Learning to Communicate: Deep Learning based solutions for the Physical Layer of Machine Type Communications [LeanCom]
Lead Research Organisation:
University College London
Department Name: Electronic and Electrical Engineering
Abstract
With the advent of the Internet of Things (IoT), machine type communications (MTC), cloud computing and many other applications, the wireless network will become far more complex, while at the same time far more essential than ever before.
Given the above exponential growth in both connectivity and complexity of the wireless systems and the unprecedented demands on latency, capacity, ultra-reliability and security, the network is becoming analytically intractable. Naturally, human-driven physical layer (PHY) design approaches rooted on mathematical models of communications systems and networks which drive today's network architectures are being surmounted by the sheer complexity of the emerging network paradigms. Hardware imperfections, that are inevitable with the employment of low-cost MTC sensors and transmitters, will drastically increase the volatility of the network, and theoretically driven solutions typically relying on generic and highly inaccurate models cannot address this as they are highly sub-optimal in practice. The above challenges necessitate new data-driven approaches to the design of communications systems, as opposed to traditional system-model driven designs that are becoming obsolete.
Towards the diverse communication paradigms of MTC of the future, there is an urgent need to address reliable and adaptive links detached from mathematical models, and instead based on data-driven approaches. This visionary project will address these fundamental challenges by developing new Neural Netowrk architectures tailored for wireless communications, and new transceiver architectures based on data-driven training. Our research will address the development of a) a communications specific DL framework, b) DL-inspired PHY solutions and, c) proof-of-concept verification of the proposed solutions.
LeanCom will be performed with Huawei, NEC Europe, Duke University, The Digital Catapult and CommNet and aspires to kick-start an innovative ecosystem for high-impact players among the infrastructure and service providers of ICT to develop and commercialize a new generation of learning-based networks. The implementation, experimentation and testing (within WP3) of the proposed solutions serves as a platform towards commercialisation of the results of LeanCom, aiming towards an impact of a foundational nature for the UK's digital economy.
Given the above exponential growth in both connectivity and complexity of the wireless systems and the unprecedented demands on latency, capacity, ultra-reliability and security, the network is becoming analytically intractable. Naturally, human-driven physical layer (PHY) design approaches rooted on mathematical models of communications systems and networks which drive today's network architectures are being surmounted by the sheer complexity of the emerging network paradigms. Hardware imperfections, that are inevitable with the employment of low-cost MTC sensors and transmitters, will drastically increase the volatility of the network, and theoretically driven solutions typically relying on generic and highly inaccurate models cannot address this as they are highly sub-optimal in practice. The above challenges necessitate new data-driven approaches to the design of communications systems, as opposed to traditional system-model driven designs that are becoming obsolete.
Towards the diverse communication paradigms of MTC of the future, there is an urgent need to address reliable and adaptive links detached from mathematical models, and instead based on data-driven approaches. This visionary project will address these fundamental challenges by developing new Neural Netowrk architectures tailored for wireless communications, and new transceiver architectures based on data-driven training. Our research will address the development of a) a communications specific DL framework, b) DL-inspired PHY solutions and, c) proof-of-concept verification of the proposed solutions.
LeanCom will be performed with Huawei, NEC Europe, Duke University, The Digital Catapult and CommNet and aspires to kick-start an innovative ecosystem for high-impact players among the infrastructure and service providers of ICT to develop and commercialize a new generation of learning-based networks. The implementation, experimentation and testing (within WP3) of the proposed solutions serves as a platform towards commercialisation of the results of LeanCom, aiming towards an impact of a foundational nature for the UK's digital economy.
Planned Impact
The explosive growth of industrial control processes and the industrial IoT applications, provides unique opportunities for the creation of impact through MTC research. This project promotes a fundamental paradigm shift to communications system design, and therefore holds the potential for high impact research of a foundational nature for the UK's digital economy. Impact will be measured in the number of products and commercialisation activities, consultancies and patents filed, by any follow-up industry funded collaborations, and through the success of public engagement activities.
