Terahertz-Band Signal Processing

We develop advanced signal processing techniques for the robust design of communications and radar applications in THz band. 

Related papers:

A. M. Elbir, W. Shi, A. K. Papazafeiropoulos, P. Kourtessis, and S. Chatzinotas, "Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation", IEEE Access,  vol. 11, pp. 36409-36420, 2023, doi: 10.1109/ACCESS.2023.3266297. [Preprint] [Full Paper] 

A. M. Elbir, W. Shi, A. K. Papazafeiropoulos, P. Kourtessis, and S. Chatzinotas, "Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model", IEEE Open Journal of Communications Society, vol. 4, pp. 892-907, 2023, doi: 10.1109/OJCOMS.2023.3263625. [Preprint] [Full Paper] 

A. M. Elbir, "A Unified Approach for Beam-Split Mitigation in Wideband THz Hybrid Beamforming", IEEE Transactions on Vehicular Technology [Preprint] [Full Paper] 

A. M. Elbir, S. Chatzinotas, "BSA-OMP: Beam-Split-Aware Orthogonal Matching Pursuit for THz Channel Estimation", IEEE Wireless Communications Letters, vol. 12, no. 4, pp. 738-742, April 2023, doi: 10.1109/LWC.2023.3242699. [Preprint] [Full Paper] 

A. M. Elbir, K. V. Mishra and S. Chatzinotas, "Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming", IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 6, pp. 1468-1483, Nov. 2021, doi: 10.1109/JSTSP.2021.3117410. [Preprint] [Full Paper] 

Integrated Sensing and Communications

In this research direction, we develop model-based and ML-based methods for efficient system architectures, which are capable of performing both radar/sensing and communication tasks.

Selected Publications:

A. M. Elbir, K. V. Mishra, S. Chatzinotas, M. Bennis, "Terahertz-Band Integrated Sensing and Communications: Challenges and Opportunities",

A. M. Elbir, K. V. Mishra and S. Chatzinotas, "Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming", IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 6, pp. 1468-1483, Nov. 2021

A. M. Elbir, K. V. Mishra, M. R. B. Shankar and S. Chatzinotas, "The Rise of Intelligent Reflecting Surfaces in Integrated Sensing and Communications Paradigms",

A. M. Elbir, K. V. Mishra, A. Abdallah, A. Celik and A. M. Eltawil, "Spatial Path Index Modulation in mmWave/THz-Band Integrated Sensing and Communications", [Preprint]

A. M. Elbir, A. Abdallah, A. Celik and A. M. Eltawil, "Antenna Selection With Beam Squint Compensation for Integrated Sensing and Communications", [Preprint]

Hybrid Beamforming

Hybrid analog/digital beamforming involves the use of a few digital baseband components and large number of RF analog phase shifter networks to provide cost-effective solutions for massive MIMO systems. We develop novel model-based and learning-based techniques for beamforming in various applications.

Selected Publications:

A. M. Elbir, K. V. Mishra, S. A. Vorobyov, R. W. Heath Jr, "Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques", IEEE Signal Processing Magazine, vol. 40, no. 4, pp. 118-131, June 2023, doi: 10.1109/MSP.2023.3262366. [Preprint] [Full Paper] 

A. M. Elbir, "A Unified Approach for Beam-Split Mitigation in Wideband THz Hybrid Beamforming", IEEE Transactions on Vehicular Technology [Preprint] [Full Paper] 

A. M. Elbir, K. V. Mishra, M. R. B. Shankar and B. Ottersten, "A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO", IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 642-656, June 2022. [Preprint] [Full Paper] 

A. M. Elbir, K. V. Mishra and S. Chatzinotas, "Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming", IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 6, pp. 1468-1483, Nov. 2021, doi: 10.1109/JSTSP.2021.3117410. [Preprint] [Full Paper] 

A. M. Elbir, "A Deep Learning Framework For Hybrid Beamforming Without Instantaneous CSI Feedback", IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 11743-11755, Oct. 2020, doi: 10.1109/TVT.2020.3017652. [Preprint][Full Paper] 

A. M. Elbir and Kumar Vijay Mishra, "Joint Antenna Selection and Hybrid Beamformer Design using Unquantized and Quantized Deep Learning Networks", IEEE Transactions on Wireless Communications, vol. 19, no. 3, pp. 1677-1688, March 2020. [Preprint] [Full Paper] 

Intelligent Reflecting Surfaces

IRS provides energy and hardware efficiency for various applications in massive MIMO, sensing and vehicular networks. We develop novel techniques for efficient design of IRS. 

