CEWiT LTE and 5GNR [175] |  IITMadras Research Park, Chennai, India |  \- 2 types of testbeds based on: 1) CEWiT hardware 2) TI’s multi-core DSPs \- Hardware is made using SDR radio nodes \- LTE PHY for UE and eNB has been developed in collaboration with IITM \- Basic implementation of LTE L1 downlink and uplink chains \- L2 MAC, RLC and a thin layer of PDCP \- Both eNodeB and UE implementations \- End-to-end IP application flow both in DL, UL \- Supports 3GPP Release 8 specifications \- Supports up to 10 MHz bandwidth and can be extended to 20MHz \- 5G NR for sub 6GHz and mm wave under development
TitanMIMO-6 [176] [177] |  Nutaq, Québec, Canada |  \- Sub 6 GHz wideband Massive MIMO testbed \- FDD+TDD capabilities \- Up to 56 MHz real-time baseband processing \- Radio tumble up to 5 GHz \- Nutaq’s SDR systems (PicoSDR) can be combined with TitanMIMO system to build up complete HetNet, MUMIMO or CRAN testbed solutions \- Enabling evaluation of interoperability behavior for various deployment scenarios
Aalto 5G research infrastructure [178] |  Otaniemi, Espoo, Finland |  \- Network slicing \- Support for NB-IOT to be used for IoT hackathon \- Mobile and edge computing, VR/AR, Gaming, Industrial Internet \- Part of 5G TNF
University of Helsinki Test Network [179] |  University of Helsinki, Kumpula campus, (Exactum building), Finland |  \- 17 Nokia Flexi Zone Indoor Pico BTS (eNBs) \- Band: 2600 MHz (E-UTRA 7) FDD \- Sync: 1588v2 (PTP) / GPS / Sync-E \- 3 connections to cores through VLANs: UH core(s), Aalto core and Nokia core \- Part of 5G TNF
VodaPhone Chair [180] [181] [182] |  TU Dresden, Germany |  \- Online Wireless Lab (OWL) testbed \- Software Defined Reconfigurable Radio Devices \- LabVIEW/LVC in combination with USRPs \- Many projects and startups, e.g., 5G Lab Germany, 5GNetMobil, 5G Picture, HPE-5G-Testbed, Airrays GmbH [183]
CORNET [184] [185] |  Virginia Tech University, USA |  \- University-wide testbed \- Software-defined radios, cognitive radio and dynamic spectrum access \- 48 indoor SDR nodes, 14 fixed outdoor nodes, 6 mobile units (O-CORNET) \- A few LTE-capable nodes (LTE-CORNET) \- CORNET nodes are remotely accessible \- Awarded the grant from DURIP for upgrading to LTE and LTE-A \- Outdoor network of 15 radio nodes and 2 mobile nodes
5G Playground [186] |  Fraunhofer FOKUS and TU Berlin campus, Germany |  \- Empowers the 5G Berlin testbed \- Support for multi-slicing \- Ultra-reliable, low latency communication in Industrial IoT lab of FOKUS \- Automotive testbed environment in underground parking of FOKUS building \- Coverage of dense urban areas, like portable 5G edge nodes in progress \- 3 Toolkits: Open5GCore, OpenSDNCore and Open5GMTC
Tampere University Wireless Test Networks [187] [188] |  Tampere University, Hervanta, Finland |  \- Part of 5G TNF \- FDD-LTE operating at band 1, 7, and 28 for mostly indoor coverage \- TDD-LTE operating at band 38 to provide campus wide outdoor test network \- Upcoming outdoor 5G test network in band n78 with 60 MHz channel \- LoRa: Digita’s LoRaWAN test network in ISM band at 868 MHz
