This document provides an overview of intelligent reflecting surfaces (IRS) for wireless communications. It discusses the motivation for IRS to overcome limitations in wireless channels and power consumption. The working principle of IRS is described, where IRS reflect signals in a way that changes the phase and amplitude to control propagation. The architecture of IRS is explained, including its passive reflecting elements that can independently control reflection. Advantages like improved coverage and throughput are presented. Applications, challenges, and research directions are also summarized.

1. Intelligent Reflecting Surfaces
Course No. EE 6511
Advanced Wireless Communication
A. S. M. Jannatul Islam
1
Department of Electrical and Electronic Engineering
Khulna University of Engineering & Technology
Khulna-9203

2. 2
Contents
☼ Motivation
☼ Intelligent Reflecting Surfaces (IRS)
☼ Working Principle of IRS
☼ Architecture of IRS
☼ Advantages and Applications
☼ Limitations/Disadvantages
☼ Challenging Issues
☼ Research Directions

3. 3
Motivation
 A very large antenna arrays at each BS
 More focused energy
 More spatial multiplexing layers
 Better cellular throughput and coverage
 Large number of user served simultaneously
 Introduces two dimensional beamforming
Two main practical limitations
@ Lack of control over the wireless channel
@ High power consumption of the wireless interface.

4. 4
Intelligent Reflecting Surface
IRS is a new and revolutionizing technology that is able to significantly
improve the performance of wireless communication networks, by smartly
reconfiguring the wireless propagation environment with the use of
massive low-cost passive reflecting elements integrated on a planar
surface

5. 5
Intelligent Reflecting Surface
An IRS comprises an array of sub-wavelength IRS unit cells, each of which
can independently incur some change to the incident signal.
The change in general may be about the phase, amplitude, frequency, or
even polarization.

6. 6
Working Principle of IRS
The working principle of IRS is as per variation to Snell’s law. The input to
IRS is plane waves whereas the output is scattered waves whose phase
shifts are controlled to meet desired reflection.

7. 7
Working Principle of IRS
The EM (Electromagnetic) waves transmitted from BS are impinged on
IRS which produces induction current in the IRS.
IRS reflects these signals toward the users.
During reflection, IRS changes response by controlling phase and
amplitude. Phase shifts are controlled by PIN diodes used in IRS.

8. 8
Architecture of IRS
The hardware implementation of IRS is based on the concept of “metasurface”, which is made of
two-dimensional (2D) meta-material that is digitally controllable.
The metasurface is a planar array consisting of a large number of elements or so-called meta-atoms
with electrical thickness in the order of the subwavelength of the operating frequency of interest.
By properly designing the elements, including geometry shape (e.g., square or split-ring),
size/dimension, orientation, arrangement, etc., their individual signal response (reflection amplitude
and phase shift) can be modified accordingly.

9. 9
Architecture of IRS
The reflection coefficient of each element should be tunable to cater for dynamic wireless
channels arising from the user mobility, thus requiring reconfigurability in real time.
This can be achieved by leveraging electronic devices such as positive-intrinsic negative
(PIN) diodes, field-effect transistors (FETs), or microelectromechanical system (MEMS)
switches.

10. 10
Architecture of IRS
As shown in the figure, a typical architecture of IRS may consist of three layers and a smart
controller.
In the outer layer, a large number of metallic patches (elements) are printed on a dielectric substrate
to directly interact with incident signals.
Behind this layer, a copper plate is used to avoid the signal energy leakage.
Lastly, the inner layer is a control circuit board that is responsible for adjusting the reflection
amplitude/phase shift of each element, triggered by a smart controller attached to the IRS.

11. 11
Architecture of IRS
In practice, field-programmable gate array (FPGA) can be implemented as the controller, which also
acts as a gateway to communicate and coordinate with other network components (e.g., BSs, APs,
and user terminals) through separate wireless links for low-rate information exchange with them.

12. 12
Architecture of IRS
One example of an individual element’s structure is also shown in Fig. , where a PIN diode is
embedded in each element.
By controlling its biasing voltage via a direct-current (DC) feeding line, the PIN diode can be
switched between “On” and “Off” states as shown in the equivalent circuits, thereby generating a
phase-shift difference of π in rad.
As such, different phase shifts of IRS’s elements can be realized independently via setting the
corresponding biasing voltages by the smart controller.

13. 13
Architecture of IRS
On the other hand, to effectively control the reflection amplitude, variable resistor load can be
applied in the element design.
For example, by changing the values of resistors in each element, different portions of the incident
signal’s energy are dissipated, thus achieving controllable reflection amplitude in [0; 1].
In practice, it is desirable to have independent control of the amplitude and phase shift at each
element, for which the above circuits need to be efficiently integrated.

14. 14
Advantages of IRS
@ They are nearly passive, and, ideally, they do not need any dedicated
energy source.
@ They are viewed as a contiguous surface, and, ideally, any point can
shape the wave impinging upon it (soft programming).
@ They can be easily deployed, e.g., on the facades of buildings,
ceilings of factories and indoor spaces, human clothing, etc.
@ They have full-band response, since, ideally, they can work at any
operating frequency.
@ They are not affected by receiver noise, since, ideally, they do not
need analog-to-digital/digital-to-analog converters (ADCs and DACs),
and power amplifiers. As a result, they do not amplify nor introduce
noise when reflecting the signals and provide an inherently full
duplex transmission.
@ Overcoming Localized Coverage Holes
@ It reduces the EM Pollution

15. 15
Advantages of IRS
@ IRS is different from the active surface based massive MIMO due to
their different array architectures (passive versus active) and
operating mechanisms (reflect versus transmit).
@ It offers better beamforming gain compare to massive MIMO. The
received power is increased by factor 'N' in massive MIMO where as it
is increased by factor 'N2' in IRS.
@ It enhances spectrum efficiency by providing extra spatial diversity
gain.
@ It extends network coverage or Base Station coverage by serving cell
edge users or subscribers.
@ It improves energy efficiency as IRS does not require energy hungry
hardware.
@ It offers low energy consumption which is better than relay, massive
MIMO and backscatter technologies.