Selected Publications:

A. M. Elbir, K. V. Mishra, M. R. B. Shankar and S. Chatzinotas, "The Rise of Intelligent Reflecting Surfaces in Integrated Sensing and Communications Paradigms",

A. M. Elbir and K. V. Mishra, "A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces"

A. M. Elbir and S. Coleri, "Federated Learning for Channel Estimation in Conventional and IRS-Assisted Massive MIMO", to appear in IEEE Transactions on Wireless Communications

A. M. Elbir, A Papazafeiropoulos, P. Kourtessis, and S. Chatzinotas, "Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems", IEEE Wireless Communications Letters, vol. 9, no. 9, pp. 1447-1451, Sept. 2020

Anastasios Papazafeiropoulos, Cunhua Pan, Ahmet M. Elbir, Van Nguyen, Pandelis Kourtessis, Symeon Chatzinotas, "Asymptotic Analysis of Max-Min Weighted SINR for IRS-Assisted MISO Systems with Hardware Impairments", to appear in IEEE Wireless Communications Letters

Machine Learning for Wireless Communications

In this research direction, we develop machine learning (ML)-based methods for the physical layer design problems for wireless communications, e.g., channel estimation, beamforming, etc.

Selected Publications:

A. M. Elbir and K. V. Mishra, "Cognitive Learning-Aided Multi-Antenna Communications", IEEE Wireless Communications, 2022. doi: 10.1109/MWC.008.2100416

A. M. Elbir, A. K. Papazafeiropoulos, and S. Chatzinotas, "Federated Learning for Physical Layer Design", IEEE Communications Magazine, vol. 59, no. 11, pp. 81-87, November 2021

A. M. Elbir and S. Coleri, "Federated Learning for Channel Estimation in Conventional and IRS-Assisted Massive MIMO", IEEE Transactions on Wireless Communications.

A. M. Elbir and Kumar Vijay Mishra, "Joint Antenna Selection and Hybrid Beamformer Design using Unquantized and Quantized Deep Learning Networks", IEEE Transactions on Wireless Communications, vol. 19, no. 3, pp. 1677-1688, March 2020

A. M. Elbir and A. Papazafeiropoulos, "Hybrid Precoding for Multi-User Millimeter Wave Massive MIMO Systems: A Deep Learning Approach," IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 552-563, Jan. 2020

Array signal Processing 

In this research direction, we study novel methods to obtain better direction-of-arrival (DOA) estimation results in multi-antenna systems, which take place in several civilian and military applications. 

Selected Publications:

A. M. Elbir, K. V. Mishra and S. Chatzinotas "Terahertz-Band Direction Finding With Beam-Squint and Mutual Coupling Calibration",

A. M. Elbir and K. V. Mishra, "Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning", IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 1, pp. 255-264, March 2021, doi: 10.1109/TCCN.2020.2999811

A. M. Elbir, "DeepMUSIC: Multiple Signal Classification via Deep Learning," IEEE Sensors Letters, vol. 4, no. 4, pp. 1-4, April 2020

A. M. Elbir, “Direction Finding in the Presence of Direction-Dependent Mutual Coupling," IEEE Antennas and Wireless Propagation Letters, vol. 16, no. , pp. 1541-1544, 2017.

Ahmet M. Elbir, T. Engin Tuncer, “2-D DOA and Mutual Coupling Coefficient Estimation for Arbitrary Array Structures With  Single and Multiple Snapshots”, Digital Signal Processing, Volume 54, Pages 75-86, July 2016.