FUSECO Playground [189] |  Fraunhofer FOKUS Institute, Berlin, Germany |  \- Open IMS Core solution \- Heterogeneous indoor and outdoor radio access technologies \- DSL/WLAN/2G/3G/4G-LTE/LTE-A and soon 5G \- M2M communication, IoT, sensor networks \- SDN/OpenFlow, NFV cloud environments \- Toolkits: Open5GCore, OpenSDNCore and Open5GMTC, OpenMTC, Open Source IMS Core, OpenStack-based Cloud Testbed, OpenXSP
5G Ready Trial Platform [190] |  Fraunhofer FOKUS, Berlin, Germany |  \- Consolidated turn-key solution of the Fraunhofer FOKUS software components \- Addresses trial needs of emerging network infrastructures - \- Edge Instantiation: solution for micro-operators and local networks, provides customized IoT connectivity for x100 devices. \- Data Center Instantiation: multi-slice environment, support for multiple parallel instances of IoT and multimedia communication \- Technology Elements: Virtual Core network, Network slicing, IoT support, Low delay network, Dynamic spectrum access and management
Ericsson 5G [191] [192] |  Ericsson, Stockholm, Sweden |  \- Live testing of key capabilities, such as multipoint connectivity with distributed MIMO and 5G-LTE dual connectivity \- 5G devices and base stations operate in 15 GHz band \- TDD and OFDM \- Up to 256 QAM modulation in downlink and up to 64 QAM in the uplink \- mm-Wave testbeds 15 GHz and 28 GHz \- Bandwidth is 80 MHz, centered at 3.5 GHz \- Massive MIMO antenna array of 128 cross-polarized antennas
SK Telecom 5G Playground [193] [194] [195] |  SK Telecom R&D Center, Bundang, Korea |  \- Developing a centimeter-wave (cmWave) 5G radio system with Nokia \- 5G 3D system level simulator with Nokia and Ericsson \- 3D beamforming techniques with large scale array antennas with Samsung \- Developing Anchor-Booster Cell and Massive MIMO with C-RAN with Intel \- Achieved 19.1Gbps transmission speed over the air \- Futuristic services including 4K live broadcast system and AR/VR
5GTN (Linnanmaa) [196] [197] [198] |  University of Oulu and VTT Technical Research Centre of Finland |  \- Multi-access edge computing \- Core network in cloud environment \- Cloud systems for applications \- Secure connection to other 5G sites worldwide, 10 Gb VPN \- Part of 5G TNF
TurboRAN [199] |  AI4Networks Research Center, University of Oklahoma, Tulsa, USA |  \- Developing first end to end programmable cellular test bed for enabling AI based SON research towards 5G and beyond \- Complete integrated mobile cellular network over 300,000 $\rm m^{2}$ area \- Tier 1: 4 outdoors macro cells on 1.2-6 GHz HF band \- Tier 2: 16 small cells (programmed to pico or femto cells). 8 small cells can operate on the HF band, other 8 can operate on the unlicensed mmWave \- Both tier cells are programmable \- Both tier cells connected to EPCs and a big data processing Hadoop cluster \- Hadoop cluster: 1 high performance master node, 15 slave nodes with high-capacity data modems \- Support both high mobility and low mobility users
OAI [200]-[203] |  EURECOM, France |  \- Open-source platform \- 8-node testbed, equipped OAI compatible RF front-ends, UEs and VMs \- 4 machines that can be used for running OAI as eNodeB \- 4 nodes that are equipped with COTS UEs \- 2 physical layer emulation modes \- 64 antenna Massive MIMO testbed .
