Reflecting Intelligent Surfaces for 5G and Beyond 5G


The milli-meter wave carriers used in 5G are typically used as direct line of sight (LOS) medium due to the high pathloss at those frequencies. This also reduces the richness in their corresponding multi-path channels. This not only restricts the availability of high data rate to users who are not in LOS path, but also reduces the effectiveness of spatial multiplexing.  However, if the multi-paths in the propagation channel between the gNB and UE can be controlled to have higher processing gain (such as passive focusing gain) or create richness in the multi-path, additional performance gains can be obtained. This vertical describes some of the challenges in implementing such a heterogenous network with regular gNBs and controllable reflecting surfaces such as RIS. 

Reflecting intelligent surfaces (RIS) otherwise known as Smart reflectors or Intelligent Reflecting Surfaces, employ controllable passive reflection of radio waves from their surfaces. The reflector properties such as angle of reflection, magnitude and phase of reflection can be controlled by suitably programming the individual reflecting elements. In millimeter-wave frequencies, one can accommodate thousands of such elements in a small area, which allows one to get large gain in the signal of interest due the passive array gain.  


Topic Description

Reflecting Intelligent Surface (RIS) also known in other names such as Smart wall, Large Intelligent Surface (LIS), Intelligent Reconfigurable Surface (IRS), etc., is 2-dimensional surface of engineered material whose reflection properties can be programmed to modify the wireless propagation environment. In 5G and beyond 5G technologies, one of the dimensions not harnessed today is the propagation environment. By placing the RIS at strategic places in a multi-path channel, the channel response can be modified to our advantage, especially to improve SINR at specific user locations, or increase the channel rank in MIMO channels. Moreover, RIS can be used for simultaneous data and power delivery to wireless IoT devices.

This vertical will touch upon 4 key aspects of RIS in the context of 5G and beyond 5G (B5G). (i) How channel estimation plays a role in the sum data rate that can be achieved in a RIS controlled environment. That is, we need to estimate the composite channel between the Transmitter (Tx) and Receiver (Rx), which consists of the channel between the Tx and RIS, RIS channel, and channel between RIS and Rx, in order to maximize the sum thruput in a multi-user scenario.  (ii) How the RIS can be integrated and controlled in the current 5G and B5G network architecture, (iii) The impending convergence of data communication, user localization and Radar and how RIS can be used in this convergence, for improving the data rate and accuracy of user location estimation. Here, the focus is on the information theoretic trade-offs between the data communication rate and Radar performance (i.e., user location estimation accuracy).  (iv) Finally, we delve at the future directions of RIS related research for diverse applications. 



In this vertical, we present 4 different topics related to the application of RIS in the context of 5G and beyond 5G communication systems.  List of topics and abstracts under this vertical are given below.

  1. [30 mins] Configuring Intelligent Reflecting Surfaces for Multiuser OFDM Wireless Communications [1]

This talk gives an application of RIS in an OFDM system, where the utility of RIS is explored for improving the sum throughput of all users. Using a training sequence, an algorithm is developed to estimate the channel between the gNB and every user which in turn is used to find the optimal reflection angles of the RIS for each user. This is the problem posed for IEEE Signal Processing Cup 2021, hosted by the recent ICASSP 2021 conference [1]. This talk gives the details of the solution designed by one of the contestants, who were adjudged within the top 10 teams. For a 4096 element RIS, with only 2 phase states, say 0 and 180 degrees, there are 2^4096 possible angle settings from which the correct angle setting for each of 50 users need to be estimated. For the 4096 elements, 4 x 4096 receiver data set along with the transmitted data is available for estimating the channel between the BS and all users. Selecting the correct set of phase values for each of elements, to maximize the SNR for every user is the final goal.  Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

  1. [30 mins] Accommodating RIS in 5G Network framework as an auxiliary RU [2,3]

This talk provides details on how RIS can be deployed in a 5G network using the existing control mechanism and implementation method. This method paves the way to implement a heterogenous network with 5G eNBs and RIS. This will prove to be very useful especially in FR2 where network coverage holes limit the ubiquitous availability of high data rates to all users. In this talk, details of the control mechanism and protocol stack which is compatible to the existing 5G network will be discussed.