16. 16
Advantages of IRS
Two scenarios of IRS-assisted wireless communications. (a) IRS-assisted beamforming.
(b) IRS-assisted broadcasting.

17. 17
Advantages of IRS
A smart radio environment with multiple IRSs. User A is far away from the AP and suffers from low received
signal strength, while user B has amble received power but a low-rank ill-conditioned channel. The IRSs can be
optimized to help in both scenarios.

18. 18
Applications of IRS

19. 19
Limitations/Disadvantages of IRS
@ Once the metasurface is fabricated with a specific physical structure, it will
have fixed EM properties and therefore can be used for a specific purpose,
e.g., a perfect absorber operating at a certain frequency.
@ However, it becomes very inflexible as a new metasurface has to be re-
designed and fabricated to serve another purpose or operate at a different
frequency.
@ In particular, based on the application requirements, the structural
parameters of the scattering elements constituting the metasurface have to
be recalculated by a synthesis approach, which is in general computational
demanding.
@ It does not outperform relay. To make performance similar to relay, requires
metasurface with higher number of elements (~200).
@ RIS elements do not support digital processing capability as it is designed
based on concept of analog beamforming.

20. 20
Challenging Issues
☼ Experimentally-validated channel models and path loss scaling
☼ Energy-efficient channel sensing, estimation and feedback overhead
☼ Spatial models for system-level analysis and optimization
☼ Integration of IRSs with emerging technologies
☼ Practical protocols for information exchange
☼ Agile and light-weight phase reconfiguration
☼ Data-driven optimization

21. 21
Research Directions
☼ Experimentally-validated channel models and path loss model
☼ An IRS’s behavior depends on its physical materials and
manufacturing processes. Models taking these issues into
account can more accurately guide the optimization of IRSs for
aiding wireless communications.
☼ Scaling laws need to be established for a fundamental
understanding of the performance limits in IRS-aided
communications.
☼ Artificial neural network, Deep learning-based design can be
employed in IRS-aided communications to see the
performance.
☼ RF Sensing and Localization issues need to be examined.

22. 21
References
[1] Q.-U.-A. Nadeem, A. Kammoun, A. Chaaban, M. Debbah, and M.-S. Alouini, “Intelligent Reflecting
Surface Assisted Wireless Communication: Modeling and Channel Estimation,” Jun. 2019, Accessed:
Apr. 16, 2021. [Online]. Available: http://arxiv.org/abs/1906.02360.
[2] J. Zhao and Y. Liu, “A Survey of Intelligent Reflecting Surfaces (IRSs): Towards 6G Wireless
Communication Networks Secure and privacy in Al-IOT system View project Blockchain for Cyber-
Physical System and Wireless Communications View project A Survey of Intelligent Reflectin,” no. mm,
pp. 1–7, Jul. 2019, Accessed: Apr. 16, 2021. [Online]. Available:
https://www.researchgate.net/publication/334207323.
[3] W. Tang et al., “Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling
and Experimental Measurement,” IEEE Trans. Wirel. Commun., vol. 20, no. 1, pp. 421–439, Jan. 2021,
doi: 10.1109/TWC.2020.3024887.
[4] Q. Wu and R. Zhang, “Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface
Aided Wireless Network,” IEEE Commun. Mag., vol. 58, no. 1, pp. 106–112, Jan. 2020, doi:
10.1109/MCOM.001.1900107.
[5] E. Basar, M. Di Renzo, J. De Rosny, M. Debbah, M. S. Alouini, and R. Zhang, “Wireless
communications through reconfigurable intelligent surfaces,” IEEE Access, vol. 7, pp. 116753–116773,
2019, doi: 10.1109/ACCESS.2019.2935192.

23. 21
[6] S. Gong et al., “Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A
Contemporary Survey,” IEEE Commun. Surv. Tutorials, vol. 22, no. 4, pp. 2283–2314, Oct. 2020, doi:
10.1109/COMST.2020.3004197.
[7] M. A. Elmossallamy, H. Zhang, L. Song, K. G. Seddik, Z. Han, and G. Y. Li, “Reconfigurable Intelligent
Surfaces for Wireless Communications: Principles, Challenges, and Opportunities,” IEEE Trans. Cogn.
Commun. Netw., vol. 6, no. 3, pp. 990–1002, Sep. 2020, doi: 10.1109/TCCN.2020.2992604.
[8] W. Tang et al., “Wireless communications with programmable metasurface: Transceiver design and
experimental results,” arXiv. arXiv, Nov. 20, 2018, doi: 10.23919/j.cc.2019.05.004.
[9] A. M. Abdelhady, A. K. S. Salem, O. Amin, B. Shihada, and M.-S. Alouini, “Visible Light
Communications via Intelligent Reflecting Surfaces: Metasurfaces vs Mirror Arrays,” IEEE Open J.
Commun. Soc., vol. 2, pp. 1–20, Dec. 2020, doi: 10.1109/ojcoms.2020.3041930.
[10] Z. Chen, S. Member, C. Han, B. Ning, Z. Tian, and S. Li, “Intelligent Reflecting Surfaces Assisted
Terahertz Communications toward 6G,” Apr. 2021, Accessed: Apr. 16, 2021. [Online]. Available:
https://arxiv.org/abs/2104.02897v1.
References

24. 22