Munich [204] [205] |  TU Munich, Munchen, Germany |  \- 5G RAN with two sectors, each having carrier frequency: 3.4 GHz, bandwidth: 40 MHz, transmission power: 5 W antennas: up to 8 \- 5G Mobile Terminals with vehicular speeds up to 50 km/h, enablingV2X \- 5G Core network: HW/SW platform \- Hardware: in-house platform of several dozen servers representing a data centre \- Software: extended network emulators, controllers, open-source and proprietary switch implementations \- Testbed can deploy virtual networks with different topologies as needed \- 5G Core network supporting functional split – SDN – NFV Orchestration \- Distributed data centres for mobile edge computing use cases
Perform Networks [206] [207] [208] |  University of Malaga, Spain |  \- T2010 conformance testing units by Keysight Technologies \- LTE release 8 small cells (Pixies) by Athena Wireless working on band 7 \- Polaris Core Network Emulator \- Several LTE UEs, working on different bands \- ExpressMIMO2 and USRP SDR cards \- SIM cards from an Spanish LTE operator to be used on commercial deployments
Centria’s Test Network [209] |  Centria University of Applied Sciences,Ylivieska, Finland |  \- TDD-LTE operating at band 40 and 42 for both outdoor and indoor coverage \- Upcoming 5G test network in band n78 with 60 MHz channel outdoor network \- Implementation plan of first 5G Non-Standalone during 2019 \- Later 5G Standalone during 2020 \- Part of 5G TNF
w-iLab.t [210] [211] [212] |  Ghent and Zwijnaarde, Belgium |  \- w-iLab.t Office testbed: three 90 m x 18 m floors of iMinds office in Ghent \- w-iLab.t Zwijnaarde testbed: 5 km away from w-iLab.t Office in Zwijnaarde \- Sensor nodes, Wi-Fi based nodes, sensing platforms, and cognitive radio \- Heterogeneous wireless/wired experiments \- Virtual Walls: Virtual Wall 1 and 2 containing 206 and 159 nodes respectively \- OpenFlow experiments \- 20 programmable moving robots
5TONIC [213] |  Madrid, Spain |  \- 9 members: Telefonica, Institute IMDEA Networks, Ericsson, Intel, Commscope, Universidad Carlos III de Madrid, Cohere Technologies, Artesyn Embedded Technologies and InterDigital \- NFV orchestrator, implemented with Open Source MANO (OSM) \- Dedicated NFVI for 5GINFIRE: 3 server computers, each with six cores, 32GB of memory, 2TB NLSAS, network card with 4 GbE ports, DPDK support \- Second NFVI: 2 high-profile servers, each equipped with eight cores in a NUMA architecture, 128GB RDIMM RAM, 4TB SAS and eight 10Gbps Ethernet optical transceivers with SR-IOV capabilities
University of Bristol 5G [214] |  University of Bristol, England |  \- Multi-site network connected through a 10 km fibre \- Core network is located at HPN Lab at the University of Bristol \- Extra edge computing node is available at Watershed \- Access technologies are located at Millennium Square for outdoor coverage and “We The Curious” science museum for indoor coverage \- Multi-vendor SDN enabled packet switched network \- SDN enabled optical (Fibre) switched network \- Nokia 4G and 5G NR \- Self-organising multipoint-to-multipoint wireless mesh network \- LiFi Access point, Cloud and NFV hosting \- 2 different NFV orchestration and management solutions: Open Source MANO , NOKIA CloudBand \- 2 cloud/edge computing solutions:Openstack Pike, Nokia MEC \- 1 SDN controller: NetOS
D-15 Labs [215] |  Ericsson, Santa Clara, CA, USA |  \- Validation and development platform for 5G use-cases, leverages cloud edge support, core network, and AI-based management and orchestration
ENCQOR 5G [216] |  Ontario Region, Canada |  \- iPaaS Services: 5G connectivity of 5 Gbps Mobile Throughput and sub 5ms latency, cloud services of IoT Accelerator, emulation cloud, edge computing \- iPaaS Infrastructure: 5G mobile user equipment (android-based Qualcom terminals operating at 3.5 GHz), 5G radio access technology (NR/LTE/CAT-M1/NB-IoT), 5G transport/backhaul, distributed core network and programmable data plane \- Future features expected by 2021 include: 5 Gbps 5G NR, sub 5ms latency, predictive analytics, federated network slicing, real time machine learning / AI \- Technology partners: Ericsson, Thales, CGI, IBM, Ciena
