SPML Research Lab

Signal Processing and Machine Learning (SPML) Research Laboratory

In SPML research lab, we are working on developing signal processing techniques for radar/sensing and communication problems, e.g.,

Array Signal Processing for Radar Systems

Signal Processing and Machine Learnnig for Physical Layer Design

Millimeter-Wave & THz Communications

Integrated Sensing and Communications

Intelligent Reflecting Surfaces

Index Modulation

Antenna Array Design and Calibration

Contact: ahmetmelbir@gmail.com ahmet.elbir@istinye.edu.tr

Ongoing Projects: DREAM-6G – Development of REconfigurable intelligent surfaces via beAM index modulation for 6G communications (2025-2028)

We are looking for graduate/postdoc students to take place in our TUBITAK 1001 project at Istinye University. Please contact: ahmetmelbir@gmail.com

Summary: Reconfigurable intelligent surfaces (RIS) are one of the architectures envisioned to be used in 6G communication systems. The aim of using RIS is to improve spectrum efficiency (SE) and energy efficiency in the communication system. Another innovative approach that will increase the SE performance of communication systems is index modulation (IM) methods. Since more information bits are sent between the transmitter and receiver with IM compared to traditional amplitude/phase modulation, SE improvement is achieved. The aim of this project is to improve the SE by the development and the design of BI modulation (BIM) in RIS-based systems. Therefore, the integration of BIM in RIS-based very-large multiple-input-multiple-output (MIMO) systems constitutes the first stage of this project study. Within the scope of the project, the theoretical analysis of the SE obtained by the BIM with hybrid beamformers and the full-digital beamformer will also be examined for the theoretical derivation of the relationship between these two techniques.

Project Outcomes:

A. M. Elbir, A. Celik, A. Abdallah and A. M. Eltawil, "Boosting Spectral Efficiency via Spatial Path Index Modulation in RIS-Aided mMIMO", IEEE Transactions on Wireless Communications, vol. 25, pp. 14382-14396, 2026, doi: 10.1109/TWC.2026.3674403 [Preprint] [Full Paper]

A. M. Elbir, A. Abdallah, A. Celik and A. M. Eltawil, "Spatial Path Index Modulation for RIS-Aided Massive MIMO", IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Istanbul, Turkey, Sep, 2025.

A. M. Elbir, "Beam Index Modulation for Adaptive Smart Surface Communication", 12th URSI Türkiye Scientific Congress and National Committee General Assembly, Izmir, Turkey

Near-fied Signal Processing

We develop signal processing techniques for near-field radar and communication systems:

Related papers:

A. M. Elbir, K. V. Mishra, S. Chatzinotas, M. Bennis, "Terahertz-Band Integrated Sensing and Communications: Challenges and Opportunities", IEEE Aerospace and Electronic Systems Magazine, vol. 39, no. 12, pp. 38-49, Dec. 2024, doi: 10.1109/MAES.2024.3476228. [Preprint] [Full Paper]

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, A. Celik and A. M. Eltawil, "NEAT-MUSIC: Auto-calibration of DOA Estimation for Terahertz-Band Massive MIMO Systems", IEEE Wireless Communications Letters, vol. 13, no. 2, pp. 451-455, Feb. 2024 [Preprint] [Full Paper]

A. M. Elbir, K. V. Mishra and S. Chatzinotas "Terahertz-Band Direction Finding With Beam-Split and Mutual Coupling Calibration", IEEE Antennas and Wireless Propagation Letters, vol. 22, no. 11, pp. 2720-2724, Nov. 2023, doi: 10.1109/LAWP.2023.3313350. [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, A. Celik, A. M. Eltawil, "Hybrid Beamforming for Integrated Sensing and Communications With Low Resolution DACs", IEEE Wireless Communications Letters, vol. 14, no. 1, pp. 103-107, Jan. 2025 [Full Paper] [Preprint]

A. M. Elbir, K. V. Mishra, S. Chatzinotas, M. Bennis, "Terahertz-Band Integrated Sensing and Communications: Challenges and Opportunities", IEEE Aerospace and Electronic Systems Magazine, vol. 39, no. 12, pp. 38-49, Dec. 2024, doi: 10.1109/MAES.2024.3476228. [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

A. M. Elbir, A. Celik and A. M. Eltawil, "The Curse of Beam-Squint in ISAC: Causes, Implications, and Mitigation Strategies", IEEE Communications Magazine, vol. 62, no. 9, pp. 52-58, September 2024 [Full Paper] [Preprint]

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", IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 10788-10802, Sept. 2024 [Preprint] [Full Paper]

A. M. Elbir, A. Abdallah, A. Celik and A. M. Eltawil, "Antenna Selection With Beam Squint Compensation for Integrated Sensing and Communications", IEEE Journal of Selected Topics in Signal Processing, vol. 18, no. 5, pp. 857-870, July 2024 [Preprint] [Full Paper]

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", IEEE Network, vol. 37, no. 6, pp. 224-231, Nov. 2023, doi: 10.1109/MNET.128.2200446 [Preprint] [Full Paper]

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", 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