FuSe: Deep Learning and Signal Processing using Silicon Photonics and Digital CMOS Circuits for Ultra-Wideband Spectrum Perception

The radio frequency (RF) spectrum weaves the very fabric of wireless communications. And it is among the most precious and scarcest of natural resources. Tomorrow’s tech applications such as digital twins, smart vehicles, and augmented reality demand Gigabit-per-second wireless connectivity everywhere all the time. Such demands call for effective mechanisms to guarantee efficient and secure RF spectrum access. Existing methods use simple techniques that can detect users’ presence in the spectrum but cannot sense the ?who, when, and how? of the spectrum being utilized. Nonetheless, emerging artificial intelligence (AI) methods including but not limited to machine learning (ML) techniques are promising for achieving ?RF perception.? A thorny problem in using AI algorithms for RF perception is the inability to process the massive sensed bandwidth of the spectrum. To solve this problem, this project will leverage a hybrid integration approach, where photonic and electronic small chips, or chiplets, will be synergistically combined to facilitate AI/ML-enabled RF perception over the entire RF spectrum. The education component of the project will address the dearth in the US-based semiconductor workforce through a combination of training on photonic and electronic chip design, AI/ML, and wireless technology skills. The FuSe team will mentor women and minorities who are underrepresented, in topics such as semiconductors, chip design, and wireless communication. Outreach to high-school students using AI-based projects will help build a pipeline of students to pursue engineering degrees focusing on semiconductors and computing. A critical educational emphasis is to fast-track training of students on newer FinFET nodes through a complete revamp of analog and digital IC design courses. The PIs will share the developed education and training material amongst the collaborators and make them available online.

To achieve AI-enabled spectrum sensing, this convergent FuSe project will co-integrate a photonic integrated circuit (PIC) with mixed-signal and energy-efficient asynchronous digital chiplets to realize real-time wideband RF perception. The PIC front-end will allow RF spectrum processing and channelization of over 24 GHz of bandwidth. The mixed-signal IC will interface the PIC?s output with digital AI accelerator chiplets. The team will create AI/ML algorithms for modulation recognition, spectrum sensing, and detection of wireless internet-of-things (IoT) devices or specific RF hardware front-ends using fast convolutional neural networks. PIs will employ high-level synthesis (HLS) of speed/power-efficient RF processing cores for real-time AI/ML algorithm implementation. These HLS prototypes will be custom optimized for minimum chip area and power consumption and will achieve low complexity and fast throughput using weight quantization, compressive processing, quantization-aware retraining, signal flow graph pruning, and power/area-optimized digital computing circuits. The team will synthesize the digital cores as asynchronous digital chiplets. Finally, the photonics and electronic chiplets will be taped-out and fabricated using state-of-the-art commercial foundries including the FinFET-based CMOS process, and then packaged for testing and evaluation.

Please see NSF abstract here.


NeTS: Medium: Resilient-by-Design Data-Driven NextG Open Radio Access Networks

Our society increasingly depends on cellular networks, making it critical to assure that the networks are secure against cyber attacks. Next-generation cellular networks are expected to rely on machine learning (ML) algorithms to achieve real-time resource optimization across space, time, frequency and devices. This project studies security threats to those ML algorithms and develops solutions to protect them, focusing on the Open Radio Access Networks (Open RAN) architecture which is rapidly becoming widespread. All project outputs (algorithms, hardware/software designs, and datasets) will be made publicly available through the NSF RFDataFactory website, helping to address the current lack of large-scale datasets for data-driven wireless research. As part of the project, several graduate students will develop unique expertise at the crossroads of ML, security, embedded systems and wireless networks. The project?s key findings will be integrated into new graduate courses in wireless ML security, and will enrich ongoing initiatives at Northeastern University for undergraduate and K-12 students coming from underrepresented minority groups.

