Indoor Environments

The deployment of Device-to-Device (D2D) systems, such as mobile robots or autonomous sensors, in dense urban or confined environments imposes stringent requirements in terms of accurate localization and reliable communication. Conventional solutions based on GNSS, 5G cellular networks, and inertial sensor fusion exhibit limited performance in such contexts, particularly due to Non-Line-of-Sight (NLOS) conditions, severe multipath propagation, and device mobility [1].

In urban and indoor environments, reduced satellite visibility, frequent obstructions, and strong signal reflections significantly degrade the accuracy and robustness of classical D2D localization techniques [2]. These limitations have motivated the exploration of alternative solutions that exploit radio signals beyond their traditional communication functionality.

Integrated Sensing and Communication (ISAC) systems, built upon 5G NR and emerging 6G technologies, have emerged as a promising approach to overcome the shortcomings of conventional methods [3][4]. By reusing communication waveforms for radar sensing purposes, ISAC systems enable the joint extraction of range, velocity, and angular information without dedicated radar hardware, paving the way for more robust localization and integrated environmental perception [5][6].

Pioneering studies have demonstrated the theoretical feasibility of this concept [21][22], leading to the development of several ISAC architectures:

·       Monostatic ISAC, where a single device transmits and receives signals for local sensing, well-suited for self-localization and collision avoidance [23][24];

·       Bistatic/Multistatic ISAC, which exploits distributed configurations to enhance geometric diversity and localization accuracy [25][26];

·       Cooperative ISAC, in which multiple nodes share radar observations for collaborative localization and collective environmental mapping [27][28].

Recent experimental results on 5G NR demonstrators have shown that sub-meter positioning accuracy can be achieved by exploiting radar echoes in the millimeter-wave bands (24–29 GHz) [29][30]. The integration of massive MIMO antenna arrays further enhances angular resolution and multipath separation capabilities [31][32].

However, the state of the art still reveals major limitations for autonomous D2D applications:

·       Static resource allocation, with limited adaptation to channel variations and application requirements [35][36];

·       Limited exploitation of prior knowledge, as current algorithms insufficiently leverage environmental geometric constraints and temporal channel correlations [37][38];

·       Underdeveloped collaborative localization, due to the lack of distributed fusion algorithms and protocols suitable for large-scale D2D scenarios [27][28];

·       Insufficient NLOS robustness, as existing bias mitigation techniques remain ineffective under fast mobility conditions [39][40].

These limitations motivate the development of new ISAC 6G architectures and algorithms, specifically designed to enhance localization and communication performance in complex urban environments, also taking advantage of the recent evolution of D2D/sidelink communication and positioning standardization in 5G-Advanced and upcoming 6G 3GPP releases.

In this context, this PhD thesis aims to design, model, and validate innovative 6G ISAC solutions for D2D localization and communication, based on:

·       the design of novel 6G radar-communication waveforms,

·       the dynamic adaptation of radio resources according to channel conditions and device mobility,

·       and the exploitation of D2D/sidelink cooperation for robust localization in urban and indoor environments, with experimental validation on an indoor robotic platform.

References:

[1] Asadi, A., Wang, Q., & Mancuso, V. (2014). "A survey on device-to-device communication in cellular networks." IEEE Communications Surveys & Tutorials, 16(4), 1801-1819.

[2] Del Peral-Rosado, J. A., Raulefs, R., López-Salcedo, J. A., & Seco-Granados, G. (2018). "Survey of cellular mobile radio localization methods: From 1G to 5G." IEEE Communications Surveys & Tutorials, 20(2), 1124-1148.

[3] Liu, F., Cui, Y., Masouros, C., Xu, J., Han, T. X., Eldar, Y. C., & Buzzi, S. (2022). "Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond." IEEE Journal on Selected Areas in Communications, 40(6), 1728-1767.

[4] Zhang, J. A., Liu, F., Masouros, C., Heath, R. W., Feng, Z., Zheng, L., & Petropulu, A. (2021). "An overview of signal processing techniques for joint communication and radar sensing." IEEE Journal of Selected Topics in Signal Processing, 15(6), 1295-1315.

[5] Chiriyath, A. R., Paul, B., & Bliss, D. W. (2017). "Radar-communications convergence: Coexistence, cooperation, and co-design." IEEE Transactions on Cognitive Communications and Networking, 3(1), 1-12.

