Skip to main content
SpringerLink
Log in

Reconfigurable intelligent surfaces for smart wireless environments: channel estimation, system design and applications in 6G networks

  • Review
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Reconfigurable intelligent surface (RIS), one of the key enablers for the sixth-generation (6G) mobile communication networks, is considered by designers to smartly reconfigure the wireless propagation environment in a controllable and programmable manner. Specifically, an RIS consists of a large number of low-cost and passive reflective elements (REs) without radio frequency chains. The system gain of RIS wireless systems can be achieved by adjusting the phase shifts and amplitudes of the REs so that the desired signals can be added constructively at the receiver. However, an RIS typically has limited signal processing capability and cannot perform active transmitting/receiving in general, which leads to new challenges in the physical layer design of RIS wireless systems. In this paper, we provide an overview of the RIS-aided wireless systems, including the reflection principle, channel estimation, and system design. In particular, two types of emerging RIS systems are considered: RIS-aided wireless communications (RAWC) and RIS-based information transmission (RBIT), where the RIS plays the role of the reflector and the transmitter, respectively. We also envision the potential applications of RIS in 6G networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. You X H, Wang C X, Huang J, et al. Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts. Sci China Inf Sci, 2021, 64: 110301

    Article  Google Scholar 

  2. Zhang Z Q, Xiao Y, Ma Z, et al. 6G wireless networks: vision, requirements, architecture, and key technologies. IEEE Veh Technol Mag, 2019, 14: 28–41

    Article  Google Scholar 

  3. Zhang L, Liang Y C, Niyato D. 6G visions: mobile ultra-broadband, super Internet-of-Things, and artificial intelligence. China Commun, 2019, 16: 1–14

    Google Scholar 

  4. Liang Y C. Dynamic Spectrum Management: from Cognitive Radio to Blockchain and Artificial Intelligence. Berlin: Springer, 2020

    Book  Google Scholar 

  5. Liang Y C, Long R Z, Zhang Q Q, et al. Large intelligent surface/antennas (LISA): making reflective radios smart. J Commun Inf Netw, 2019, 4: 40–50

    Article  Google Scholar 

  6. Dai L L, Wang B C, Wang M, et al. Reconfigurable intelligent surface-based wireless communications: antenna design, prototyping, and experimental results. IEEE Access, 2020, 8: 45913–45923

    Article  Google Scholar 

  7. Wu Q Q, Zhang R. Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Commun Mag, 2020, 58: 106–112

    Article  Google Scholar 

  8. Gong S, Lu X, Hoang D T, et al. Toward smart wireless communications via intelligent reflecting surfaces: a contemporary survey. IEEE Commun Surv Tut, 2020, 22: 2283–2314

    Article  Google Scholar 

  9. Wu Q Q, Zhang S W, Zheng B X, et al. Intelligent reflecting surface aided wireless communications: a tutorial. IEEE Trans Commun, 2021. doi: https://doi.org/10.1109/TCOMM.2021.3051897

  10. Yuan X J, Zhang Y J A, Shi Y M, et al. Reconfigurable-intelligent-surface empowered wireless communications: challenges and opportunities. IEEE Wirel Commun, 2021. doi: https://doi.org/10.1109/MWC.001.2000256

  11. Renzo M D, Debbah M, Phan-Huy D T, et al. Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come. J Wirel Com Netw, 2019, 2019: 129

    Article  Google Scholar 

  12. di Renzo M, Zappone A, Debbah M, et al. Smart radio environments empowered by reconfigurable intelligent surfaces: how it works, state of research, and the road ahead. IEEE J Sel Areas Commun, 2020, 38: 2450–2525

    Article  Google Scholar 

  13. Huang C W, Hu S, Alexandropoulos G C, et al. Holographic MIMO surfaces for 6G wireless networks: opportunities, challenges, and trends. IEEE Wirel Commun, 2020, 27: 118–125

    Article  Google Scholar 

  14. di Renzo M, Ntontin K, Song J, et al. Reconfigurable intelligent surfaces vs. relaying: differences, similarities, and performance comparison. IEEE Open J Commun Soc, 2020, 1: 798–807

    Article  Google Scholar 

  15. Liu Y W, Liu X, Mu X D, et al. Reconfigurable intelligent surfaces: principles and opportunities. 2020. ArXiv:2007.03435