Novel optimization frameworks are investigated to model adversarial ML attacks in Open RANs. These findings are used to design ML architecture search algorithms to find ML models for Open RANs that are resilient to attack while still satisfying constraints such as end-to-end latency and energy consumption. The project designs anomaly detection techniques to enhance resilience in dynamic settings, and dynamic defense strategies against real-time dataset poisoning attacks. The proposed techniques are evaluated using one or more of the following testbeds: the Colosseum network emulator, the OpenRANGym framework, and the NSF PAWR POWDER platform.

Please see the NSF abstract here.


NeTS: Small: Reliable Task Offloading in Mobile Autonomous Systems Through Semantic MU-MIMO Control


Mobile autonomous systems (MASs) such as self-driving vehicles and drones have a pivotal role in critical applications such as urban mobility, precision agriculture and remote surveillance. To achieve their tasks, MASs increasingly rely on high-throughput low-latency streaming of computer vision tasks (e.g., object detection) to edge servers. However, ephemeral environmental factors such as blockages, congestion and fading may erratically interrupt the flow of tasks to the edge servers. Existing work has addressed computation and communication issues of task offloading by MASs separately, which necessarily leads to suboptimal solutions. Task accuracy, indeed, is inevitably tied to the quality of the multimedia data being sent to the edge, which in turns depends on the adopted wireless strategy. However, the wireless parameters being used depend on the quality of data being sent (the more compression, the higher the latency), which ultimately impacts the desired task accuracy. Thus, to achieve applications that are “resilient-by-design” without compromising task accuracy, the semantics of the multimedia data must be holistically and fundamentally intertwined with real-time optimization of wireless transmissions. The core advance of this project is the design and experimental evaluation of fundamentally novel techniques for hardware based semantic-driven joint optimization of multimedia compression strategies and MU-MIMO transmissions in the context of resource-limited wireless systems. The PIs will leverage the support of this project to involve minority and underrepresented students in research and outreach activities. As part of the project, graduate students will develop unique expertise at the crossroads of machine learning, embedded systems and wireless networks.

Real-time object Detection with Edge Task offloading

The key technical efforts of this project will focus on the design of novel deep reinforcement learning (DRL)-based strategies that will control how the acquired data stream is compressed and wirelessly transmitted to the edge servers through MU-MIMO. The PIs will utilize techniques based on split computing to avoid increasing computational overhead due to the compression and MU-MIMO channel state information (CSI) feedback, while keeping the task accuracy close to the original. A full-fledged drone-based prototype based on customized software-defined radio (SDR) interfaces based on FPGA real-time processing and edge computing will be developed as part of the project. Large-scale data collection campaigns will be performed with a 64-antenna SDR testbed at Northeastern, a drone experimental testbed at UC Irvine, and the AERPAW PAWR platform to (i) collect the necessary wireless/multimedia data to train our algorithms; (ii) perform extensive testing and performance evaluation.


Principal Investigator: Francesco Restuccia (Northeastern)
Co-Principal Investigator: Marco Levorato (UC Irvine)
Graduate Research Assistant: Foysal Haque (Northeastern)
Graduate Research Assistant: Sharon Ladrón de Guevara (UC Irvine)


All the publications below are available under the “Publication” tab.

Y. Wu, C. Chiasserini, F. Malandrino and M. Levorato, “Invited Paper: Enhancing Privacy in Federated Learning via Early Exit”, Proceedings of ACM Workshop on Advanced tools, programming languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems (ACM ApPLIED), 2023.

F. Malandrino, G. Di Giacomo, A. Karamzade, M. Levorato, and C. Chiasserini, “Matching DNN Compression and Cooperative Training with Resources and Data Availability”, Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM), 2023.

A. Alsoliman, F. Abkenar, and M. Levorato, “State-Recovery Protocol for URLLC Applications in 5G Systems”, IEEE Topical Conference on Wireless Sensors and Sensor Networks (IEEE WiSNet), 2023.

N. Bahadori, Y. Matsubara, M. Levorato and F. Restuccia, “SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing”, Proceedings of IEEE International Conference on Distributed Computing Systems (IEEE ICDCS), 2023.