[6] Cui, Y., Liu, F., Jing, X., & Mu, J. (2021). "Integrating sensing and communications for ubiquitous IoT: Applications, trends, and challenges." IEEE Network, 35(5), 158-167.

[21] Sturm, C., & Wiesbeck, W. (2011). "Waveform design and signal processing aspects for fusion of wireless communications and radar sensing." Proceedings of the IEEE, 99(7), 1236-1259.

[22] Dokhanchi, S. H., Mysore, B. S., Mishra, K. V., & Ottersten, B. (2019). "A mmWave automotive joint radar-communications system." IEEE Transactions on Aerospace and Electronic Systems, 55(3), 1241-1260.

[23] Kumari, P., Gonzalez-Prelcic, N., & Heath, R. W. (2017). "Investigating the IEEE 802.11ad standard for millimeter wave automotive radar." IEEE Vehicular Technology Conference (VTC), 1-5.

[24] Luong, N. C., Lu, X., Hoang, D. T., Niyato, D., & Kim, D. I. (2019). "Radio resource management in joint radar and communication: A comprehensive survey." IEEE Communications Surveys & Tutorials, 23(2), 780-814.

[25] Liu, Y., Liao, G., Xu, J., Yang, Z., & Zhang, Y. (2020). "Adaptive OFDM integrated radar and communications waveform design based on information theory." IEEE Communications Letters, 24(5), 1093-1096.

[26] Cheng, Z., Liao, B., He, Z., Li, J., & Xie, J. (2022). "Joint design of the transmit and receive beamforming in MIMO radar systems." IEEE Transactions on Vehicular Technology, 71(2), 1891-1905.

[27] Wild, T., Braun, V., & Viswanathan, H. (2021). "Joint design for integrated sensing and communication: A survey." IEEE Open Journal of the Communications Society, 2, 2434-2459.

[28] Wei, Z., Yuan, W., Li, S., Yuan, J., & Ng, D. W. K. (2023). "Transmit signal and bandwidth optimization for multi-target sensing in multi-functional MIMO radar-communication systems." IEEE Transactions on Signal Processing, 71, 1-16.

[29] Grossi, E., Lops, M., & Venturino, L. (2021). "Opportunistic radar in IEEE 802.11ad networks." IEEE Transactions on Signal Processing, 69, 2441-2454.

[30] Daubechies, I., Defrise, M., & De Mol, C. (2004). "An iterative thresholding algorithm for linear inverse problems with a sparsity constraint." Communications on Pure and Applied Mathematics, 57(11), 1413-1457.

[31] Demirhan, U., & Alkhateeb, A. (2022). "Integrated sensing and communication in 6G: Motivations, use cases, and design aspects." IEEE Wireless Communications, 29(6), 160-169.

[32] Pérez-Adán, D., Fresnedo, O., González-Coma, J. P., & Castedo, L. (2021). "Intelligent reflective surfaces for wireless networks: An overview of applications, approached issues, and open problems." Electronics, 10(19), 2345.

[35] Ma, D., Shlezinger, N., Huang, T., Liu, Y., & Eldar, Y. C. (2021). "Joint radar-communication strategies for autonomous vehicles: Combining two key automotive technologies." IEEE Signal Processing Magazine, 37(4), 85-97.

[36] Liu, F., Masouros, C., Petropulu, A. P., Griffiths, H., & Hanzo, L. (2020). "Joint radar and communication design: Applications, state-of-the-art, and the road ahead." IEEE Transactions on Communications, 68(6), 3834-3862.

[37] Wymeersch, H., Seco-Granados, G., Destino, G., Dardari, D., & Tufvesson, F. (2017). "5G mmWave positioning for vehicular networks." IEEE Wireless Communications, 24(6), 80-86.

[38] Garcia, N., Wymeersch, H., Larsson, E. G., Haimovich, A. M., & Coulon, M. (2017). "Direct localization for massive MIMO." IEEE Transactions on Signal Processing, 65(10), 2475-2487.

[39] Marano, S., Gifford, W. M., Wymeersch, H., & Win, M. Z. (2010). "NLOS identification and mitigation for localization based on UWB experimental data." IEEE Journal on Selected Areas in Communications, 28(7), 1026-1035.

[40] Chen, L., Yang, K., & Wang, X. (2020). "Robust cooperative Wi-Fi fingerprint-based indoor localization." IEEE Internet of Things Journal, 7(7), 6972-6983.