  16. Tang W, Chen M Z, Chen X, et al. Wireless communications with reconfigurable intelligent surface: path loss modeling and experimental measurement. IEEE Trans Wirel Commun, 2021, 20: 421–439

    Article  Google Scholar 

  17. Tang W K, Chen X Y, Chen M Z, et al. Path loss modeling and measurements for reconfigurable intelligent surfaces in the millimeter-wave frequency band. 2021. ArXiv:2101.08607

  18. Gacanin H, di Renzo M. Wireless 2.0: toward an intelligent radio environment empowered by reconfigurable meta-surfaces and artificial intelligence. IEEE Veh Technol Mag, 2020, 15: 74–82

    Article  Google Scholar 

  19. Elbir A M, Mishra K V. A survey of deep learning architectures for intelligent reflecting surfaces. 2020. ArXiv:2009.02540

  20. Gao F F, Cui T, Nallanathan A. On channel estimation and optimal training design for amplify and forward relay networks. IEEE Trans Wirel Commun, 2008, 7: 1907–1916

    Article  MATH  Google Scholar 

  21. Yang S, Belfiore J C. Towards the optimal amplify-and-forward cooperative diversity scheme. IEEE Trans Inform Theor, 2007, 53: 3114–3126

    Article  MathSciNet  MATH  Google Scholar 

  22. Li Q, Wen M W, Di Renzo M. Single-RF MIMO: from spatial modulation to metasurface-based modulation. 2020. ArXiv:2009.00789

  23. Lin S E, Zheng B X, Alexandropoulos G C, et al. Reconfigurable intelligent surfaces with reflection pattern modulation: beamforming design and performance analysis. IEEE Trans Wirel Commun, 2020, 20: 741–754

    Article  Google Scholar 

  24. Yang G, Liang Y C, Zhang R, et al. Modulation in the air: backscatter communication over ambient OFDM carrier. IEEE Trans Commun, 2018, 66: 1219–1233

    Article  Google Scholar 

  25. Yang G, Ho C K, Guan Y L. Multi-antenna wireless energy transfer for backscatter communication systems. IEEE J Sel Areas Commun, 2015, 33: 2974–2987

    Article  Google Scholar 

  26. Kang X, Liang Y C, Yang J. Riding on the primary: a new spectrum sharing paradigm for wireless-powered IoT devices. IEEE Trans Wirel Commun, 2018, 17: 6335–6347

    Article  Google Scholar 

  27. Liu W, Liang Y C, Li Y, et al. Backscatter multiplicative multiple-access systems: fundamental limits and practical design. IEEE Trans Wirel Commun, 2018, 17: 5713–5728

    Article  Google Scholar 

  28. Fara R, Phan-Huy D T, Ratajczak P, et al. Reconfigurable intelligent surface-assisted ambient backscatter communications — experimental assessment. 2021. ArXiv:2103.08427

  29. Liang Y C, Zhang Q, Larsson E G, et al. Symbiotic radio: cognitive backscattering communications for future wireless networks. IEEE Trans Cogn Commun Netw, 2020, 6: 1242–1255

    Article  Google Scholar 

  30. Zhang Q, Liang Y C, Poor H V. Large intelligent surface/antennas (LISA) assisted symbiotic radio for IoT communications. 2020. ArXiv:2002.00340

  31. Gradoni G, di Renzo M. End-to-end mutual coupling aware communication model for reconfigurable intelligent surfaces: an electromagnetic-compliant approach based on mutual impedances. IEEE Wirel Commun Lett, 2021, 10: 938–942

    Article  Google Scholar 

  32. Qian X W, di Renzo M. Mutual coupling and unit cell aware optimization for reconfigurable intelligent surfaces. IEEE Wirel Commun Lett, 2021. doi: https://doi.org/10.1109/LWC.2021.3061449

  33. Abrardo A, Dardari D, Di Renzo M, et al. MIMO interference channels assisted by reconfigurable intelligent surfaces: mutual coupling aware sum-rate optimization based on a mutual impedance channel model. 2021. ArXiv:2102.07155

  34. Abeywickrama S, Zhang R, Wu Q Q, et al. Intelligent reflecting surface: practical phase shift model and beamforming optimization. IEEE Trans Commun, 2020, 68: 5849–5863

