Integrated Systems

Our Integrated Systems department innovates, designs, and prototypes high-performance intelligent distributed systems, applications, and services on complex, large-scale communication networks like 5G and beyond. We develop next-generation wireless technologies for sensing the world, localizing critical assets, and improving the capacity, coverage, and scalability of communication networks like 5G and beyond.

New application needs have always sparked human innovation. A decade ago, cloud computing enabled high-value enterprise services with a global reach and scale but with several minutes or seconds of delay. Large-scale services like enterprise resource planning (ERP) were a corner-case scenario, often designed as one-off systems. Today, applications like social networks, automated trading, and video streaming have made large-scale services the norm rather than the exception. In the future, advances in 5G networks and an explosion in smart devices, microservices, databases, networking, and computing tiers will make services so complex that humans cannot tune or manage them.

The sheer scale, dynamic nature, and concurrency in services on 5G slices will require them to be intelligent and autonomic. They will need to continuously self-assess, learn, and automatically adjust for resource needs, data quality, and service reliability. The need for increased efficiency and reduced latency between measurement and action drives our design of real-time distributed systems for feature extraction, computation, and machine learning on multimodal streaming data. We are conducting extensive research on creating end-to-end solutions using multimodal sensing technologies in the retail, public safety, and transportation domains.

Our 5G cellular network research encompasses the development of technologies on the Radio Access Network (RAN), the mobile edge, and the 5G LAN. Within the RAN, we are developing technologies that optimize massive MIMO/MU-MIMO deployments and millimeter-wave access (e.g., transmission at 28 GHz to nomadic/mobile users). At the mobile edge (MEC), we focus on virtualization, scalability, and cloud deployment of appropriate services. Our 5G LAN research extends the benefits of 5G slicing technology to enterprise LANs to position the enterprise as the new MEC.

Read our news and publications from our world-class team of researchers from our Integrated Systems department.

Posts

Opportunistic Temporal Fair Mode Selection and User Scheduling for Full-duplex Systems

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In-band full-duplex (FD) communications – enabled by recent advances in antenna and RF circuit design – has emerged as one of the promising techniques to improve data rates in wireless systems. One of the major roadblocks in enabling high data rates in FD systems is the inter-user interference (IUI) due to activating pairs of uplink and downlink users at the same time-frequency resource block. Opportunistic user scheduling has been proposed as a means to manage IUI and fully exploit the multiplexing gains in FD systems. In this paper, scheduling under long-term and short-term temporal fairness for single-cell FD wireless networks is considered. Temporal fair scheduling is of interest in delay-sensitive applications, and leads to predictable latency and power consumption. The feasible region of user temporal demand vectors is derived, and a scheduling strategy maximizing the system utility while satisfying long-term temporal fairness is proposed. Furthermore, a short-term temporal fair scheduling strategy is devised which satisfies user temporal demands over a finite window-length. It is shown that the strategy achieves optimal average system utility as the window-length is increased asymptotically. Subsequently, practical construction algorithms for long-term and short-term temporal fair scheduling are introduced. Simulations are provided to verify the derivations and investigate the multiplexing gains. It is observed that using successive interference cancellation at downlink users improves FD gains significantly in the presence of strong IUI.

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Beam Training Optimization in Millimeter-wave Systems under Beamwidth, Modulation and Coding Constraints

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Millimeter-wave (mmWave) bands have the potential to enable significantly high data rates in wireless systems. In order to overcome intense path loss and severe shadowing in these bands, it is essential to employ directional beams for data transmission. Furthermore, it is known that the mmWave channel incorporates a few number of spatial clusters necessitating additional time to align the corresponding beams with the channel prior to data transmission. This procedure is known as beam training (BT). While a longer BT leads to more directional beams (equivalently higher beamforming gains), there is less time for data communication. In this paper, this trade-off is investigated for a time slotted system under practical constraints such as finite beamwidth resolution and discrete modulation and coding schemes. At each BT time slot, the access point (AP) scans a region of uncertainty by transmitting a probing packet and refines angle of arrival (AoA) estimate based on user equipment (UE) feedback. Given a total number time slots, the objective is to find the optimum allocation between BT and data transmission and a feasible beamwidth for the estimation of AoA at each BT time slot such that the expected throughput is maximized. It is shown that the problem satisfies the optimal substructure property enabling the use of a backward dynamic programming approach to find the optimal solution with polynomial computational complexity. Simulation results reveal that in practical scenarios, the proposed approach outperforms existing techniques such as exhaustive and bisection search.

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Robust Beam Tracking and Data Communication in Millimeter Wave Mobile Networks

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Millimeter-wave (mmWave) bands have shown the potential to enable high data rates for next generation mobile networks. In order to cope with high path loss and severe shadowing in mmWave frequencies, it is essential to employ massive antenna arrays and generate narrow transmission patterns (beams). When narrow beams are used, mobile user tracking is indispensable for reliable communication. In this paper, a joint beam tracking and data communication strategy is proposed in which, the base station (BS) increases the beamwidth during data transmission to compensate for location uncertainty caused by user mobility. In order to evade low beamforming gains due to widening the beam pattern, a probing scheme is proposed in which the BS transmits a number of probing packets to refine the estimation of angle of arrival based on the user feedback, which enables reliable data transmission through narrow beams again. In the proposed scheme, time is divided into similar frames each consisting of a probing phase followed by a data communication phase. A steady state analysis is provided based on which, the duration of data transmission and probing phases are optimized. Furthermore, the results are generalized to consider practical constraints such as minimum feasible beamwidth. Simulation results reveal that the proposed method outperforms well-known approaches such as optimized beam sweeping.

