Research

Our research group focuses on integrating sensing capabilities into communication systems. These capabilities may improve the communication performance, enable localization for the users connected to the network, or detect unconnected objects in the environment, ultimately improving network resiliency [1]. We apply signal processing and machine learning techniques to tackle these challenges. Below, you can find brief descriptions of the current projects at WiSeCom Lab.

[1] N. González-Prelcic et al., "The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications," Proceedings of the IEEE, vol. 112, no. 7, pp. 676-723, July 2024. [IEEEXplore] [arXiv]

Localization as a by-product of sparse channel estimation at mmWave bands

Millimeter-wave (mmWave) communication channels exhibit sparsity in both the angular and delay domains. Sparse channel estimation techniques can extract the angle and delay information associated with each propagation path, enabling user localization with a single access point (AP). However, realistic system effects, such as clock offset between the AP and user equipment (UE) and filtering, must be accounted for as demonstrated in [1]–[5]. Moreover, the unique characteristics of indoor environments can be leveraged to enhance localization accuracy, as shown in these studies. Additionally, reconfigurable intelligent surfaces (RISs) have been shown to significantly improve localization performance [2], [5]. Finally, practical hardware impairments, including array calibration errors and mutual coupling, can severely degrade localization accuracy. Therefore, their impact must be carefully considered and mitigated during joint channel estimation and localization [5].

[1] J. Palacios, N. González-Prelcic and C. Rusu, "Low complexity joint position and channel estimation at millimeter wave based on multidimensional orthogonal matching pursuit," in Proceedings of 30th European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, 2022, pp. 1002-1006. [IEEEXplore]

[2] M. Bayraktar, J. Palacios, N. González-Prelcic, and C. J. Zhang, "Multidimensional orthogonal matching pursuit-based RIS-aided joint localization and channel estimation at mmWave," in Proceedings of IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2022, pp. 1-5. [IEEEXplore]

[3] J. Palacios and N. González-Prelcic, "Separable Multidimensional Orthogonal Matching Pursuit and its Application to Joint Localization and Communication at mmWave," in Proceedings of IEEE Globecom Workshops (GC Wkshps), Kuala Lumpur, Malaysia, 2023, pp. 1421-1426. [IEEEXplore]

[4] J. Palacios, M. Bayraktar, N. González-Prelcic, and H. Chen, "High accuracy device localization in indoor mmWave networks exploiting channel sparsity and virtual anchor mapping," in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), April, 2024. [IEEEXplore]

[5] M. Bayraktar, N. González-Prelcic, G. C. Alexandropoulos, and H. Chen, "RIS-Aided joint channel estimation and localization at mmWave under hardware impairments: A dictionary learning-based approach," in IEEE Transactions on Wireless Communications, vol. 23, no. 12, pp. 19696-19712, Dec. 2024. [IEEEXplore]

Full-duplex integrated sensing and communication

Downlink (DL) signals transmitted by an access point (AP) can reflect off environmental objects and be captured by a colocated receiver, enabling monostatic sensing with full-duplex (FD) transceivers. However, FD operation introduces significant self-interference (SI), necessitating precoder and combiner designs that mitigate SI while supporting ISAC functionalities. To address this, an SI-aware analog full-duplex codebook is proposed in [1], while hybrid precoders/combiners are optimized for joint DL communication and monostatic sensing in [2]-[4]. Expanding on these designs, [5] introduces a framework that integrates joint DL/uplink (UL) communication with monostatic sensing. These optimization problems are highly intractable due to the coupling between precoders/combiners and the non-convexity introduced by hybrid architectures. Thus, advanced techniques such as alternating optimization, convex relaxation, iterative methods, and manifold optimization have been employed in [1]–[5].

[1] M. Bayraktar, C. Rusu, N. González-Prelcic, and H. Chen, "Self-interference aware codebook design for full-duplex joint sensing and communication systems at mmWave," in Proceedings of IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2023, 231-235. [IEEEXplore]

[2] C. B. Barneto, T. Riihonen, S. D. Liyanaarachchi, M. Heino, N. González-Prelcic and M. Valkama, "Beamformer Design and Optimization for Joint Communication and Full-Duplex Sensing at mm-Waves," in IEEE Transactions on Communications, vol. 70, no. 12, pp. 8298-8312, Dec. 2022. [IEEEXplore]

[3] M. Bayraktar and N. González-Prelcic, and H. Chen, "Hybrid precoding and combining for mmWave full duplex joint radar and communication systems under self-interference," in Proceedings of IEEE International Conference on Communications (ICC), 2024. [IEEEXplore] [arXiv]

[4] M. Bayraktar and N. González-Prelcic, H. Chen, and C. J. Zhang, "Near-field full-duplex integrated sensing and communication with dynamic metasurface antennas," to appear in Proceedings of 58th Asilomar Conference on Signals, Systems, and Computers, 2024, pp. 1-5.

[5] M. Bayraktar and N. González-Prelcic, H. Chen, and C. J. Zhang, "Truly full-duplex integrated sensing and single-user communication at mmWave," in Proceedings of IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2024. [IEEEXplore]

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