    Article  Google Scholar 

  35. Wang D, Yin L Z, Huang T J, et al. Design of a 1 bit broadband space-time-coding digital metasurface element. Anten Wirel Propag Lett, 2020, 19: 611–615

    Article  Google Scholar 

  36. Huang C, Sun B, Pan W B, et al. Dynamical beam manipulation based on 2-bit digitally-controlled coding metasurface. Sci Rep, 2017, 7: 42302

    Article  Google Scholar 

  37. Xu H J, Xu S H, Yang F, et al. Design and experiment of a dual-band 1 bit reconfigurable reflectarray antenna with independent large-angle beam scanning capability. Anten Wirel Propag Lett, 2020, 19: 1896–1900

    Article  Google Scholar 

  38. Yang H H, Yang F, Xu S H, et al. A 1-bit multipolarization reflectarray element for reconfigurable large-aperture antennas. Anten Wirel Propag Lett, 2017, 16: 581–584

    Article  Google Scholar 

  39. Wang Z L, Ge Y H, Pu J X, et al. 1 bit electronically reconfigurable folded reflectarray antenna based on p-i-n diodes for wide-angle beam-scanning applications. IEEE Trans Anten Propag, 2020, 68: 6806–6810

    Article  Google Scholar 

  40. Yang H H, Yang F, Xu S H, et al. A 1-Bit 10×10 reconfigurable reflectarray antenna: design, optimization, and experiment. IEEE Trans Anten Propag, 2016, 64: 2246–2254

    Article  MathSciNet  MATH  Google Scholar 

  41. Han J, Li L, Liu G, et al. A wideband 1 bit 12×12 reconfigurable beam-scanning reflectarray: design, fabrication, and measurement. Anten Wirel Propag Lett, 2019, 18: 1268–1272

    Article  Google Scholar 

  42. Li Y Z, Abbosh A. Reconfigurable reflectarray antenna using single-layer radiator controlled by PIN diodes. IET Microw Anten Propag, 2015, 9: 664–671

    Article  Google Scholar 

  43. Yang H H, Yang F, Cao X Y, et al. A 1600-element dual-frequency electronically reconfigurable reflectarray at X/Ku-band. IEEE Trans Anten Propag, 2017, 65: 3024–3032

    Article  MathSciNet  MATH  Google Scholar 

  44. Yang X, Xu S H, Yang F, et al. A novel 2-bit reconfigurable reflectarray element for both linear and circular polarizations. In: Proceedings of IEEE International Symposium on Antennas and Propagation and USNC/URSI National Radio Science Meeting, 2017. 2083–2084

  45. Venneri F, Costanzo S, di Massa G. Design and validation of a reconfigurable single varactor-tuned reflectarray. IEEE Trans Anten Propag, 2013, 61: 635–645

    Article  Google Scholar 

  46. Ratni B, de Lustrac A, Piau G P, et al. Active metasurface for reconfigurable reflectors. Appl Phys A, 2018, 124: 104

    Article  Google Scholar 

  47. Trampler M E, Lovato R E, Gong X. Dual-resonance continuously beam-scanning x-band reflectarray antenna. IEEE Trans Anten Propag, 2020, 68: 6080–6087

    Article  Google Scholar 

  48. Yang X, Xu S H, Yang F, et al. Design of a 2-bit reconfigurable reflectarray element using two MEMS switches. In: Proceedings of IEEE International Symposium on Antennas and Propagation and USNC/URSI National Radio Science Meeting, 2015. 2167–2168

  49. Debogovic T, Perruisseau-Carrier J. Low loss MEMS-reconfigurable 1-bit reflectarray cell with dual-linear polarization. IEEE Trans Anten Propag, 2014, 62: 5055–5060

    Article  MATH  Google Scholar 

  50. Bayraktar O, Civi O A, Akin T. Beam switching reflectarray monolithically integrated with RF MEMS switches. IEEE Trans Anten Propag, 2012, 60: 854–862

    Article  Google Scholar 

  51. Yang J, Wang P J, Sun S Y, et al. A novel electronically controlled two-dimensional terahertz beam- scanning reflectarray antenna based on liquid crystals. Front Phys, 2020, 8: 435

    Article  Google Scholar 

  52. Perez-Palomino G, Baine P, Dickie R, et al. Design and experimental validation of liquid crystal-based reconfigurable reflectarray elements with improved bandwidth in F-band. IEEE Trans Anten Propag, 2013, 61: 1704–1713