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TrackIO: Tracking First Responders Inside-Out

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First responders, a critical lifeline of any society, often find themselves in precarious situations. The ability to track them in real-time in unknown indoor environments would significantly contribute to the success of their mission as well as their safety. In this work, we present the design, implementation and evaluation of TrackIO–a system capable of accurately localizing and tracking mobile responders real-time in large indoor environments. TrackIO leverages the mobile virtual infrastructure offered by unmanned aerial vehicles (UAVs), coupled with the balanced penetration-accuracy tradeoff offered by ultra-wideband (UWB), to accomplish this objective directly from outside, without relying on access to any indoor infrastructure. Towards a practical system, TrackIO incorporates four novel mechanisms in its design that address key challenges to enable tracking responders (i) who are mobile with potentially non-uniform velocities (e.g. during turns), (ii) deep indoors with challenged reachability, (iii) in real-time even for a large network, and (iv) with high accuracy even when impacted by UAV’s position error. TrackIO’s real-world performance reveals that it can track static nodes with a median accuracy of about 1–1.5m and mobile (even running) nodes with a median accuracy of 2–2.5m in large buildings in real-time.

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SkyRAN: A Self-Organizing LTE RAN in the Sky

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We envision a flexible, dynamic airborne LTE infrastructure built upon Unmanned Autonomous Vehicles (UAVs) that will provide on-demand, on-time, network access, anywhere. In this paper, we design, implement and evaluate SkyRAN, a self-organizing UAV-based LTE RAN (Radio Access Network) that is a key component of this UAV LTE infrastructure network. SkyRAN determines the UAV’s operating position in 3D airspace so as to optimize connectivity to all the UEs on the ground. It realizes this by overcoming various challenges in constructing and maintaining radio environment maps to UEs that guide the UAV’s position in real-time. SkyRAN is designed to be scalable in that it can be quickly deployed to provide efficient connectivity even over a larger area. It is adaptive in that it reacts to changes in the terrain and UE mobility, to maximize LTE coverage performance while minimizing operating overhead. We implement SkyRAN on a DJI Matrice 600 Pro drone and evaluate it over a 90 000 m2 operating area. Our testbed results indicate that SkyRAN can place the UAV in the optimal location with about 30 secs of a measurement flight. On an average, SkyRAN achieves a throughput of 0.9 – 0.95X of optimal, which is about 1.5 – 2X over other popular baseline schemes.

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SkyCore: Moving Core to the Edge for Untethered and Reliable UAV-based LTE Networks

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The advances in unmanned aerial vehicle (UAV) technology have empowered mobile operators to deploy LTE base stations (BSs) on UAVs, and provide on-demand, adaptive connectivity to hotspot venues as well as emergency scenarios. However, today’s evolved packet core (EPC) that orchestrates the LTE RAN faces fundamental limitations in catering to such a challenging, wireless and mobile UAV environment, particularly in the presence of multiple BSs (UAVs). In this work, we argue for and propose an alternate, radical edge EPC design, called SkyCore that pushes the EPC functionality to the extreme edge of the core network – collapses the EPC into a single, light-weight, self-contained entity that is co-located with each of the UAV BS. SkyCore incorporates elements that are designed to address the unique challenges facing such a distributed design in the UAV environment, namely the resource-constraints of UAV platforms, and the distributed management of pronounced UAV and UE mobility. We build and deploy a fully functional version of SkyCore on a two-UAV LTE network and showcase its (i) ability to interoperate with commercial LTE BSs as well as smartphones, (ii) support for both hotspot and standalone multi-UAV deployments, and (iii) superior control and data plane performance compared to other EPC variants in this environment.

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ELI: Empowering LTE with Interference Awareness in Unlicensed Spectrum

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The advent of LTE into the unlicensed spectrum has necessitated the understanding of its operational efficiency when sharing spectrum with different radio access technologies. Our study reveals that LTE, owing to its inherent transmission characteristics, suffers significant performance degradation in the presence of interference caused by hidden terminals. This motivates the need for interference-awareness in LTE’s channel access in unlicensed spectrum. To address this problem, we propose ELI. ELI’s three-pronged solution equips the LTE base station with novel techniques to: (a) accurately detect and measure interference caused by hidden terminals, (b) collect interference statistics from clients across different channels with affordable overhead, and (c) leverage interference-awareness to improve its channel access performance. Our evaluations show that ELI can achieve 1.5-2x throughput gains over baseline schemes. Finally, ELI is LTE-LAA/MulteFire-standard compliant and can be deployed over the existing LTE-LAA implementation without any modifications.

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SkyLiTE: End-to-End Design of Low-altitutde UAV Networks for Providing LTE Connectivity

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Un-manned aerial vehicle (UAVs) have the potential to change the landscape of wide-area wireless connectivity by bringing them to areas where connectivity was sparing or non-existent (e.g. rural areas) or has been compromised due to disasters. While Google’s Project Loon and Facebook’s Project Aquila are examples of high-altitude, long-endurance UAV-based connectivity efforts in this direction, the telecom operators (e.g. AT&T and Verizon) have been exploring low-altitude UAV-based LTE solutions for on-demand deployments. Understandably, these projects are in their early stages and face formidable challenges in their realization and deployment. The goal of this document is to expose the reader to both the challenges as well as the potential offered by these unconventional connectivity solutions. We aim to explore the end-to-end design of such UAV-based connectivity networks particularly in the context of low-altitude UAV networks providing LTE connectivity. Specifically, we aim to highlight the challenges that span across multiple layers (access, core network, and backhaul) in an inter-twined manner as well as the richness and complexity of the design space itself. To help interested readers navigate this complex design space towards a solution, we also articulate the overview of one such end-to-end design, namely SkyLiTE– a self-organizing network of low-altitude UAVs that provide optimized LTE connectivity in a desired region.

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