TABLE IV: Review of modeling techniques for handling scarce datasets in Radio Access Networks (RAN). |  |  |  |  |  |
---|---|---|---|---|---|---
Reference |  Year |  Modeling technique |  Use case and data |  Data type |  Use-case type w.r.t. OSI layer |  Use-case type w.r.t. level of analysis
[83] |  2020 |  Few-shot learning |  eNodeB performance metric analysis using cell trace data |  Tabular data |  Network |  System
[84] |  2021 |  Few-shot learning |  Modeling indoor pathloss model at 28 GHz using RSS data |  Tabular data |  Physical |  Link
[144] |  2020 |  Few-shot learning + Transfer learning |  Network power minimization in C-RAN for resource management using UE SINR data |  Tabular data |  Physical |  System
[89] |  2019 |  Transfer learning |  Identifying optimal deployment density of the BSs given a BS transmit power w.r.t. spectral and energy efficiency of the network using UE SINR data |  Tabular data |  Phyical |  System
[90] |  2019 |  Transfer learning |  Network parameter optimization for uplink power control and user scheduling using Cell KPI/counter data |  Tabular data |  Application |  System
[91] |  2014 |  Transfer learning |  BS ON/OFF switching for energy saving using traffic load data |  Tabular data |  Data Link |  System
[92] |  2020 |  Transfer learning |  Radio map prediction under different antenna tilt using UE RSS data |  Tabular data |  Physical |  Link
[93] |  2021 |  Transfer learning |  Cell performance prediction (CQI and Active UE count) using cell KPI/Counter data |  Tabular data |  Application |  System
[94, 95] |  2019-2021 |  Transfer learning |  Network service performance prediction using testbed traces |  Tabular data |  Network |  System
[87] |  2020 |  Transfer learning + GAN |  REM generation |  Spatial data |  Physical |  System
[19] |  2019 |  GAN |  Synthetic CDR generation using CDR data (call start hour and call duration) |  Tabular data |  Network |  System
[81] |  2020 |  ZipNet-GAN |  Infer fine-grained traffic patterns from course aggregates using CDR data |  Spatio-temporal data |  Network |  System
[82] |  2020 |  GAN |  Cell outage detection using MDT data |  Tabular data |  Application |  System
[86] |  2020 |  GAN |  REM generation |  Spatial data |  Physical |  System
[114] |  2020 |  Variational autoencoder |  Anomaly detection and root cause analysis (RCA) in RAN using KPI/KQI data |  Tabular data |  Application |  System
[115] |  2018 |  Adversarial autoencoder |  Detecting anomalous behavior in wireless spectrum using power spectral density data |  Tabular data |  Network |  System
[76, 13, 77],[72, 67] |  2015-2020 |  Context-aware interpolation |  REM construction using BS location estimated through reverse triangulation |  Spatial data |  Physical |  System
[56, 64],[62, 60, 70] |  2018-2020 |  Kriging interpolation + variants |  REM generation |  Spatial data |  Physical |  System
[32] |  2019 |  Correlation-based interpolation |  Crowdsourced spatio-temporal REM generation |  Spatio-temporal data |  Application |  System
[217] |  2019 |  Adaptive spatial interpolation |  Uplink channel estimation in 3-D massive MIMO systems |  Spatial data |  Physical |  Link
[53] |  2019 |  Adaptive triangulation - induced interpolation |  Multiple REM generation |  Spatial data |  Physical |  System
[65] |  2019 |  NN-enhanced, Kriging interpolation |  REM generation |  Spatial data |  Physical |  System
[33] |  2018 |  Congregate group pattern |  Signaling data (User trajectory data) for discovering congregate group patterns |  Spatio-temporal data |  Network |  System
[34] |  2020 |  Kriging, moving average, matrix completion, IDW, nearest neighbors, natural neighbors, spline interpolation |  MDT coverage map (RSRP) construction |  Spatial data |  Physical |  System
[57] |  2019 |  Kriging interpolation |  REM generation from crowdsourced data |  Spatial data |  Application |  System
[49] |  2011 |  Kriging, MSM and GIDS interpolation |  REM construction from total received signal power |  Spatial data |  Physical |  System
[42] |  2012 |  IDW, adaptive IDW, MSM interpolation |  REM construction |  Spatial data |  Physical |  System