    Article  Google Scholar 

  53. Carrasco E, Perruisseau-Carrier J. Reflectarray antenna at terahertz using graphene. Anten Wirel Propag Lett, 2013, 12: 253–256

    Article  Google Scholar 

  54. Hamzavi-Zarghani Z, Yahaghi A, Matekovits L. Reconfigurable metasurface lens based on graphene split ring resonators using Pancharatnam-Berry phase manipulation. J ElectroMagn Waves Appl, 2019, 33: 572–583

    Article  Google Scholar 

  55. Dong L, Wang H M. Enhancing secure MIMO transmission via intelligent reflecting surface. IEEE Trans Wirel Commun, 2020, 19: 7543–7556

    Article  Google Scholar 

  56. Tang W, Dai J Y, Chen M Z, et al. MIMO transmission through reconfigurable intelligent surface: system design, analysis, and implementation. IEEE J Sel Areas Commun, 2020, 38: 2683–2699

    Article  Google Scholar 

  57. Zhang L, Wang Z X, Shao R W, et al. Dynamically realizing arbitrary multi-bit programmable phases using a 2-bit timedomain coding metasurface. IEEE Trans Anten Propag, 2020, 68: 2984–2992

    Article  Google Scholar 

  58. Li L L, Shuang Y, Ma Q, et al. Intelligent metasurface imager and recognizer. Light Sci Appl, 2019, 8: 97

    Article  Google Scholar 

  59. Pan X T, Yang F, Xu S H, et al. A 10 240-element reconfigurable reflectarray with fast steerable monopulse patterns. IEEE Trans Anten Propag, 2021, 69: 173–181

    Article  Google Scholar 

  60. Huang J. Microstrip reflectarray. In: Proceedings of Antennas and Propagation Society Symposium 1991 Digest, 1991. 612–615

  61. Berry D, Malech R, Kennedy W. The reflectarray antenna. IEEE Trans Anten Propag, 1963, 11: 645–651

    Article  Google Scholar 

  62. Chen J, Liang Y C, Pei Y Y, et al. Intelligent reflecting surface: a programmable wireless environment for physical layer security. IEEE Access, 2019, 7: 82599–82612

    Article  Google Scholar 

  63. Wu Q Q, Zhang R. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming. IEEE Trans Wirel Commun, 2019, 18: 5394–5409

    Article  Google Scholar 

  64. Taha A, Alrabeiah M, Alkhateeb A. Enabling large intelligent surfaces with compressive sensing and deep learning. IEEE Access, 2021, 9: 44304–44321

    Article  Google Scholar 

  65. Zhang J M, Qi C H, Li P, et al. Channel estimation for reconfigurable intelligent surface aided massive MIMO system. In: Proceedings of the 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2020. 1–5

  66. He Z Q, Yuan X. Cascaded channel estimation for large intelligent metasurface assisted massive MIMO. IEEE Wirel Commun Lett, 2020, 9: 210–214

    Article  Google Scholar 

  67. Liu H, Yuan X J, Zhang Y J A. Matrix-calibration-based cascaded channel estimation for reconfigurable intelligent surface assisted multiuser MIMO. IEEE J Sel Areas Commun, 2020, 38: 2621–2636

    Article  Google Scholar 

  68. Guan X R, Wu Q Q, Zhang R. Anchor-assisted intelligent reflecting surface channel estimation for multiuser communications. 2020. ArXiv:2008.00622

  69. Wei L, Huang C W, Alexandropoulos G C, et al. Channel estimation for RIS-empowered multi-user MISO wireless communications. IEEE Trans Commun, 2021. doi: https://doi.org/10.1109/TCOMM.2021.3063236

  70. de Araújo G T, de Almeida A L. Parafac-based channel estimation for intelligent reflective surface assisted MIMO system. In: Proceedings of the 11st Sensor Array and Multichannel Signal Processing Workshop (SAM), 2020. 1–5

  71. Mishra D, Johansson H. Channel estimation and low-complexity beamforming design for passive intelligent surface assisted MISO wireless energy transfer. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019. 4659–4663

  72. Yang Y F, Zheng B X, Zhang S W, et al. Intelligent reflecting surface meets OFDM: protocol design and rate maximization. IEEE Trans Commun, 2020, 68: 4522–4535