To foster the economic competitiveness of the UK, this project will develop novel, low-cost solutions suitable for the MTC ecosystem. Direct beneficiaries are: (1) researchers in wireless communications, machine-type communications, supported by the Internet-of-Things (IoT), and researchers in the general areas of machine learning and in other engineering fields, (2) users of MTC applications, as well as, government departments and private sector companies (from small firms to big enterprises) with interest in emerging MTC (e.g. industry 4.0 and IoT), (3) policy making regulators, such as Ofcom and.
Commercial and societal impact: The growing reliance of society on wireless technologies, along with the emergence of industrial applications, prominently Industry 4.0, that rely on MTC solutions, and others such as smart cities and connected cars, provides a unique platform for achieving commercial impact through the proposed work. By focusing on practical implementation and hardware efficiency, along with proof-of-concept testing, we will ensure the promised massive connectivity targets are brought to practice. By enabling highly complex and adaptive communication scenarios, the proposed technological shift will be able to tackle the data deluge in the upcoming decades. It is anticipated that the outputs of this research project will be used by telecom/electronics manufacturers (e.g., Huawei, National Instruments, Nokia, Samsung), telecom business operators (e.g., Vodafone-UK, BT) and data stakeholders (e.g. Facebook, Google) within the UK and abroad to build machine-type networks with massive connectivity. The strategic placement of our Partners Huawei, NEC, Digital Catapult at the centre of the 5G ecosystem provides direct means for commercialization through IP exploitation of the project's solutions and promotion of the developed solutions to their product lines.
Overall the project lies at the centre of EPSRC's artificial intelligence strategy, and aligns with the following EPSRC's cross-ICT priorities (2017-2020) : Future Intelligent Technologies - learning-based transmission is at the centre of this portfolio and our MTC solutions open new horizons for high-reliability communication between different objects, machines and humans, fully aligned with the EPSRC vision to '' promote research which aims to develop intelligent, adaptive or autonomous systems that can learn, adapt and make decisions without the need for human control''. Information and Communications Technology (ICT) networks and distributed systems - by proposing disruptive solutions for future dense MTC, we will facilitate their rollout after 2025.
New experts: The research training within the project, involving visits for hands-on training to our industrial partners Huawei and NEC will develop the research profile, expertise and man power of the world class research group in UCL and foster a team with excellence in DL-based communications and MTC, by producing new experts in the field of communications-tailored learning architectures, learning-based transmission, machine-type communications, and wireless communications in general.
The commercialization, exploitation, outreach and dissemination activities to realise the above impact are detailed in the Pathways to Impact section.
To foster the economic competitiveness of the UK, this project will develop novel, low-cost solutions suitable for the MTC ecosystem. Direct beneficiaries are: (1) researchers in wireless communications, machine-type communications, supported by the Internet-of-Things (IoT), and researchers in the general areas of machine learning and in other engineering fields, (2) users of MTC applications, as well as, government departments and private sector companies (from small firms to big enterprises) with interest in emerging MTC (e.g. industry 4.0 and IoT), (3) policy making regulators, such as Ofcom and.
Commercial and societal impact: The growing reliance of society on wireless technologies, along with the emergence of industrial applications, prominently Industry 4.0, that rely on MTC solutions, and others such as smart cities and connected cars, provides a unique platform for achieving commercial impact through the proposed work. By focusing on practical implementation and hardware efficiency, along with proof-of-concept testing, we will ensure the promised massive connectivity targets are brought to practice. By enabling highly complex and adaptive communication scenarios, the proposed technological shift will be able to tackle the data deluge in the upcoming decades. It is anticipated that the outputs of this research project will be used by telecom/electronics manufacturers (e.g., Huawei, National Instruments, Nokia, Samsung), telecom business operators (e.g., Vodafone-UK, BT) and data stakeholders (e.g. Facebook, Google) within the UK and abroad to build machine-type networks with massive connectivity. The strategic placement of our Partners Huawei, NEC, Digital Catapult at the centre of the 5G ecosystem provides direct means for commercialization through IP exploitation of the project's solutions and promotion of the developed solutions to their product lines.
