Nowadays, we are witnessing an unprecedented rise in the demand for higher data rates than ever before, owing to the paradigm shift in our day-to-day life. Due to the large bandwidth available at mmWave frequencies, massive MIMO systems operating at mmWave frequencies offers high data-rate communications of the order of gigabits per second and high precision sensing. Since high data-rate communication and high precision sensing are key attributes envisioned for 6G systems, systems operating at mmWave frequencies that enable the coexistence of communications and radar sensing, referred to as Joint Radar Communication (JRC) or Integrated Sensing and Communication (ISAC) systems are also gaining popularity.
However, operating at mmWave frequencies is challenging due to the extreme pathloss, which often results in scenarios with weak non-line-of-sight (NLoS) paths. To combat this harsh propagation environment, mmWave MIMO systems use a large number of millimeter-sized antennas, which can be compactly packed in a small area to provide higher throughput, spectral efficiency, and precise localization. However, due to the large bandwidth of operation and the large number of antennas, the use of dedicated high-resolution quantizers for each radio frequency chain of the system leads to prohibitively large levels of power consumption and circuit complexity.
Moreover, the communication (sensing) ability of the system is completely lost whenever the direct paths to the users (respectively, targets) are blocked. Reconfigurable intelligent surfaces (RISs) are a promising technology that can favorably modify the wireless propagation environment by introducing additional paths. RIS is a large planar array of passive phase shifters and reflection type amplifiers, which can be remotely controlled from the base station (BS) to introduce additional paths that allow reliable sensing and communication even when the direct paths are blocked. In essence, the use of low-resolution quantization and RIS are expected to play a crucial role in 6G systems. My main theme of research is the application of signal processing and machine learning techniques to solve challenging signal processing problems arising in next-generation wireless systems.
Following is a summary of the research work I do at the Signal and Information Processing Lab, headed by Prof. Sundeep Prabhakar Chepuri, at the Dept. of ECE, IISc Bangalore.
Sigma-delta quantization is a classical technique used to increase the resolution of quantizers. Applying the idea of sigma-delta quantization over spatial domain (i.e., across the antennas) lead to the so-called spatial sigma-delta ADCs. MIMO systems utilizing spatial sigma-delta ADCs have been found beneficial when compared with their regular counterparts. In this work, we investigate the problem of channel estimation in mmWave MIMO systems with spatial sigma-delta quantization. A careful selection of the quantization voltage level and phase shift used in the feedback loop of 1-bit sigma-delta ADCs is critical to improve its effective resolution. We first develop a quantization noise model for 1-bit spatial sigma-delta ADCs. Using the developed noise model, we then present a two-step channel estimation algorithm to estimate a multipath channel parameterized by the gains, angles of arrival (AoAs), and angles of departure (AoDs). We demonstrate that with the proposed parametric channel estimation algorithm, MIMO systems with 1-bit spatial sigma-delta ADCs perform significantly better than those with regular 1-bit ADCs and are on par with MIMO systems with high-resolution ADCs.
Associated publications.
[J1] R. S. P. Sankar and S. P. Chepuri, “Channel Estimation in MIMO Systems with One-bit Spatial Sigma-delta ADCs”, IEEE Trans. Signal Process., vol. 70, pp. 4681-4696, Sep. 2022.
[C1] R. S. P. Sankar and S. P. Chepuri, “Millimeter Wave MIMO Channel Estimation with 1-bit Spatial Sigma-Delta Analog-to-Digital Converters,” in Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Toronto, Canada, Jun. 2021.
Reconfigurable intelligent surfaces (RISs) are a promising technology that can favorably modify the wireless propagation environment by introducing additional paths. RIS is a large planar array of passive phase shifters and reflection type amplifiers, which can be remotely controlled from the base station (BS) to introduce additional paths that allow reliable sensing and communication even when the direct paths are blocked. In this project, we investigate the use of RIS in MIMO communication systems and MIMO ISAC systems.
Associated publications and preprints.
[J3] R. S. P. Sankar and S. P. Chepuri, "Channel-aware Placement of Active and Passive Elements in Hybrid RIS-assisted MISO Systems," IEEE Wireless Commun. Lett., vol. 12, no. 7, pp. 1229-1233, Jul. 2023 .
[C4] R. S. P. Sankar and S. P. Chepuri, “Quantized Precoding and RIS-Assisted Modulation for Integrated Sensing and Communications Systems”, in Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Rhodes Island, Greece, Jun. 2023.
[J2] R. S. P. Sankar, S. P. Chepuri, and Y. C. Eldar, “Beamforming in Integrated Sensing and Communication Systems with Reconfigurable Intelligent Surfaces”, IEEE Trans. Wireless Commun., vol. 23, no. 5, pp. 4017-4031, Sep. 2023
[C3] R. S. P. Sankar and S. P. Chepuri, “Beamforming in Hybrid RIS assisted Integrated Sensing and Communication Systems”, in Proc. of the European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, Aug. 2022.
[C2] R. S. P. Sankar, B. Deepak, and S. P. Chepuri, “Joint communication and radar sensing with reconfigurable intelligent surfaces,” in Proc. of the IEEE Int. Wrksp. on Signal Process. Adv. Wireless Commun. (SPAWC), Lucca, Italy, Sep. 2021.
[P1] B. Deepak, R. S. P. Sankar, and S. P. Chepuri, Channel Estimation for RIS-assisted Millimeter-wave MIMO Systems, arXiv preprint arXiv:2011.00900, Mar. 2021.
Integrated sensing and communication (ISAC) systems are modern systems that carry out both sensing and communication while sharing hardware and spectral resources. Reconfigurable intelligent surfaces (RISs) are an emerging technology that allows us to favorably modify the wireless channel. Existing methods use optimization-based techniques to design the precoders and require knowledge of wireless channels. In this project, we aim to develop low-complexity beamforming solutions for ISAC systems. We propose a deep learning-based framework using neural networks to "learn" the precoders in ISAC systems while avoiding the need for wireless channel knowledge. We apply the proposed learning-based framework for various beamforming problems in next-generation ISAC systems. During the previous year, we applied our proposed learning-based framework for beamforming in both narrow-band and wide-band ISAC systems, with and without RIS. The proposed method offers significantly improved performance compared to traditional methods while incurring much lesser computational complexity. During the current academic year, we propose to further extend our learning-based framework for more challenging scenarios, such as cell-free ISAC systems and ISAC systems with antenna selection and hardware impairments.
For this proposal, we have been awarded Qualcomm Innovation Fellowship India 2023 (under "Winners" category) and Qualcomm Innovation Fellowship India 2024 (under "Super Winners" category).
Associated publications
[C5] R. S. P. Sankar, S. Nair, S. Doshi, and S. P. Chepuri, “Learning to Precode for Integrated Sensing and Communications Systems”, in Proc. of the European Signal Processing Conference 2023 (EUSIPCO), Helsinki, Finland, Sep. 2023.