    Article  Google Scholar 

  73. Jensen T L, de Carvalho E. An optimal channel estimation scheme for intelligent reflecting surfaces based on a minimum variance unbiased estimator. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020. 5000–5004

  74. Sun S, Yan H S. Channel estimation for reconfigurable intelligent surface-assisted wireless communications considering doppler effect. IEEE Wirel Commun Lett, 2021, 10: 790–794

    Article  Google Scholar 

  75. Zheng B X, Zhang R. Intelligent reflecting surface-enhanced OFDM: channel estimation and reflection optimization. IEEE Wirel Commun Lett, 2020, 9: 518–522

    Article  Google Scholar 

  76. Kundu N K, McKay M R. A deep learning-based channel estimation approach for MISO communications with large intelligent surfaces. In: Proceedings of the 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 2020. 1–6

  77. Liu C, Liu X M, Ng D W K, et al. Deep residual network empowered channel estimation for IRS-assisted multi-user communication systems. 2020. ArXiv:2012.00241

  78. Wang Z R, Liu L, Cui S G. Channel estimation for intelligent reflecting surface assisted multiuser communications: framework, algorithms, and analysis. IEEE Trans Wirel Commun, 2020, 19: 6607–6620

    Article  Google Scholar 

  79. Zheng B X, You C S, Zhang R. Intelligent reflecting surface assisted multi-user OFDMA: channel estimation and training design. IEEE Trans Wirel Commun, 2020, 19: 8315–8329

    Article  Google Scholar 

  80. Chen J, Liang Y C, Cheng H V, et al. Channel estimation for reconfigurable intelligent surface aided multi-user MIMO systems. 2019. ArXiv:1912.03619

  81. Nadeem Q U A, Kammoun A, Chaaban A, et al. Asymptotic max-min SINR analysis of reconfigurable intelligent surface assisted MISO systems. IEEE Trans Wirel Commun, 2020, 19: 7748–7764

    Article  Google Scholar 

  82. Guo H, Liang Y C, Chen J, et al. Weighted sum-rate maximization for reconfigurable intelligent surface aided wireless networks. IEEE Trans Wirel Commun, 2020, 19: 3064–3076

    Article  Google Scholar 

  83. Huang C, Zappone A, Alexandropoulos G C, et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication. IEEE Trans Wirel Commun, 2019, 18: 4157–4170

    Article  Google Scholar 

  84. Yang G, Xu X Y, Liang Y C. Intelligent reflecting surface assisted non-orthogonal multiple access. In: Proceedings of IEEE Wireless Communications and Networking Conference (WCNC), 2020. 1–6

  85. Mu X D, Liu Y W, Guo L, et al. Exploiting intelligent reflecting surfaces in NOMA networks: joint beamforming optimization. IEEE Trans Wirel Commun, 2020, 19: 6884–6898

    Article  Google Scholar 

  86. Zhang L, Wang Y, Tao W G, et al. Intelligent reflecting surface aided MIMO cognitive radio systems. IEEE Trans Veh Technol, 2020, 69: 11445–11457

    Article  Google Scholar 

  87. Cui M, Zhang G C, Zhang R. Secure wireless communication via intelligent reflecting surface. IEEE Wirel Commun Lett, 2019, 8: 1410–1414

    Article  Google Scholar 

  88. Yu X H, Xu D F, Sun Y, et al. Robust and secure wireless communications via intelligent reflecting surfaces. IEEE J Sel Areas Commun, 2020, 38: 2637–2652

    Article  Google Scholar 

  89. Shen H, Xu W, Gong S L, et al. Secrecy rate maximization for intelligent reflecting surface assisted multi-antenna communications. IEEE Commun Lett, 2019, 23: 1488–1492

    Article  Google Scholar 

  90. Hu S K, Wei Z Q, Cai Y X, et al. Robust and secure sum-rate maximization for multiuser MISO downlink systems with self-sustainable IRS. 2021. ArXiv:2101.10549

  91. Chu Z, Hao W M, Xiao P, et al. Intelligent reflecting surface aided multi-antenna secure transmission. IEEE Wireless Commun Lett, 2020, 9: 108–112

    Article  Google Scholar 

  92. Hong S, Pan C H, Ren H, et al. Artificial-noise-aided secure MIMO wireless communications via intelligent reflecting surface. IEEE Trans Commun, 2020, 68: 7851–7866

