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Towards 6G: The New Frontier er of W Wireless Communication Systems ystems

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Shih-Chun un Lin (㜿⢓ ⢓懆)

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Assistantt Professor Profess in Networking Group North Car Carolina State University Department Electrical and Computer Engineering ment of E iWN Wireless Networking) Lab WN (Intelligent (Inte Raleigh, NC 27695, USA slin23@ncsu.edu http://www4.ncsu.edu/~slin23/

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Outlines

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1) Towards Digitalization and 6G

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ure 2) Next-Generation Wireless Infrastructure – From Softwarization to Intelligence ence

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3) Deployment Cases: Industryy Verticals Vertic

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4) Bilateral Research and Education Exchange Educa

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5) Catalyst – Accelerate & Broadening Discussion celerate Outreach O

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Digital Government Transformation •

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J. Gray and B. Rumpe, “Models for Digitalization,” tion,” Software Softw & Systems Modeling, Sept. 2015.

Digitaliztion

“What is Digitalization,” IGI Global.

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– Example: smart homes (for entertainment, security, y, childcare, childca electrical, and heating), e-healthcare, smart mobility, smart cities ps augmen – Affect everything from personal relationships augmented by social media and h as how ccitizens interact with support their services, to other relationships such services in e-government

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B. Corydon, V. Ganesan, and M. Lundqvist, “Transforming g Government through Digitization,” McKinsey & Company Article, Nov. 2016.

Governm A. Bertrand, “How Does Digital Government Become Better Government?”, EY, Apr. 2019.

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Digital Government

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“Digital Government Transformation- The Journey to Government’s Gove nt Digital Transformation”, Deloitte.

– Objectives of digitalization: talization:

Source: OECD ECD and the t European Commission

R. Duneja, et al. “Digitalization of Government Services”, Arthur D Little, Nov. 2018.

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Digital Government Transformation (Conti.)

– Provided better governance in delivering public c services

Next-Generation Wireless ss Communications Comm

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澃Ṽ⌺Ṇ, 5GὭṭ!, December 2019.

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1. Provide infrastructure/platform /platform for digital government 2. Enabler: key enabling enablin technologies

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Objectives of 5G Wireless Systems

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I. F. Akyildiz, S. Nie, S.-C. Lin, and M. Chandrasekaran, “5G Roadmap: 10 Key E Enabling Technologies,” Computer Networks (Elsevier) Journal, vol. 106, pp. 17-48, Sept. 2016.

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Evaluation from 5G to 6G

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Major thrusts: • New spectrum usage & radio designs ctrum usa • Noel network etwork architectures a • Increasing creasing intelligence i and automation • Enhancin Enhancing network coverage beyond terrestrial domain terrest

I. F. Akyildiz, A. Kak, and S. Nie, “6G and Beyond: The Future of Wireless Communications Systems,” IEEE Access, May 2020.

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Key motivation for 5G development

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Roughly every 10 years: a new generation release

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Going Towards 6G

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6G directions based on limitatio limitations of 5G • PHY communications: ons: even eve higher bandwidths yet lower power • Industry verticals: support automated vehicles and automation in general cals: supp • Machine learning potential to improve efficiency at different levels ng p R. W. Heath, “Going Toward 6G,” IEEE Signal Processing Magazine, May 2019.

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Key questions when towards 6G:

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How to connect U.S. cutting-edge dge research resea with Taiwan’s national labs (and government overnme agencies)/ industry/ academia? ademia?

