12 research outputs found
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
Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA
In this work, we examine an intelligent reflecting surface (IRS) assisted
downlink non-orthogonal multiple access (NOMA) scenario with the aim of
maximizing the sum rate of users. The optimization problem at the IRS is quite
complicated, and non-convex, since it requires the tuning of the phase shift
reflection matrix. Driven by the rising deployment of deep reinforcement
learning (DRL) techniques that are capable of coping with solving non-convex
optimization problems, we employ DRL to predict and optimally tune the IRS
phase shift matrices. Simulation results reveal that IRS assisted NOMA based on
our utilized DRL scheme achieves high sum rate compared to OMA based one, and
as the transmit power increases, the capability of serving more users
increases. Furthermore, results show that imperfect successive interference
cancellation (SIC) has a deleterious impact on the data rate of users
performing SIC. As the imperfection increases by ten times, the rate decreases
by more than 10%