    Article  Google Scholar 

  93. Long R, Liang Y C, Pei Y, et al. Active reconfigurable intelligent surface aided wireless communications. IEEE Trans Wirel Commun, 2021. doi: https://doi.org/10.1109/TWC.2021.3064024

  94. Lyu B, Hoang D T, Gong S M, et al. Intelligent reflecting surface assisted wireless powered communication networks. In: Proceedings of IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2020. 1–6

  95. Wu Q Q, Zhang R. Joint active and passive beamforming optimization for intelligent reflecting surface assisted SWIPT under QoS constraints. IEEE J Sel Areas Commun, 2020, 38: 1735–1748

    Article  Google Scholar 

  96. Perović N S, Tran L N, Di Renzo M, et al. Achievable rate optimization for MIMO systems with reconfigurable intelligent surfaces. IEEE Trans Wirel Commun, 2021. doi: https://doi.org/10.1109/TWC.2021.3054121

  97. Perović N S, Tran L N, Di Renzo M, et al. Optimization of RIS-aided MIMO systems via the cutoff rate. 2020. ArXiv:2012.05131

  98. Huang C, Mo R, Yuen C. Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning. IEEE J Sel Areas Commun, 2020, 38: 1839–1850

    Article  Google Scholar 

  99. Yang H L, Xiong Z H, Zhao J, et al. Deep reinforcement learning-based intelligent reflecting surface for secure wireless communications. IEEE Trans Wirel Commun, 2021, 20: 375–388

    Article  Google Scholar 

  100. Lee G, Jung M, Kasgari A T Z, et al. Deep reinforcement learning for energy-efficient networking with reconfigurable intelligent surfaces. In: Proceedings of IEEE International Conference on Communications (ICC), 2020. 1–6

  101. Huang C W, Yang Z H, Alexandropoulos G C, et al. Multi-hop RIS-empowered terahertz communications: a DRL-based hybrid beamforming design. IEEE J Sel Areas Commun, 2021. doi: https://doi.org/10.1109/JSAC.2021.3071836

  102. Li D. Ergodic capacity of intelligent reflecting surface-assisted communication systems with phase errors. IEEE Commun Lett, 2020, 24: 1646–1650

    Article  Google Scholar 

  103. Wu Q Q, Zhang R. Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts. IEEE Trans Commun, 2020, 68: 1838–1851

    Article  Google Scholar 

  104. Zhao M M, Wu Q Q, Zhao M J, et al. IRS-aided wireless communication with imperfect CSI: is amplitude control helpful or not? In: Proceedings of IEEE Global Communications Conference, 2020. 1–6

  105. Rajagopalan H, Rahmat-Samii Y. Loss quantification for microstrip reflectarray: issue of high fields and currents. In: Proceedings of IEEE Antennas and Propagation Society International Symposium, 2008. 1–4

  106. Jung M, Saad W, Debbah M, et al. On the optimality of reconfigurable intelligent surfaces (RISs): passive beamforming, modulation, and resource allocation. IEEE Trans Wirel Commun, 2021. doi: https://doi.org/10.1109/TWC.2021.3058366

  107. Shen H, Xu W, Gong S L, et al. Beamforming optimization for IRS-aided communications with transceiver hardware impairments. IEEE Trans Commun, 2021, 69: 1214–1227

    Article  Google Scholar 

  108. Han Y, Tang W K, Jin S, et al. Large intelligent surface-assisted wireless communication exploiting statistical CSI. IEEE Trans Veh Technol, 2019, 68: 8238–8242

    Article  Google Scholar 

  109. Guo H, Liang Y C, Xiao S. Model-free optimization for reconfigurable intelligent surface with statistical CSI. 2019. ArXiv:1912.10913

  110. Zhang J, Liu J, Ma S D, et al. Transmitter design for large intelligent surface-assisted MIMO wireless communication with statistical CSI. In: Proceedings of IEEE International Conference on Communications Workshops, 2020. 1–5

  111. Zhou G, Pan C H, Ren H, et al. Robust beamforming design for intelligent reflecting surface aided MISO communication systems. IEEE Wirel Commun Lett, 2020, 9: 1658–1662

    Article  Google Scholar 

  112. Yuan J, Liang Y C, Joung J G, et al. Intelligent reflecting surface (IRS)-enhanced cognitive radio system. In: Proceedings of IEEE International Conference on Communications, 2020. 1–6