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What kinds of resources & interfaces can facilitate the exchange? in erfa

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Our Team: Intelligent Wireless Networking (iWN) Lab

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Research Expertise net • NG wireless networking: B5G, IoT, CPS elligent mobile mo • Intelligent edge computing analyti & machine learning • RF analytic Internet of underground/underwater Things • Interne

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Principal Investigator

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Prof. Shih-Chun n Lin

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Wireless less SDN DN • Md Ferd Ferdous Pervej (PhD) SDR Net N & Edge Control • Rohit Ro Kantheti (PhD) • Wei-Chi Chen (PhD)

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Grads & Undergrad

Deep Learning & RF Sensing • Chia-Hung Lin (PhD) • Anmol Desai (MS)

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43 peer-reviewed d research resea papers Previous success in n commercializing IP • • •

Intelligent Wireless Communication and Networking: 2 patent applications Traffic Engineering for Software-Defined Networks: 3 patents & 1 patent applications Communication & Localization for Internet of Underground Things: 6 patents

From Softwarization to Intelligence

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2) Next-Generation Wireless Networking Architecture

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1. Bring software-defined infrastructure into wireless ss

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2. Incorporate AI/ML/DL into wireless: computation-based wireless edge networks I. F. Akyildiz, S.-C. Lin, and P. Wang, “Wireless Software-defined Networks (W-SDNs) and Network Function Virtualization (NFV) for 5G Cellular Systems: An Overview and Qualitative Evaluation,” Computer Network (Elsevier) Journal, vol. 93, pp. 66-79, December 2015.

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SoftAir Project (NSF: 2015-2018; Huawei HQ: 2012-2016)

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S.-C. Lin, “SoftAir: Software-Defined Networking and Network Function Virtualization Virtualiza Solutions for 5G Cellular Systems,” Ph.D. Thesis, Georgia Institute of Technology, May y 2017.

I. F. Akyildiz, P. Wang, and S.-C. Lin, “SoftAir: A Software Defined Networking Architecture for 5G Wireless Systems,” Computer Networks (Elsevier) Journal, vol. 85, pp. 1-18 1-18, July 2015.

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Intelligent Wireless Communication & Networking Systems

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AI/ML/DL-Based Wireless Communications and Networking etworkin

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Integrate computation into wirelesss communications and networking comm Cloud Æ edge (even on-board) computing computi Physical- (device-level), cyberer- (data & control plane), application-layers Deployment cases: mobile networks, network V2X, UAVs, smart factory, resilient microgrids

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Preliminary results:

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Distributed computing architecture

¾ With 128 MB data ch chuck and Hadoop application, a small-scale computing g testbed provides 53% reduction in average read/write time

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Smart wireless front-end Automatic spectrum analytic – Dynamic Spectrum Access • Intelligent MODEM for mMTC, V2X: Automatic utomatic modulation classification – Security Exploitation and Vulnerability ty Testing

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(MOST International Cooperation Program m in 2020)

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“AI-Based Radio Frequency Analytic with SDR-SDN Architecture,” AFRL Beyond 5G SDR University Showcase, Wright Brothers Institute, Dayton, Ohio, USA, May 3, 2019.

Unsupervised d antenna antenn beamforming – NCTU: Adaptive aptive network resource management and signal processing for intelligent 5G wireless communications

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Machine learning-based network adaptivity

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(Cisco: 2019-2020) Adaptive network policy management – AI-Based Resource Orchestration • Systematically exploit ML techniques into nto edge resource orchestration: maximize throughput/data rate while e guarantee guarante delay

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Distributed privacy-preserved learning earning – Massively distributed, non-IID, ID, and unbalanced characteristics – Tradeoff between computations putat ns and communications – Prevent privacy issues sensitive information ues for se

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In-Network Computation on for Dis Distributed Privacy-Preserving Learning over Wireless Edges, Edge NC State 2020 Faculty Research and Professional Development Program (FRPD), June 2020.

