URLLC

Building the foundations of Ultra-RELIABLE and Low-LATENCY Wireless Communication for Verticals

Ensuring ultra-reliable and low-latency communication (URLLC) for 5G wireless networks and beyond is of capital importance and is currently receiving tremendous attention in academia and industry. At its core, URLLC mandates a departure from expected utility-based network design approaches, in which relying on average quantities (e.g., average throughput, average delay and average response time) is no longer an option. Instead, a principled and scalable framework which takes into account delay, reliability, packet size, network architecture, and topology (across access, edge, and core) and decision-making under uncertainty is sorely lacking.

Within the NOOR project, we are working on the theoretical and algorithmic underpinnings of URLLC with applications to several verticals (V2X, VR, AI, UAV, IIoT, Blockchain). We focus our attention on a plethora of techniques and methodologies pertaining to the requirements of ultra-reliable and low-latency communication, as well as their applications through selected use cases.

Time for Beyond Vanilla URLLC has come! [Vision paper coming soon]

Publications

M. K. Abdel-Aziz, S. Samarakoon, M. Bennis, and W. Saad, "Reliable and Low-Latency Communication in Vehicular Networks with Active Learning using Gaussian Process Regression," IEEE Comm Letter., Nov. 2019, minor revision.
M. Bennis, M. Debbah and H. V. Poor, "Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale," Proceedings of the IEEE, 2018. Available: https://arxiv.org/pdf/1801.01270
M. K. Abdel-Aziz, S. Samarakoon, C.-F. Liu, M. Bennis, and W. Saad, ‘‘Optimized age of information tail for ultra-reliable low-latency communications in vehicular networks,’’ IEEE Trans. Commun., vol. 68, 2020, accepted.
C.-F. Liu and M. Bennis, ‘‘Taming the tail of maximal information age in wireless industrial networks,’’ IEEE Commun. Lett., vol. 23, no. 12, pp. -, Dec. 2019.
M. Alsenwi, N. H. Tran, M. Bennis, A. Kumar Bairagi and C. S. Hong, "eMBB-URLLC Resource Slicing: A Risk-Sensitive Approach," in IEEE Communications Letters, vol. 23, no. 4, pp. 740-743, April 2019.
S. Samarakoon, M. Bennis, W. Saad, M. Debbah, "Federated Learning for Ultra-Reliable Low-Latency V2V Communications. CoRR abs/1805.09253 (2018).
M. S. Elbamby, C. Perfecto, C.-F. Liu, J. Park, S. Samarakoon, X. Chen, and M. Bennis, ‘‘Wireless edge computing with latency and reliability guarantees,’’ Proc. IEEE, vol. 107, no. 8, pp. 1717-1737, Aug. 2019.
M. Khairy, S. Samarakoon, C.F. Liu, M. Bennis, W. Saad, "Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail," in Proc. of the IEEE Globecom 2018, Abu-Dhabi, UAE.
J. Park, M. Bennis, "URLLC-eMBB Slicing to Support VR Multimodal Perceptions over Wireless Cellular Systems," in Proc. of the IEEE Globecom 2018, Abu-Dhabi, UAE..
C.-F. Liu, M. Bennis, "Ultra-Reliable and Low-Latency Vehicular Transmission: An Extreme Value Theory Approach. CoRR abs/1804.06368 (2018).
C.-F. Liu, M. Bennis, and H. V. Poor, “Latency and reliability-aware task offloading and resource allocation for mobile edge computing,” in Proc. IEEE Global Commun. Conf. Workshops , Dec. 2017, pp. 1–7. Available: http://arxiv.org/abs/1710.00590
M. S. ElBamby, C. Perfecto, M. Bennis, K. Doppler, "Toward Low-Latency and Ultra-Reliable Virtual Reality," IEEE Network 32(2): 78-84 (2018).
T. Kien Vu, M. Bennis, M. Debbah, M.Latva-aho, C. S. Hong, "Ultra-Reliable Communication in 5G mmWave Networks: A Risk-Sensitive Approach," CoRR abs/1802.03878 (2018)
T. Kien Vu, C. F. Liu, M. Bennis, M. Debbah, M.Latva-aho, C. S. Hong, "Ultra-Reliable and Low Latency Communication in mmWave-Enabled Massive MIMO Networks. IEEE Communications Letters 21(9): 2041-2044 (2017).

Anatomy of the URLLC building blocks, composed of tail, scale and risk

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