NYU WIRELESS researchers realize that a composite well-designed wireless-wireline network is essential for today’s networks. We are working on optimizing networks for low latency, high reliability and high capacity, while minimizing costs. With the emergence of low latency applications like AR/VR, vehicular communications, robotic and haptic communications, all made possible by the emergence of 5G millimeter wave (mmWave) and future 6G Terahertz wireless communications and sensing, mobile edge computing is emerging as an essential component of wireless communications. By bringing applications to the network edge, close to users and end devices, millisecond end-to-end delays are now feasible. We are working on advanced caching of data at the edge, using machine learning based predictive techniques. These will be complemented by low delay transport layer mechanisms using advanced delay sensitive congestion control mechanisms targeted for such applications. SDN mechanisms can be used to redirect and reroute traffic on the fly as traffic hot spots appear. Given the prevalence of small cells and the challenge of exploiting macrodiversity among base stations to provide reliable wireless links, it is expected that handoffs will occur orders of magnitude more frequently than in legacy networks. To meet this challenge, we are developing advanced fast handoff mechanisms with zero data traffic interruptions.

The potential of doubling the value of scarce spectrum has driven the recent interest in full duplex (FD) communications. We are examining how this can be realized in a cellular network, using techniques that range from fundamental information theoretic techniques to the innovative use of FD relays to deliver capacity doubling even with asymmetric uplink/downlink traffic.

We are also examining the economics of spectrum and infrastructure sharing by wireless service providers. This thrust provides new insight by combining communication theory with economics theory in an innovative way to provide insights into when sharing lead to a profitable win-win situation for service providers.

Legacy congestion control protocols


stochastic

Legacy congestion control protocols including TCP and its variants, are known to perform poorly over cellular networks due to highly variable capacities over short time scales, self-inflicted packet delays, and packet losses unrelated to congestion. Three specific characteristics directly impact the unpredictability of cellular channels.

First, the state of a cellular channel between a mobile device and a base station undergoes several complex state transitions that affect channel availability in short time scales; this introduces variability in the underlying channel.

Second, the frame scheduling algorithms used in cellular networks cause burstiness in the cellular channel. Based on real-world cellular measurements, we observe that the typical traffic characteristics at a receiver are bursty (even for smooth source transmission patterns), with variable burst sizes and burst inter-arrival periods.

Third, while prior work has considered only self-inflicted queuing delay as a cause for high delays, we find that competing traffic does affect end-to-end delay characteristics, especially under high contention or when the cellular channel is near saturation.

Finally, device mobility has a substantial impact on channel characteristics that further compounds these challenges. The lack of channel predictability has important implications on the design of new congestion control protocols. The large bandwidth coupled with connectivity impairments associated with mmWave channels will qualitatively change the nature of congestion control.

Improved Network Performance Over 5G mmWave Cellular


In this project we aim at designing a transport layer protocol optimized for the mmWave access network, and for the new class of applications that it will enable, aiming to work seamlessly across a connection consisting of both wireline and wireless segments.In our recent ICC paper submission “The Bufferbloat Problem over Intermittent Multi-Gbps mmWave Links”, we have proposed a simple approach to solve some of the major issues related to TCP over mmWave that we observed in our previous publication: “Transport layer performance in 5G mmWave cellular”. Our solution, namely Dynamic RW, delivers high throughput while guaranteeing low latency.

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Current Research


Conference Papers


CitationResearch AreasDate

Chenge Li, Gregory Dobler, Yilin Song, Xin Feng, Yao Wang “TrackNet: TrackNet: Simultaneous Detection and Tracking of Multiple Objects”.

Computer Vision, mobile edgeSeptember 14, 2018

Liyang Sun, Guibin Tian, Guanyu Zhu, Yong Liu, Hang Shi, and David Dai,
“Multipath IP Routing on End Devices: Motivation, Design, and Performance”,
in the Proceedings of IFIP Networking 2018 Conference, May 2018.

Computer Networks, mobile edgeMay 1, 2018

R. Kumar, A. Francini, S. Panwar, and S. Sharma, “Dynamic Control of RLC Buffer Size for Latency Minimization in Mobile RAN,” in Proc. of IEEE WCNC, Apr. 2018.

Distributed Core, mobile edgeJanuary 24, 2018

Bo Yan, Shu Shi, Yong Liu, Weizhe Yuan, Haoqin He, Rittwik Jana, Yang Xu, and H.Jonathan Chao, “LiveJack: Integrating CDNs and Edge Clouds for Live Content Broadcasting”, in the Proceedings of ACM Multimedia, October 2017.

mobile edge, videoOctober 1, 2017

R. Ford, S. Rangan, E. Mellios, D. Kong and A. Nix, “Markov Channel-Based Performance Analysis for Millimeter Wave Mobile Networks,” 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, 2017, pp. 1-6.