Overall the project lies at the centre of EPSRC's artificial intelligence strategy, and aligns with the following EPSRC's cross-ICT priorities (2017-2020) : Future Intelligent Technologies - learning-based transmission is at the centre of this portfolio and our MTC solutions open new horizons for high-reliability communication between different objects, machines and humans, fully aligned with the EPSRC vision to '' promote research which aims to develop intelligent, adaptive or autonomous systems that can learn, adapt and make decisions without the need for human control''. Information and Communications Technology (ICT) networks and distributed systems - by proposing disruptive solutions for future dense MTC, we will facilitate their rollout after 2025.
New experts: The research training within the project, involving visits for hands-on training to our industrial partners Huawei and NEC will develop the research profile, expertise and man power of the world class research group in UCL and foster a team with excellence in DL-based communications and MTC, by producing new experts in the field of communications-tailored learning architectures, learning-based transmission, machine-type communications, and wireless communications in general.
The commercialization, exploitation, outreach and dissemination activities to realise the above impact are detailed in the Pathways to Impact section.
Organisations
- University College London (Lead Research Organisation)
- NEC Corporation (Collaboration)
- Southeast University (Collaboration)
- Huawei Technologies Sweden AB (Collaboration)
- Huawei Technologies (France) (Project Partner)
- Digital Catapult (Project Partner)
- Duke University (Project Partner)
- CommNet2 (Project Partner)
- NEC (Germany) (Project Partner)
Publications
Xu T
(2022)
An Experimental Proof of Concept for Integrated Sensing and Communications Waveform Design
in IEEE Open Journal of the Communications Society
Dizdar O
(2022)
Energy Efficient Dual-Functional Radar-Communication: Rate-Splitting Multiple Access, Low-Resolution DACs, and RF Chain Selection
in IEEE Open Journal of the Communications Society
Zeng H
(2022)
Multicluster-Coordination Industrial Internet of Things: The Era of Nonorthogonal Transmission
in IEEE Vehicular Technology Magazine
Bigdeli M
(2023)
Noncoherent OFDM Transmission via Off-the-Grid Joint Channel and Data Estimation
in IEEE Wireless Communications Letters
Mohammad A
(2023)
A Memory-Efficient Learning Framework for Symbol Level Precoding With Quantized NN Weights
in IEEE Open Journal of the Communications Society
Li A
(2023)
Practical Interference Exploitation Precoding Without Symbol-by-Symbol Optimization: A Block-Level Approach
in IEEE Transactions on Wireless Communications
Du Z
(2023)
Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets
in IEEE Transactions on Wireless Communications
Li R
(2023)
Scenario-Aware Learning Approaches to Adaptive Channel Estimation
in IEEE Transactions on Communications
Babu N
(2023)
Energy-Efficient Trajectory Design of a Multi-IRS Assisted Portable Access Point
in IEEE Transactions on Vehicular Technology
Hu Y
(2023)
Timely Data Collection for UAV-Based IoT Networks: A Deep Reinforcement Learning Approach
in IEEE Sensors Journal
Zhang J
(2023)
Beam Training and Tracking With Limited Sampling Sets: Exploiting Environment Priors
in IEEE Transactions on Communications
Meng X
(2023)
Vehicular Connectivity on Complex Trajectories: Roadway-Geometry Aware ISAC Beam-Tracking
in IEEE Transactions on Wireless Communications
Zhang J
(2023)
Joint Precoding and CSI Dimensionality Reduction: An Efficient Deep Unfolding Approach
in IEEE Transactions on Wireless Communications
Mohammad A
(2023)
An Unsupervised Deep Unfolding Framework for Robust Symbol-Level Precoding
in IEEE Open Journal of the Communications Society
Temiz M
(2023)
An Experimental Study of Radar-Centric Transmission for Integrated Sensing and Communications
in IEEE Transactions on Microwave Theory and Techniques
Salem A
(2023)
Secure Rate Splitting Multiple Access: How Much of the Split Signal to Reveal?
