Intelligent Reflecting Surfaces

This slide describes the fundamentals of 6G communication based Intelligent Reflecting Surfaces

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 R eflecting S urfaces (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 L ack of control over the wireless channel High power consumption of the wireless interface.

4. 4 Intelligent Reflecting S urface 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 S urface 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 c hannel m odels and path loss scaling Energy-efficient c hannel s ensing, estimation and f eedback o verhead Spatial m odels for system-level a nalysis and optimization Integration of IRSs with emerging technologies Practical p rotocols for information e xchange 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 .  

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24. 22 Thank You Have a Nice Day