    1. [60 mins] Convergence of radar, communication and user localization in B5G using RIS [4,5,6,7]

In this talk, the authors discuss the utility of RIS for joint radar and communication (JRC) and localization especially in a multi-path dominated scenario for tracking mobile users. A JRC framework is seen as a solution to the spectrum shortage and congestion in existing communication bands, where both radar and communication can co-exist. Moreover, JRC can share the radio resources and help in improve the localization of the devices. The location information of the users is critical to enable the location dependent services such as VLC, Tera-Hertz communication and broadcast of (for example, marketing) information. In this talk, starting from fundamentals, an information theoretic framework for the problem at hand is described, and the respective achievable bounds are presented. It is shown that the performance of such an RIS-enabled JRC scenario is dependent on minimizing the target ambiguity functions. 

    1. [30 mins] Integration of RIS with other technologies for B5G systems [4,5,6,7]

RIS-assisted wireless networks are expected to offer benefits such as easy deployment and sustainability, passive beamforming with flexible reconfiguration, enhanced spectral and energy efficiencies, and utility in many applications. In particular, the flexible reconfigurability of RIS makes it easy to be integrated with other relevant key technologies for 5G and B5G applications. In this talk, we discuss the potential benefits of use cases such as deep learning-based RIS-assisted systems, RIS-assisted simultaneous wireless information and wireless power transfer, RIS-enabled UAV communications, RIS-enabled edge computing, RIS-enabled non-orthogonal multiple access and RIS-enabled terahertz communication. Challenges, future research directions and open research problems will also be discussed.


  1. Dr. S. J. Thiruvenkatam, is a professor at Department of ECE, Thiagarajar college of Engineering, Madurai and has more than 30 years of experience in teaching the various signal processing and communication courses. He was principal-investigator in 11 projects executed for several defence labs in India. He has authored more than 40 publications in SCI indexed journals and 80 publications in SCI indexed conferences. 11 PhD students defended their thesis under his guidance. His research interests include wireless communication systems, MIMO Radar waveform design, Cognitive radio networks, OFDM Systems, etc.  He received several awards and research fellowship. To name a few, he received Young Scientist Fellowship in 2001, TIFAC Research fellowship in 2007 and Engineering Educator award in 2017. 
  1. Sudhakar Balijepalli is a freelance consultant and technical lead and program manager in the Indian Institute of Science (IISc) driven 5G test bed development project. He has 3 decades of experience in several multi-nationals including IBM, Motorola and Cisco, assuming various roles in the development of software for telecommunication products. He is currently consulting the 5G testbed project as a lead architect and program manager. The IISc 5G testbed is built based on the open software (Open Air Interface) and COTS hardware. Sudhakar played crucial role in expanding the nFAPI for the 5G RAN – Split 6 configuration and successfully demonstrated that in the OAI framework. He is currently migrating the OAI software to Xilinx MPSoC and RFSoC hardware platforms for enabling high bandwidth and MIMO configuration. 