in IEEE Transactions on Wireless Communications
Al-Jarrah M
(2023)
A Unified Performance Framework for Integrated Sensing-Communications Based on KL-Divergence
in IEEE Transactions on Wireless Communications
Valiulahi I
(2023)
Net-Zero Energy Dual-Functional Radar-Communication Systems
in IEEE Transactions on Green Communications and Networking
Su N
(2024)
Sensing-Assisted Eavesdropper Estimation: An ISAC Breakthrough in Physical Layer Security
in IEEE Transactions on Wireless Communications
Description | new ways of designing communication systems using AI |
Exploitation Route | - standardization from comms industry - patents |
Sectors | Aerospace, Defence and Marine,Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Energy,Healthcare,Government, Democracy and Justice,Transport |
Description | Cities Partnership Grant with KTH entitled "Integrated Sensing and Communications for Perceptive Smart Cities" |
Amount | £5,000 (GBP) |
Organisation | Kunliga Tekniska Hoegskolan |
Sector | Academic/University |
Country | Sweden |
Start | 03/2021 |
End | 07/2021 |
Description | DASA Emerging Innovations Grant with QinetiQ entitled "Design and Testing of Low Probability Of Intercept (LPI) Waveforms For Joint Radar And Communications" |
Amount | £98,000 (GBP) |
Organisation | Defence Science & Technology Laboratory (DSTL) |
Sector | Public |
Country | United Kingdom |
Start | 07/2021 |
End | 03/2022 |
Description | Next generation information networks |
Amount | £8,000,000 (GBP) |
Organisation | Defence Science & Technology Laboratory (DSTL) |
Sector | Public |
Country | United Kingdom |
Start | 12/2021 |
End | 03/2025 |
Description | Intelligent beam-tracking using machine learning |
Organisation | Southeast University |
Country | Bangladesh |
Sector | Academic/University |
PI Contribution | design of machine learning algorithms for fast beam alignment between transmitter and a moving receiver |
Collaborator Contribution | intellectual contribution in the development of the algorithm |
Impact | - one journal publication - a research link to SE university |
Start Year | 2020 |
Description | NEC-UCL beam alignement |
Organisation | NEC Corporation |
Department | NEC (UK) Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | development of fast beam alignment algorithms |
Collaborator Contribution | definition of the research problem, review of results, and guidance |
Impact | - monthly technical reports |
Start Year | 2020 |
Description | UCL Huawei on symbol level precoding |
Organisation | Huawei Technologies Sweden AB |
Country | Sweden |
Sector | Private |
PI Contribution | Test our SLP tecniques in standards relevant environments |
Collaborator Contribution | define scenarios and KPIs for the technologies, advise on the development |
Impact | one submitted publication |
Start Year | 2020 |
Title | methods and apparatus for beam alignment |
Description | a beam alignment technigue for cellular communication systems |
IP Reference | 2021-146426 |
Protection | Patent application published |
Year Protection Granted | 2022 |
Licensed | No |
Impact | Patent result from a collaboration with NEC on a technique to achieve beam alignment in cellular systems. NEC is looking to exploit this in the 3GPP standards |
Description | DCMS 6G forum |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | Invited talk "Learning to Communicate" to UK Department for Digital, Culture, Media & Sport (DCMS) / Spectrum Policy Workshop "6G: Technology Enablers for Spectrum & Energy Efficient Wireless Access" |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.techuk.org/what-we-deliver/events/6g-technology-enablers-for-spectrum-energy-efficient-w... |
Description | ICC 2020 workshop on JCR |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Workshop in flagship conference IEEE ICC 2020 to disseminate the outcomes of our research |
Year(s) Of Engagement Activity | 2020 |
URL | https://icc2020.ieee-icc.org/workshop/ws-11-workshop-communication-and-radar-spectrum-sharing |
Description | ITU AI forum |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Invited talk "Learning to communicate (LeanCom): Deep learning based solutions for the physical layer of communications" to The ITU AI for Good Forum / Challenge on AI and Machine Learning in 5G |
Year(s) Of Engagement Activity | 2021 |
URL | https://aiforgood.itu.int/event/learning-to-communicate/ |
Description | the IET |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Public/other audiences |
Results and Impact | an invited public talk on the integration of sensing into 6G wireless networks |
Year(s) Of Engagement Activity | 2022 |