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3) Deployment Cases: Industry Verticals

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NC-CAV: low-latency vehicular edge computing infrastructure

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(NCDOT: 2020-2022)

Core Network with Cloud Computing Anchor Node

Backhaul

Anchor Node (AN)

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Edge Computing SDN Controller

Virtual Clusters ters

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Data Plane

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Smart and Connected Infrastructure: massive operations/platoon erations/p of AVs • Allow large amount of real-time (low-latency)) mobile data processing d • Remove network bandwidth to send all the data back ba and forth to the cloud

Fronthaul Fronthau

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SD-VEC: SD V Software-Defined Vehicular Edge Computing Distributed computing architecture Ultra-low latency vehicular networking Assessing North Carolina readiness for CAVs in traditional and emerging infrastructure needs Verizon: provide 5G connections

Access Point (AP)

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Vehicle

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Simulation Setup for SD-VEC

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Rea Real-world environment: NC State in Raleigh

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U-RAN: UAV-enabled 3D cell deployment

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Objective: • Advance the frontiers of spectral efficiency into access ccess network-level: net spectrum management and sharing • Integrate the computation capability to address dress energy ene efficiency: UAV RAN planning

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Exploit (i) agent & edge computing ng (ii) softwarization/virtualization n (iii) reinforcement learning g + deep networks

URAN: Enabling Spectral ral and Energy Efficient UAV Radio Access Networks using In-Network Computation, NC State 2019 Faculty Research and Professional Development (FRPD), May 2019.

Towards Ultra-Reliable Low-Latency Communications for 5G UAV Ecosystems: Collaborative Research Planning among NC State, NU and AU, AC21 Special Project Fund (SPF), Mar. 2019.

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Smart AgIoT

Communication Infrastructure in Rural Area

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4) Bilateral Exchange Planning: Research and Education Inward: U.S. to Taiwan

Outward: Taiwan an to U.S. U.S

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– Digital/online experimental platform – Naogya Univ., Japan: 1st collaborative workshop op in ICMaSS ICMa 2019 (Harry C. Kelly Memorial Fund, 2019-2020 Internationalization ation Seed Grants) – AC21 Special Project Fund: U.S., Japan,, Australia – Taiwan’s MOST International Cooperation ration Program Pro – IEEE GLOBECOM 2020 • Workshop on V2X Technologies logies and an Advanced Security/Privacy (V2X&ASP) • Invited tutorial talk: Machine and Ultra-Low Latency achin Learning-Enabled Lear Connected Transportation ortation

– Talented M.S. .S. recruiting, recr visiting students/scholars – Consultant forr industry partners: TMYTEK䠄⛸◊⛉ᢏ䠅, TSMC – Leverage existing collaboration in government agencies/labs, DoD SC Lin

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Brain Drain: Taiwan’s Pros and Cons

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“How to Upgrade Current Taiwan Industry into Industry 4.0 (⏗䁊䔉㥔䏥㲨ㆰ⥩Ἴ弰❲ⷌ㥔4.0): 㲨ㆰ⥩Ἴ弰 AI, Big Data, and IoT System Integration,” LiFT Program, MOST, Taipei, pei, Taiwan, June 18, 2019.

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Pros: Software-skill, hardware experience, component mponent design d Cons: System integration, from Software to o AI??

DELTA example: – White-branded products

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What does the industry need, d, i.e., opportunities?

• AI • System architect • Big-data analytics: s: – Machine learning earning vversus optimization • IoT or AIoT: – Customization, e.g., home security system (ADT) SC Lin

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An example of communication testbeds • PoC: iterative and incremental • B2B Æ B2B2X

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The gap between academia and industry

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㛴⋀

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澃Ṽ⌺Ṇ, 5GὭṭ!,, Dec. 2019.

2019

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䴩䫖䔏㈝

㛴⋀ᾂㆰ┭ 婿⛇ 㕟㓁

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How to accelerate outreach & broadening g stage – startup, consulting/visiting, potential resources esources (offices, connections, etc.)

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5) Discussion

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1. How to connect U.S. cutting-edge -edge re research with Taiwan’s national labs (and government nment agencies)/industry/academia? age

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urces/in 2. What kinds of resources/interfaces can facilitate/accelerate the exchange?

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