  113. Zappone A, di Renzo M, Shams F, et al. Overhead-aware design of reconfigurable intelligent surfaces in smart radio environments. IEEE Trans Wirel Commun, 2021, 20: 126–141

    Article  Google Scholar 

  114. Wang J, Liang Y C, Han S Y, et al. Robust beamforming and phase shift design for IRS-enhanced multi-user MISO downlink communication. In: Proceedings of IEEE International Conference on Communications, 2020. 1–6

  115. Zhao M M, Liu A, Zhang R. Outage-constrained robust beamforming for intelligent reflecting surface aided wireless communication. IEEE Trans Signal Process, 2021, 69: 1301–1316

    Article  MathSciNet  MATH  Google Scholar 

  116. Abrardo A, Dardari D, Di Renzo M. Intelligent reflecting surfaces: sum-rate optimization based on statistical CSI. 2020. ArXiv:2012.10679

  117. Basar E, di Renzo M, de Rosny J, et al. Wireless communications through reconfigurable intelligent surfaces. IEEE Access, 2019, 7: 116753

    Article  Google Scholar 

  118. Tang W, Dai J Y, Chen M, et al. Programmable metasurface-based RF chain-free 8PSK wireless transmitter. Electron lett, 2019, 55: 417–420

    Article  Google Scholar 

  119. Basar E. Reconfigurable intelligent surface-based index modulation: a new beyond MIMO paradigm for 6G. IEEE Trans Commun, 2020, 68: 3187–3196

    Article  Google Scholar 

  120. Dai J Y, Tang W, Yang L X, et al. Realization of multi-modulation schemes for wireless communication by time-domain digital coding metasurface. IEEE Trans Anten Propag, 2020, 68: 1618–1627

    Article  Google Scholar 

  121. Bereyhi A, Jamali V, Muller R R, et al. A single-RF architecture for multiuser massive MIMO via reflecting surfaces. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2020. 8688–8692

  122. Liu R, Li H Y, Li M, et al. Symbol-level precoding design for intelligent reflecting surface assisted multi-user MIMO systems. In: Proceedings of the 11th International Conference on Wireless Communications and Signal Processing, 2019. 1–6

  123. Liu V, Parks A, Talla V, et al. Ambient backscatter: wireless communication out of thin air. In: Proceedings of ACM SIGCOMM Conference, 2013. 39–50

  124. Iyer V, Talla V, Kellogg B, et al. Inter-technology backscatter: towards internet connectivity for implanted devices. In: Proceedings of ACM SIGCOMM Conference, 2016. 356–369

  125. Zhang P Y, Rostami M, Hu P, et al. Enabling practical backscatter communication for on-body sensors. In: Proceedings of ACM SIGCOMM Conference, 2016. 370–383

  126. Nguyen T, Shin Y, Kim J, et al. Signal detection for ambient backscatter communication with OFDM carriers. Sensors, 2019, 19: 517

    Article  Google Scholar 

  127. Zhao H T, Shuang Y, Wei M L, et al. Metasurface-assisted massive backscatter wireless communication with commodity wi-fi signals. Nat Commun, 2020, 11: 1–10

    Google Scholar 

  128. Long R Z, Liang Y C, Guo H Y, et al. Symbiotic radio: a new communication paradigm for passive Internet of Things. IEEE Int Things J, 2020, 7: 1350–1363

    Article  Google Scholar 

  129. Yang G, Zhang Q Q, Liang Y C. Cooperative ambient backscatter communications for green Internet-of-Things. IEEE Int Things J, 2018, 5: 1116–1130

    Article  Google Scholar 

  130. Yan W J, Yuan X J, He Z Q, et al. Passive beamforming and information transfer design for reconfigurable intelligent surfaces aided multiuser MIMO systems. IEEE J Sel Areas Commun, 2020, 38: 1793–1808

    Article  Google Scholar 

  131. Guo S S, Lv S H, Zhang H X, et al. Reflecting modulation. IEEE J Sel Areas Commun, 2020, 38: 2548–2561

    Article  Google Scholar 

  132. Karasik R, Simeone O, Di Renzo M, et al. Single-RF multi-user communication through reconfigurable intelligent surfaces: an information-theoretic analysis. 2021. ArXiv:2101.07556

