144 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
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
When Meta-Surfaces Meet Users: Optimization of Smart Radio Environments in 6G Sub-THz Communications
We consider a smart radio environment where meta-surfaces are employed to
improve the performance of wireless networks working at sub-THz frequencies. To
this end, we propose a comprehensive mathematical channel model, taking into
account both the ability of the meta-surfaces to redirect the impinging signal
towards a desired direction, and the signal reflection due to large objects. We
show how the design of both the meta-surface and the transmitter precoder
influences the network throughput. Furthermore, we compare several algorithms
to optimize the effect of the meta-surfaces in a realistic scenario. As a
result, a simpler algorithm that associates network users and meta-surfaces
provides a performance comparable to more complex numerical optimization
methods. Simulation results suggest how many users are supported in the
designed system
Intelligent Reflecting Surface based Passive Information Transmission: A Symbol-Level Precoding Approach
Intelligent reflecting surfaces (IRS) have been proposed as a revolutionary
technology owing to its capability of adaptively reconfiguring the propagation
environment in a cost-effective and hardware-efficient fashion. While the
application of IRS as a passive reflector to enhance the performance of
wireless communications has been widely investigated in the literature, using
IRS as a passive transmitter recently is emerging as a new concept and
attracting steadily growing interest. In this paper, we propose two novel
IRS-based passive information transmission systems using advanced symbol-level
precoding. One is a standalone passive information transmission system, where
the IRS operates as a passive transmitter serving multiple receivers by
adjusting its elements to reflect unmodulated carrier signals. The other is a
joint passive reflection and information transmission system, where the IRS not
only enhances transmissions for multiple primary information receivers (PIRs)
by passive reflection, but also simultaneously delivers additional information
to a secondary information receiver (SIR) by embedding its information into the
primary signals at the symbol level. Two typical optimization problems, i.e.,
power minimization and quality-of-service (QoS) balancing, are investigated for
the proposed IRS-based passive information transmission systems. Simulation
results demonstrate the feasibility of IRS-based passive information
transmission and the effectiveness of our proposed algorithms, as compared to
other benchmark schemes.Comment: 14 pages, 11 figures, major revisio
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
Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks
Extremely large-scale multiple-input-multiple output (XL-MIMO) is a promising
technology to achieve high spectral efficiency (SE) and energy efficiency (EE)
in future wireless systems. The larger array aperture of XL-MIMO makes
communication scenarios closer to the near-field region. Therefore, near-field
resource allocation is essential in realizing the above key performance
indicators (KPIs). Moreover, the overall performance of XL-MIMO systems heavily
depends on the channel characteristics of the selected users, eliminating
interference between users through beamforming, power control, etc. The above
resource allocation issue constitutes a complex joint multi-objective
optimization problem since many variables and parameters must be optimized,
including the spatial degree of freedom, rate, power allocation, and
transmission technique. In this article, we review the basic properties of
near-field communications and focus on the corresponding "resource allocation"
problems. First, we identify available resources in near-field communication
systems and highlight their distinctions from far-field communications. Then,
we summarize optimization tools, such as numerical techniques and machine
learning methods, for addressing near-field resource allocation, emphasizing
their strengths and limitations. Finally, several important research directions
of near-field communications are pointed out for further investigation
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