Dynamic Channel Models, mobile edge, ns3June 1, 2017

S. Xu, P. Liu, and S. Panwar “Exploiting network similarity for latency prediction of edge devices,” 2017 IEEE International Conference on Communications (ICC), Paris, France, May 2017​.​

mobile edgeMay 1, 2017

M. Polese, R. Jana, M. Zorzi, “TCP in 5G mmWave Networks: Link Level Retransmissions and MP-TCP,” to be presented at 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Atlanta, USA, May 2017.

mobile edge, multi connectivity handoverApril 4, 2017

M. Polese, M. Giordani, M. Mezzavilla, S. Rangan, M. Zorzi, “Improved Handover Through Dual Connectivity in 5G mmWave Mobile Networks,” submitted to 2017 IEEE JSAC Special Issue on mmWave.

mobile edge, multi connectivity handoverApril 4, 2017

R. Ford, A. Sridharan, R. Margolies, R. Jana, S. Rangan “Provisioning Low Latency, Resilient Mobile Edge Clouds for 5G” arXiv:1703.10915 [cs.NI].

Distributed Core, mobile edgeMarch 31, 2017

M. Polese, M. Mezzavilla, S. Rangan, M. Zorzi, “Mobility Management for TCP on mmWave Networks”, mmNets 2017.

MmWave cellular system design, mmWave Channel Modeling, mobile edgeJanuary 13, 2017

M. Polese, M. Zhang, M. Mezzavilla, J. Zhu, S. Rangan, S. Panwar, M. Zorzi, “A Split TCP Proxy Architecture for 5G mmWave Cellular Systems”, Asilomar 2017.

MmWave cellular system design, mobile edgeJanuary 13, 2017

S. Dutta, M. Mezzavilla, R. Ford, M. Zhang, S. Rangan, M. Zorzi, “MAC Layer Frame Design for Millimeter Wave Cellular System”, Proc. IEEE European Conference on Networks and Communications (EuCNC), Athens, 2016, pp. 117-121.

high speed mmwave mac, mmWave MAC, ns3June 30, 2016

R. Ford, M. Zhang, M. Mezzavilla, S. Dutta, S. Rangan, M. Zorzi, Achieving Ultra-Low Latency in 5G Millimeter Wave Cellular Networks, IEEE Communications Magazine 55.3 (2017): 196-203.

Congestion Control, high speed mmwave mac, High-speed, networking, Millimeter Wave 5G Prototype, mmWave MAC, ns3February 23, 2016

Journal Articles


CitationResearch AreasDate

I. K. Jain, R. Kumar, S. Panwar, “Driven by Capacity or Blockage? A Millimeter Wave Blockage Analysis,” IEEE ITC30, Sep. 2018.

MmWave cellular system design, mmWave Channel Modeling, mobile edgeSeptember 1, 2018

I. K. Jain, R. Kumar, S. Panwar, “Can Millimeter Wave Cellular Systems provide High Reliability and Low Latency? An analysis of the impact of Mobile Blockers,” e-print in arXiv.org:1807.04388, Jul. 2018.

MmWave cellular system design, mmWave Channel Modeling, mobile edgeJuly 1, 2018

R. Kumar, R. Margolies, R. Jana, Y. Liu, S. Panwar, “WiLiTV: Reducing Live Satellite TV Costs using Wireless Relays”, in IEEE Journal on Selected Areas in Communications, February, 2018.

5g and 6g apps, Distributed Core, mobile edge, terahertzFebruary 1, 2018

M. Polese, M. Giordani, M. Mezzavilla, S. Rangan, M. Zorzi, “Improved Handover Through Dual Connectivity in 5G mmWave Mobile Networks,” in IEEE Journal on Selected Areas in Communications, vol.PP, no.99, pp.1-1, June 2017.

mobile edge, Spectrum SharingJune 27, 2017

P. Hassanzadeh, A. Tulino, J. Llorca, E. Erkip “Rate-Memory Trade-off for the Two-User Broadcast Caching Network with Correlated Sources”, in Proc. IEEE International Symposium Information Theory (ISIT), June 2017.

Cache-Aided Wireless Networks, mobile edgeJune 1, 2017

Z. Cao, S. Panwar, M. Kodialam, T. Lakshman, “Enhancing Mobile Networks With Software Defined Networking and Cloud Computing,” in IEEE/ACM Transactions on Networking , vol.PP, no.99, pp.1-14.

Distributed Core, mobile edgeApril 11, 2017

R. Ford, M. Zhang, M. Mezzavilla, S. Dutta, S. Rangan and M. Zorzi, “Achieving Ultra-Low Latency in 5G Millimeter Wave Cellular Networks,” in IEEE Communications Magazine, vol. 55, no. 3, pp. 196-203, March 2017.

Distributed Core, high speed mmwave mac, mmWave MAC, mobile edgeMarch 3, 2017

S. Dutta, M. Mezzavilla, R. Ford, M. Zhang, S. Rangan, M. Zorzi, “Frame Structure Design and Analysis for Millimeter Wave Cellular Systems”, IEEE Transactions on Wireless Communications 16.3 (2017): 1508-1522.

high speed mmwave mac, MmWave cellular system design, mmWave MAC, ns3, terahertzJanuary 4, 2017