Research

Localization and Tracking
Specific interests include the following.

  • Localization-of-things in 5G and beyond ecosystems – Develop machine learning approaches, called soft information (SI), with unprecedent performance in complex wireless environments. Design of SI-based localization algorithms that learn the environment and fuse data from heterogeneous sensors. Perform experimentations with ultra-wideband (UWB) radios. Quantifying localization performance in full conformity with the 3GPP specifications by ETSI at both sub-7 GHz and mmWaves.
  • Device-free multi-target tracking, identification, and activity recognition – Establish foundations for device-free localization of multiple targets in cluttered environments. Develop algorithms for integrated tracking and identification of multiple targets and for activity recognition based on reflected signals. Perform experimentation with mmWaves MIMO radar.


Wireless System and Networks
Specific interests include the following.

  • Adaptive diversity communication systems – Design and analysis of adaptive diversity communication systems that provide reliable and efficient operation in wireless environments. Derive a new class of upper and lower bounds on multichannel communication performance with non-ideal channel estimation.
  • Wireless resource optimization – Determine network performance metrics as functions of the wireless resources and nodes deployment for the design and operation of communication and location-aware networks. Develop strategies for deploying assisting (i.e. cooperative) nodes to improve network performance in complex wireless environments. In the framework of 5G and beyond localization, develop network operation strategies for node prioritization, node selection, and node deployment in complex wireless environments.


Stochastic Sensing and Distributed Inference
Specific interests include the following.

  • Multidimensional stochastic sampling – Design and analysis of wireless sensor networks with application to multidimensional signal reconstruction from spatiotemporal stochastic samples. Derive of the optimal interpolator that minimizes the reconstruction error as a function of multidimensional signal characteristics (signal spectrum and spatial correlation) and sampling properties (sensor spatial distribution, sample availability, and sensor position knowledge).
  • Distributed inference – Determine the accuracy of decentralized inference in wireless networks and established both necessary conditions and sufficient conditions for boundedness of the inference error in terms of the nodal sensing and communication capabilities. Develop real-time encoding strategies for generating the information-carrying messages exchanged among different nodes for decentralized inference.


Quantum Information Science
Specific interests include the following.

  • Quantum state engineering – Design and characterization of quantum states, including photon-added and photon-subtracted states, for optimized quantum identification, sensing, and communications. Distribution of entangled parties.
  • Quantum communications – Design of quantum constellations for performance advantage in communications. Develop methods for piggybacking classical information over quantum streams for information labelling and routing. Design uantum key distribution techniques with intermittent relays.



Selected Publications

A. Conti, G. Torsoli, C. A. Gómez-Vega, A. Vaccari, G. Mazzini, and M. Z. Win, “3GPP-compliant datasets for xG location-aware networks,” IEEE Open J. Veh. Tech., 2023, early access

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F. Morselli, S. M. Razavi, M. Z. Win and A. Conti, “Soft Information Based Localization for 5G Networks and Beyond,” IEEE Trans. Wireless Commun., vol. 22, no. 12, pp. 9923-9938, Dec. 2023.

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G. Torsoli, M. Z. Win and A. Conti, “Blockage Intelligence in Complex Environments for Beyond 5G Localization,” IEEE J. Sel. Areas Commun., vol. 41, no. 6, pp. 1688-1701, Jun. 2023, special issue on 3GPP Technologies: 5G-Advanced and Beyond.

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Z. Wang, Z. Liu, Y. Shen, A. Conti, and M. Z. Win, “Location awareness in beyond 5G networks via reconfigurable intelligent surfaces,” IEEE J. Sel. Areas Commun., vol. 40, no. 7, pp. 2011–2025, Jul. 2022, special issue on Integrated Sensing and Communication.

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A. Conti, F. Morselli, Z. Liu, S. Bartoletti, S. Mazuelas, W. C. Lindsey, and M. Z. Win, “Location Awareness in Beyond 5G Networks,” IEEE Commun. Mag., vol. 59, iss. 11, pp. 22-27, Nov. 2021.

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A. Conti, S. Mazuelas, S. Bartoletti, W. C. Lindsey, and M. Z. Win, “Soft Information for Localization-of-Things,” Proc. IEEE, vol. 107, iss. 11, pp. 2240-2264, Nov. 2019.