[50] |  2014 |  Nearest neighbor, IDW, Kriging interpolation |  Interference map estimation of MDT reports in cognitive radio networks |  Spatial data |  Physical |  System
[51] |  2012 |  Nearest neighbor, natural neighbor, triangulation-based interpolation |  Interference map generation in cognitive radio networks |  Spatial data |  Physical |  System
[52] |  2013 |  Nearest neighbor, IDW, Kriging |  Interference maps for licensed shared access |  Spatial data |  Physical |  System
[14] |  2012 |  Natural neighbor, kriging and spline |  Interference cartography generation in cognitive radio networks |  Spatial data |  Physical |  System
[54] |  2010 |  Kriging |  Predict network coverage in wireless networks |  Spatial data |  Physical |  Link
[56] |  2018 |  Kriging |  REM construction |  Spatial data |  Physical |  System
[59] |  2018 |  Kriging |  REM construction in cognitive radio networks |  Spatial data |  Physical |  System
[60] |  2019 |  Kriging, nearest neighbor, IDW |  REM construction based on RSSI mobile crowdsensing data |  Spatial data |  Application |  System
[61] |  2019 |  Nearest neighbor, IDW, Kriging |  REM construction for spectrum sharing |  Spatial data |  Physical |  System
[62] |  2020 |  Nearest neighbor, IDW, Kriging |  REM construction |  Spatial data |  Physical |  System
[63] |  2014 |  Kriging |  REM generation for coverage mapping |  Spatial data |  Physical |  System
[64] |  2028 |  Kriging |  REM generation for coverage mapping |  Spatial data |  Link |  System
[65], [67] |  2019-2020 |  Hybrid neural networks and Kriging interpolation |  REM generation |  Spatial data |  Physical |  System
[69] |  2015 |  Kriging, splines, moving average, triangulation-based interpolation |  Coverage extension and prediction with signal strength crowdsourced measurements |  Spatial data |  Application |  System
[70] |  2019 |  Nearest neighbor, IDW, Kriging |  REM construction for military cognitive networks |  Spatial data |  Physical |  System
[71] |  2018 |  RSS and RSSD based methods |  REM enrichment using RSS measurements from sensors |  Spatial data |  Physical |  System
[72] |  2015 |  STM method, location estimation-based method, IDW, Kriging |  REM construction using omnidirectional and directional transmitter antenna |  Spatial data |  Physical |  System
[75] |  2015 |  RSS-based methods |  REM construction in fading channels |  Spatial data |  Physical |  System
[76] |  2018 |  RSS-based method, kriging |  REM construction |  Spatial data |  Physical |  System
[77] |  2010 |  AOA based and SNR based methods |  REM construction |  Spatial data |  Physical |  System
[145] |  2022 |  Synthetic data generation through Atoll simulator |  Cell outage detection and diagnosis using SINR-based REM maps |  Tabular data |  Physical |  System
[85] |  2022 |  Synthetic data generation through Atoll simulator |  Modeling outdoor propagation model using RSS data |  Tabular data |  Physical |  System
[146] |  2022 |  Synthetic data generation through SyntheticNet simulator |  Optimization of A5 mobility parameters using RSRP, SINR, and handover success rate data (HOSR) |  Tabular data |  RSRP/SINR: Physical, HOSR: Network |  System
[113] |  2016 |  Real data generation through smartphone application |  Building semi-markov model based mobility prediction schemes using handover data |  Tabular data |  Network |  System
[147] |  2020 |  Real data generation using mmWave testbed |  Building beam tracking algorithms using mmWave channel measurement data |  Tabular data |  Physical |  Link
[148] |  2014 |  Real data generation using CORNET testbed |  Evaluating real-time radio spectrum access using RSS, packet and bit error rate data (PER/BER) |  Tabular data |  Physical |  Link
[149] |  2017 |  Real data generation using LuMaMi testbed |  Design and validation of massive MIMO research using SNR and BER data for different antenna configurations and modulation schemes |  Tabular data |  Physical |  Link

## Acknowledgment

This work was supported in part by the National Science Foundation under Grant
1923669, 1730650, the Qatar National Research Fund (QNRF) under Grant NPRP12-S
0311-190302 and in part by an unrestricted award from Ericsson Research, CA,
USA.

