to the AP
transmit precoders, the channel training power and the IRS reflection
coefficients during both channel training and data transmission. Then, for the
single-user case, by combining the benefits of the penalty method, Dinkelbach
method and block successive upper-bound minimization (BSUM) method, a new
penalized Dinkelbach-BSUM algorithm is proposed to optimize the IRS reflection
coefficients for maximizing the achievable data transmission rate subjected to
CSI errors; while for the multiuser case, a new penalty dual decomposition
(PDD)-based algorithm is proposed to maximize the users' weighted sum-rate.
Simulation results are presented to validate the effectiveness of our proposed
algorithms as compared to benchmark schemes. In particular, useful insights are
drawn to characterize the effect of IRS reflection amplitude control
(with/without the conventional phase shift) on the system performance under
imperfect CSI.Comment: 15 pages, 10 figures, accepted by IEEE Transactions on Communication
Outage-Constrained Robust Beamforming for Intelligent Reflecting Surface Aided Wireless Communication
In intelligent reflecting surface (IRS) aided wireless communication systems,
channel state information (CSI) is crucial to achieve its promising passive
beamforming gains. However, CSI errors are inevitable in practice and generally
correlated over the IRS reflecting elements due to the limited training with
discrete phase shifts, which degrade the data transmission rate and
reliability. In this paper, we focus on investigating the effect of CSI errors
to the outage performance in an IRS-aided multiuser downlink communication
system. Specifically, we aim to jointly optimize the active transmit precoding
vectors at the access point (AP) and passive discrete phase shifts at the IRS
to minimize the AP's transmit power, subject to the constraints on the maximum
CSI-error induced outage probability for the users. First, we consider the
single-user case and derive the user's outage probability in terms of the mean
signal power (MSP) and variance of the received signal at the user. Since there
is a trade-off in tuning these two parameters to minimize the outage
probability, we propose to maximize their weighted sum with the optimal weight
found by one-dimensional search. Then, for the general multiuser case, since
the users' outage probabilities are difficult to obtain in closed-form due to
the inter-user interference, we propose a novel constrained stochastic
successive convex approximation (CSSCA) algorithm, which replaces the
non-convex outage probability constraints with properly designed convex
surrogate approximations. Simulation results verify the effectiveness of the
proposed robust beamfoming algorithms and show their significant performance
improvement over various benchmark schemes.Comment: 15 pages, 14 figures, accepted for publication in IEEE Transactions
on Signal Processin
Two-timescale Beamforming Optimization for Intelligent Reflecting Surface Aided Multiuser Communication with QoS Constraints
Intelligent reflecting surface (IRS) is an emerging technology that is able
to reconfigure the wireless channel via tunable passive signal reflection and
thereby enhance the spectral and energy efficiency of wireless networks
cost-effectively. In this paper, we study an IRS-aided multiuser multiple-input
single-output (MISO) wireless system and adopt the two-timescale (TTS)
transmission to reduce the signal processing complexity and channel training
overhead as compared to the existing schemes based on the instantaneous channel
state information (I-CSI), and at the same time, exploit the multiuser channel
diversity in transmission scheduling. Specifically, the long-term passive
beamforming is designed based on the statistical CSI (S-CSI) of all links,
while the short-term active beamforming is designed to cater to the I-CSI of
all users' reconfigured channels with optimized IRS phase shifts. We aim to
minimize the average transmit power at the access point (AP), subject to the
users' individual quality of service (QoS) constraints. The formulated
stochastic optimization problem is non-convex and difficult to solve since the
long-term and short-term design variables are complicatedly coupled in the QoS
constraints. To tackle this problem, we propose an efficient algorithm, called
the primal-dual decomposition based TTS joint active and passive beamforming
(PDD-TJAPB), where the original problem is decomposed into a long-term problem
and a family of short-term problems, and the deep unfolding technique is
employed to extract gradient information from the short-term problems to
construct a convex surrogate problem for the long-term problem. The proposed
algorithm is proved to converge to a stationary solution of the original
problem almost surely. Simulation results are presented which demonstrate the
advantages and effectiveness of the proposed algorithm as compared to benchmark
schemes.Comment: 16 pages, 10 figures, accepted by IEEE Transactions on Wireless
communication
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning
The paper presents a joint beamforming algorithm using statistical channel
state information (S-CSI) for reconfigurable intelligent surfaces (RIS) for
multiuser MISO wireless communications. We used S-CSI, which is a long-term
average of the cascaded channel as opposed to instantaneous CSI utilized in
most existing works. Through this method, the overhead of channel estimation is
dramatically reduced. We propose a proximal policy optimization (PPO) algorithm
which is a well-known actor-critic based reinforcement learning (RL) algorithm
to solve the optimization problem. To test the efficacy of this algorithm,
simulation results are presented along with evaluations of key system
parameters, including the Rician factor and RIS location, on the achievable sum
rate of the users
Intelligent Reflecting Surface Enhanced Wireless Network: Two-timescale Beamforming Optimization
Intelligent reflecting surface (IRS) has drawn a lot of attention recently as
a promising new solution to achieve high spectral and energy efficiency for
future wireless networks. By utilizing massive low-cost passive reflecting
elements, the wireless propagation environment becomes controllable and thus
can be made favorable for improving the communication performance. Prior works
on IRS mainly rely on the instantaneous channel state information (I-CSI),
which, however, is practically difficult to obtain for IRS-associated links due
to its passive operation and large number of elements. To overcome this
difficulty, we propose in this paper a new two-timescale (TTS) transmission
protocol to maximize the achievable average sum-rate for an IRS-aided multiuser
system under the general correlated Rician channel model. Specifically, the
passive IRS phase-shifts are first optimized based on the statistical CSI
(S-CSI) of all links, which varies much slowly as compared to their I-CSI,
while the transmit beamforming/precoding vectors at the access point (AP) are
then designed to cater to the I-CSI of the users' effective channels with the
optimized IRS phase-shifts, thus significantly reducing the channel training
overhead and passive beamforming complexity over the existing schemes based on
the I-CSI of all channels. For the single-user case, a novel penalty dual
decomposition (PDD)-based algorithm is proposed, where the IRS phase-shifts are
updated in parallel to reduce the computational time. For the multiuser case,
we propose a general TTS optimization algorithm by constructing a quadratic
surrogate of the objective function, which cannot be explicitly expressed in
closed-form. Simulation results are presented to validate the effectiveness of
our proposed algorithms and evaluate the impact of S-CSI and channel
correlation on the system performance.Comment: 15 pages, 12 figures, accepted for publication in IEEE Transactions
on Wireless Communication
- …