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S. Bartoletti, A. Giorgetti, M. Z.Win, and A. Conti, “Blind selection of representative observations for sensor radar networks,” IEEE Trans. Veh. Technol., vol. 64, no. 4, pp. 1388–1400, Apr. 2015.

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A. Conti, M. Guerra, D. Dardari, N. Decarli, and M. Z. Win, “Network Experimentation for Cooperative Localization,” IEEE J. Sel. Areas Commun., vol. 30, iss. 2, pp. 467-475, Feb. 2012.

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G. Kwon, A. Conti, H. Park, and M. Z. Win, “Joint communication and localization in millimeter wave networks,” IEEE J. Sel. Topics Signal Process., vol. 15, no. 6, pp. 1439–1454, Nov. 2021, special issue on Joint Communication and Radar Sensing for Emerging Applications.

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G. Chisci, H. ElSawy, A. Conti, M.-S. Alouini, and M. Z. Win, “Uncoordinated massive wireless networks: Spatiotemporal models and multiaccess strategies,” IEEE/ACM Trans. Netw., vol. 27, no. 3, pp. 918–931, Jun. 2019.

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A. Rabbachin, A. Conti, and M. Z.Win, “Wireless network intrinsic secrecy,” IEEE/ACM Trans. Netw., vol. 23, no. 1, pp. 56 – 69, Feb. 2015.

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A. Conti, W. M. Gifford, M. Z.Win, and M. Chiani, “Optimized simple bounds for diversity systems,” IEEE Trans. Commun., vol. 57, no. 9, pp. 2674–2685, Sep. 2009.

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A. Conti, M. Z. Win, and M. Chiani, “Slow adaptive M-QAM with diversity in fast fading and shadowing,” IEEE Trans. Commun., vol. 55, no. 5, pp. 895–905, May 2007.

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Z. Liu, A. Conti, S. K. Mitter, and M. Z. Win, “Communication-efficient distributed learning over networks–Part I: Sufficient conditions for accuracy,” IEEE J. Sel. Areas Commun., vol. 41, no. 4, pp. 1081-1101, Apr. 2023.

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Z. Liu, A. Conti, S. K. Mitter, and M. Z. Win, “Communication-efficient distributed learning over networks–Part II: Necessary conditions for accuracy,” IEEE J. Sel. Areas Commun., vol. 41, no. 4, pp. 1102-1119, Apr. 2023.

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Z. Liu, A. Conti, S. K. Mitter, and M. Z. Win, “Filtering over non-Gaussian channels: The role of anytime capacity,” IEEE Contr. Syst. Lett., vol. 7, pp. 472–477, Jul. 2022.

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G. Chisci, A. Conti, L. Mucchi, and M. Z. Win, “Intrinsic secrecy in inhomogeneous stochastic networks,” IEEE/ACM Trans. Netw., vol. 27, no. 4, pp. 1291–1304, Aug. 2019.

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F. Zabini and A. Conti, “Inhomogeneous poisson sampling of finite-energy signals with uncertainties in Rd,” IEEE Trans. Signal Process., vol. 64, no. 18, pp. 4679–4694, Sep. 2016.

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S. Guerrini, M. Z. Win, and A. Conti, “Photon-varied Quantum States: Unified Characterization ,” Phys. Rev. A, 2023, to appear.

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N. K. Kundu, M. R. McKay, A. Conti, R. K. Mallik and M. Z. Win, “MIMO Terahertz Quantum Key Distribution Under Restricted Eavesdropping,” IEEE Trans. on Quantum Eng., vol. 4, pp. 1-15, Apr. 2023.

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S. Guerrini, M. Z. Win, M. Chiani, and A. Conti, “Quantum discrimination of noisy photon-added coherent states,” IEEE J. Sel. Areas Inf. Theory, vol. 1, no. 2, pp. 469–479, Aug. 2020, special issue on Quantum Information Science.

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M. Chiani, A. Conti, and M. Z. Win, “Piggybacking on quantum streams,” Phys. Rev. A , vol. 102, p.012410, Jul. 2020. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevA.102.012410

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