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|  HANEYA NAEEM QURESHI received her BS degree in Electrical Engineering from
Lahore University of Management Sciences (LUMS), Pakistan, in 2016 and M.S.
and PhD degrees in Electrical and Computer Engineering from the University of
Oklahoma (OU), USA in 2017 and 2021, respectively. She is currently a
Postdoctoral Research Fellow at the Artificial Intelligence (AI) for Networks
Research Center at OU, where she is managing and contributing to several NSF-
funded projects and teaching graduate level courses. She has also worked as an
ORISE fellow with the Center for Devices and Radiological Health, U.S Food and
Drug Administration (FDA), Maryland; and has significant industrial research
experience in wireless communication with Ericsson Research, California, USA
and in 3GPP standardization with InterDigital, Inc., New York, USA. Her other
current research interests include digital smart healthcare, network
automation and combination of machine learning and analytics for future
cellular systems. She has also been engaged in system design of unmanned
aerial vehicles deployment, channel estimation and pilot contamination problem
in Massive MIMO TDD systems.
---|---
|  USAMA MASOOD is pursuing his Ph.D. degree in electrical and computer
engineering at the AI4Networks Research Center, University of Oklahoma, USA,
where his research focus is on designing novel Artificial Intelligence-based
network modeling techniques for enabling zero touch automation in next
generation networks. He is currently working at AT&T Labs, California, where
he is co-leading several projects on network analytics and optimization of
AT&T nationwide 5G network. Previously, he worked with T-Mobile USA, where he
developed innovative machine learning based cloud-native applications for RAN
automation use-cases.
---|---
|  MARVIN MANALASTAS holds a B.S. degree in electronics and communication
engineering from the Polytechnic University of the Philippines (2011), as well
as an M.S. degree in electrical and computer engineering from The University
of Oklahoma, USA (2020), where he is currently pursuing a Ph.D. in electrical
engineering. He is also affiliated with the AI4Networks Research Center.
Recently, Marvin joined Nokia Standards as a Senior RAN Architecture Research
Engineer. Marvin has gained valuable industry experience in cellular network
optimization through his work in the Philippines and Japan. He has also
completed multiple internships in the USA, including positions as an RF
Optimization Intern with Mobilecomm Professionals in TX, an AI/ML Intern with
Synopsys in VA, and a Research Fellow with the U.S. FDA in MA. Marvin’s
research interests center on machine learning applied to optimize 5G and
beyond networks.
---|---
|  SYED MUHAMMAD ASAD ZAIDI received the B.Sc. degree in information and
communication engineering from the National University of Science and
Technology (NUST), Pakistan, in 2008, MS from Ajou University, South Korea in
2013, and PhD Electrical engineering from University of Oklahoma in 2021. With
almost 15 years’ experience in telecom industry, he has worked in Mobilink,
Pakistan, KoreaElectronics and Technology Institute (KETI), South Korea, MOTiV
Research, Japan, ATT, USA, Sprint, USA and T-Mobile, USA. Currently, he is
leading 5G radio-frequency optimization team in T-Mobile networks. His
research domain is mobility robustness and optimization of futuristic ultra-
dense base station deployment.
---|---
|  HASAN FAROOQ is Senior AI Researcher at Ericsson Research in Santa Clara,
USA. His background is AI/ML driven zero-touch automation algorithms for Radio
Access Networks. He holds a B.Sc. degree in Electrical Engineering from the
University of Engineering and Technology, Lahore, Pakistan, M.Sc. by Research
degree in Information Technology from Universiti Teknologi PETRONAS, Malaysia,
a Ph.D. degree in Electrical and Computer Engineering and Post Doc from the
University of Oklahoma, USA. He has authored/co-authored over 50 publications
in high impact journals, book chapters and proceedings of IEEE flagship
conferences on communications. He also has patents in the area of SON
algorithms.
---|---
|  JULIEN FORGEAT is an artificial intelligence principal researcher at
Ericsson Research. He joined Ericsson in 2010 after spending several years
working on network analysis and optimization. He holds an M.Eng. in computer
science from the National Institute of Applied Sciences in Lyon, France. At
Ericsson, Julien has worked on mobile learning, Internet of Things and big
data analytics before specializing in machine learning and AI infrastructure.