  133. Nayak S, Patgiri R. 6G: envisioning the key issues and challenges. 2020. ArXiv:2004.04024

  134. Mursia P, Sciancalepore V, Garcia-Saavedra A, et al. RISMA: reconfigurable intelligent surfaces enabling beamforming for IoT massive access. IEEE J Sel Areas Commun, 2021, 39: 1072–1085

    Article  Google Scholar 

  135. Xia S H, Shi Y M. Intelligent reflecting surface for massive device connectivity: joint activity detection and channel estimation. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2020. 5175–5179

  136. Lu Y, Dai L L. Reconfigurable intelligent surface based hybrid precoding for THz communications. 2020. ArXiv:2012.06261

  137. Ning B Y, Chen Z, Chen W R, et al. Channel estimation and transmission for intelligent reflecting surface assisted THz communications. In: Proceedings of IEEE International Conference on Communications (ICC), 2020. 1–7

  138. Tekbıyık K, Kurt G K, Ekti A R, et al. Reconfigurable intelligent surface empowered terahertz communication for LEO satellite networks. 2020. ArXiv:2007.04281

  139. Pan Y J, Wang K Z, Pan C H, et al. UAV-assisted and intelligent reflecting surfaces-supported terahertz communications. IEEE Wirel Commun Lett, 2021. doi: https://doi.org/10.1109/LWC.2021.3063365

  140. Hashemi R, Ali S, Mahmood N H, et al. Average rate and error probability analysis in short packet communications over RIS-aided URLLC systems. 2021. ArXiv:2102.13363

  141. Ndjiongue A R, Ngatched T, Dobre O A, et al. Re-configurable intelligent surface-based VLC receivers using tunable liquid-crystals: the concept. 2021. ArXiv:2101.02369

  142. Yang L, Yan X Q, da Costa D B, et al. Indoor mixed dual-hop VLC/RF systems through reconfigurable intelligent surfaces. IEEE Wirel Commun Lett, 2020, 9: 1995–1999

    Article  Google Scholar 

  143. Bai T, Pan C H, Han C, et al. Empowering mobile edge computing by exploiting reconfigurable intelligent surface. 2021. ArXiv:2102.02569

  144. Hu X, Masouros C, Wong K K. Reconfigurable intelligent surface aided mobile edge computing: from optimization-based to location-only learning-based solutions. IEEE Trans Commun, 2021. doi: https://doi.org/10.1109/TCOMM.2021.3066495

  145. Hashida H, Kawamoto Y, Kato N. Intelligent reflecting surface placement optimization in air-ground communication networks toward 6G. IEEE Wirel Commun, 2020, 27: 146–151

    Article  Google Scholar 

  146. Ge L H, Dong P H, Zhang H, et al. Joint beamforming and trajectory optimization for intelligent reflecting surfaces-assisted UAV communications. IEEE Access, 2020, 8: 78702–78712

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. U1801261, 61631005), in part by National Key R&D Program of China (Grant No. 2018YFB1801105), in part by Macau Science and Technology Development Fund (FDCT), Macau SAR (Grant No. 0009/2020/A1), in part by Key Areas of Research and Development Program of Guangdong Province (Grant No. 2018B010114001), in part by Programme of Introducing Talents of Discipline to Universities (Grant No. B20064), and in part by Fundamental Research Funds for the Central Universities (Grant No. ZYGX2019Z022).

Author information

Authors and Affiliations

  1. Center for Intelligent Networking and Communications, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Ying-Chang Liang, Jie Chen, Ruizhe Long & Zhen-Qing He

  2. School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Xianqi Lin, Chenlu Huang & Shilin Liu

  3. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada

    Xuemin Sherman Shen

  4. Laboratoire des Signaux et Systèmes, Université Paris-Saclay, CNRS, CentraleSupélec, 3 Rue Joliot-Curie, 91192, Gif-sur-Yvette, France

    Marco Di Renzo

Authors
  1. Ying-Chang Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruizhe Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhen-Qing He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xianqi Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chenlu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shilin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xuemin Sherman Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Marco Di Renzo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying-Chang Liang.

Additional information

Invited article

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, YC., Chen, J., Long, R. et al. Reconfigurable intelligent surfaces for smart wireless environments: channel estimation, system design and applications in 6G networks. Sci. China Inf. Sci. 64, 200301 (2021). https://doi.org/10.1007/s11432-020-3261-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-3261-5

Keywords

Search

Navigation