  1. Dr. Ganesan Thiagarajan [Organizer] is currently CTO of MMRFIC Technology Pvt. Ltd, Bangalore. With more than 25 years of experience in telecom/semiconductor devices industry, he had delivered multiple products in WLAN, Cellular infrastructure and mm-wave Radar systems. He assumed various roles at Motorola, Texas Instruments and Arraycomm Inc., including system architect, Algorithm lead and R&D lead. He was elected to Senior Member of Technical Staff (SMTS) during his tenure at Texas Instruments — less than 5% of the technical population gets this award. He holds 20 granted patents in USPTO and published several IEEE journal and conference papers. HIs research interests are mm-wave communication systems, Joint Radar Communication, Machine learning for Signal processing and Quantum error correction codes. He is a senior member of IEEE and chair-elect for IEEE ComSoc Bangalore chapter. He published a research article and delivered an invited talk in “Massive MIMO for IoT” vertical in IEEE 5G WF 2020.
  2. Dr. Sanjeev Gurugopinath [Organizer] is a professor in the department of Electronics and Communication Engineering at PES University, Bangalore. His research interests are in the broad areas of next generation wireless communication systems, power line communication, underwater acoustics and speech technology. He is a co-recipient of the best paper awards at IEEE INDICON 2016, IEEE ICEECCOT 2019, IEEE CONECCT 2020 and IEEE CONECCT 2021.



Organizers’ Profile

Dr. Ganesan Thiagarajan, CTO at MMRFIC Technology Pvt. Ltd., Bangalore, INDIA.

 Dr. G. Thiagarajan, Senior member IEEE.

Dr. Thiagarajan received his B. E. degree in Electronics and Communications Engineering from Thiagarajar College of Engineering, Madurai in 1991, the M.Sc. (Engg.) and Ph.D. degrees in Electrical Communication Engineering from Indian Institute of Science, Bangalore, India, in 1994 and 2014 respectively. He has more than 25 years of Industrial experience in the field of Signal processing and Communication system design, assuming various roles in Texas Instruments, Arraycomm Inc, CA and Motorola. His research interests are in Wireless communication systems, mmwave Radars, MIMO processing, Machine learning for Signal processing and Error correction codes.

He is currently the C.T.O of MMRFIC Technology Pvt. Ltd., Bangalore. He holds 20 patents in USPTO. He authored several IEEE conference and journal papers. He was elected as IEEE Senior member in 2013. He is chair-elect of IEEE ComSoc Bangalore chapter for the year 2020. 


Dr. Sanjeev Gurugopinath, Professor at Dept. of ECE, PES University, Bangalore, INDIA. 

Prof. S. Gurugopinath, Member, IEEE, Member, ISCA, Member, EURASIP, Life Member, ACCS. 

Dr.Gurugopinath received his B. E. degree in Electrical and Electronics Engineering from Dr. Ambedkar Institute of Technology, Bangalore and the M. Tech. degree in Digital Electronics and Communication Engineering from M. S. Ramaiah Institute of Technology, Bangalore, both from Visvesvaraya Technological University, Belgaum. He obtained his Ph.D. degree in signal processing for communications from the Indian Institute of Science, Bangalore. He is currently a professor in the department of Electronics and Communication Engineering at PES University, Bangalore. He has earlier worked with PES Institute of Technology and CMR Institute of Technology in Bangalore.

His research interests are in the areas of detection and estimation theory, information theory and statistics, cross-layer optimization and machine learning techniques for cognitive radio networks. He is a co-recipient of the best paper awards at IEEE INDICON 2016, IEEE ICEECCOT 2019, IEEE CONECCT 2020 and IEEE CONECCT 2021.



  4. del Peral-Rosado, José A., et al. “Survey of cellular mobile radio localization methods: From 1G to 5G.” IEEE Communications Surveys & Tutorials 20.2 (2017): 1124-1148.
  5. He, Jiguang, et al. “Adaptive beamforming design for mmWave RIS-aided joint localization and communication.” 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). IEEE, 2020.
  6. P. Kumari, J. Choi, N. González-Prelcic and R. W. Heath, “IEEE 802.11ad-Based Radar: An Approach to Joint Vehicular Communication-Radar System,” in IEEE Transactions on Vehicular Technology, vol. 67, no. 4, pp. 3012-3027, April 2018, doi: 10.1109/TVT.2017.2774762.
  7. Wymeersch, Henk, et al. “Radio localization and mapping with reconfigurable intelligent surfaces: Challenges, opportunities, and research directions.” IEEE Vehicular Technology Magazine 15.4 (2020): 52-61.