His current research focuses on the software components required to run AI and
machine learning workloads on distributed infrastructures as well as the
algorithmic approaches that are best suited for complex distributed and
decentralized use-cases.
---|---
|  MAXIME BOUTON is an artificial intelligence researcher at Ericsson
Research. His research interests lie in applying reinforcement learning to
network optimization problems. He also works on topics related to AI safety,
multi-agent systems and decision-making problems with partial observability.
Maxime received his PhD from Stanford University where he worked on safety and
scalability of intelligent autonomous systems. Prior to doing research, Maxime
got a MS in Aeronautics and Astronautics as part of a double degree between
Ecole Centrale Paris and Stanford University.
---|---
|  SHRUTI BOTHE is an Artificial Intelligence Researcher at Ericsson. With
several years of academic and industry experience and a proven track record of
identifying issues in and achieving solutions in domains combining AI/ML to
telecommunication networks, Shruti was a main contributor of Ericsson’s
entrepreneurial effort ”Ericsson Routes” that brings autonomous and unmanned
vehicles the awareness required to have consistent and reliable connectivity
throughout the whole journey. Shruti also heads Ericsson Research’s multi-year
collaboration with MIT CSAIL related to neuromorphic computing and Lithionics.
This research is aimed to produce the next generation of more efficient
algorithms and hardware that enable more efficient computing and substantial
energy savings. She has several research publications and over 18 pending
patents and has recently been honored with a ”Key Contributor” Award at
Ericsson.
---|---
|  PER KARLSSON is the Director of Media Research in Ericsson, focusing on A/V
Coding, Content Analytics, and how new XR Experiences will be enabled by the
rollout of 5G Networks. He is also the Director of Ericsson Research in
Silicon Valley focused on the areas of Radio, AI, Networking, Media, Cloud,
and Security. The Research is performed together with Academia, Customers,
Partners, and Universities. His team is currently actively engaged in
collaborative projects focused on exploring new opportunities that the 5G
Networks will bring to the entertainment, manufacturing, and automotive
industry. Per has been in the industry since 1993 working in the intersection
of Research and Products mainly at Ericsson but also leading the Networking
Research at the Swedish Research Institute Acreo.
---|---
|  ALI RIZWAN obtained his Bachelor’s degree in Applied and Theoretical
Mathematics from Bahauddin Zakariya University, Pakistan in 2006. He then
pursued an MBA-IT degree from the same institution in 2008. In 2016, he earned
an M.Sc. degree in Big Data Science from Queen Mary University of London, U.K.
He completed his academic journey by achieving a Ph.D. degree from the
University of Glasgow, Glasgow, U.K, in 2021. Currently, Dr. Rizwan serves as
the Chief Technical Officer and Co-founder of Artificial Intelligence For
Life, Pakistan. His work primarily focuses on the research and development of
AI-enabled screening solutions in healthcare.
---|---
|  ALI IMRAN is Professor of Cyber Physical Systems in James Watt School of
Engineering, University of Glasgow. He is currently on leave from University
of Oklahoma where he is Williams Presidential Professor in ECE and the
founding director of the Artificial Intelligence (AI) for Networks
(AI4Networks) Research Center. His research interests include AI and its
applications in wireless networks and healthcare. His work on these topics has
resulted in several patents and over 150 peer-reviewed articles including some
of the highly influential papers in the domain of wireless network automation.
On these topics he has led numerous multinational projects, given invited
talks/keynotes and tutorials at international forums and advised major public
and private stakeholders and co-founded multiple start-ups. He holds a B.Sc.
degree in electrical engineering from the University of Engineering and
Technology Lahore, Pakistan, in 2005, and the M.Sc. degree (Hons.) in mobile
and satellite communications and the PhD degree from the University of Surrey,
Guildford, U.K., in 2007 and 2011, respectively. He is an Associate Fellow of
the Higher Education Academy, U.K. He is also a member of the Advisory Board
to the Special Technical Community on Big Data, the IEEE Computer Society.
---|---