A. Coletta, F. Giorgi, G. Maselli, M. Prata, D. Silvestri, J. Ashdown and F. Restuccia, “AA-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems,” Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM), 2023.

M. Cominelli and F. Gringoli and F. Restuccia, “Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations,” Proc. of IEEE International Conference on Pervasive Computing and Communications (IEEE PerCom), 2023.

K. Foysal Haque, F. Meneghello, and F. Restuccia, “Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices,” Proc. of ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization (ACM WINTECH), 2023.

Y. Matsubara, M. Levorato and F. Restuccia, “Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges,” ACM Computing Surveys (CSUR), Vol. 55, Is. 5, Art. No.: 90, pp. 130, December 2022.

F. Meneghello, C. Chen, C. Cordeiro and F. Restuccia, “Toward Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks,” IEEE Communications Magazine (IEEE COMMAG), Vol. 61, Is. 7, pp. 128 – 133, July 2023.

A. Pinto, A. Ashdown, T. Hassan, H. Cheng, F. Esposito, L. Bonati, S. D’Oro, T. Melodia, F. Restuccia, “Hercules: An Emulation-Based Framework for Transport Layer Measurements over 5G Wireless Networks,” Proceedings of ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization (ACM WINTECH), 2023.

C. Puligheddu, J. Ashdown, C. F. Chiasserini, and F. Restuccia, “SEM-O-RAN: Semantic and Flexible O-RAN Slicing for NextG Edge-Assisted Mobile Systems,” Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM), 2023.

F. Raviglione, C. Casetti and F. Restuccia, “Edge-V: Enabling Vehicular Edge Intelligence in Unlicensed Spectrum Bands“, Proceedings of IEEE Vehicular Technology Conference (VTC2023-Spring), 2023.

F. Restuccia, E. Blasch, A. Ashdown, J. Ashdown, and K. Turck, “3D-O-RAN: Dynamic Data Driven Open Radio Access Network Systems,” Proceedings of IEEE Military Communications Conference (IEEE MILCOM), pp. 19-24, 2022.

R. Rusca, F. Raviglione, C. Casetti, P. Giaccone, and F. Restuccia, “Mobile RF Scenario Design for Massive-Scale Wireless Channel Emulators,” Proceedings of Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 2023.

Y. Matsubara, D. Callegaro, S. Singh, M. Levorato, and F. Restuccia, “BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing,” Proceedings of IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (IEEE WoWMoM), 2022.

D. Callegaro, M. Levorato and F. Restuccia, “SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection,” Proceedings of IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (IEEE WoWMoM), 2022.

N. Bahadori, J. Ashdown and F. Restuccia, “ReWiS: Reliable Wi-Fi Sensing Through Few-Shot Multi-Antenna Multi-Receiver CSI Learning,” Proceedings of IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (IEEE WoWMoM), 2022. Best Paper Award.

F. Meneghello, M. Rossi and F. Restuccia, “DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning,” Proceedings of IEEE International Conference on Distributed Computing Systems (IEEE ICDCS), 2022.

P. Tehrani, F. Restuccia, and M. Levorato, “Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks,” Proceedings of IEEE International Symposium on Dynamic Spectrum Access Networks (IEEE DySPAN), December 2021.


F. Restuccia, K. F. Haque and  M. Zhang, “Method and Apparatus for Wi-Fi Sensing Through MU-MIMO Beamforming Feedback Learning,” PCT. Application No.: PCT/US23/31651, filed on August 31, 2023.

F. Meneghello, M. Rossi and F. Restuccia, “System and Method for Identifying a Remote Device,” U.S. No.: 17/934,802, filed on September 23, 2022.

N. Bahadori and F. Restuccia, “System and Method for Sensing an Environment,” U.S. No.: 17/822,313, Filed on August 25, 2022.

F. Raviglione, F. Restuccia and C. Casetti, “Vehicular  Edge Intelligence in Unlicensed Spectrum  Bands”, PCT/US2023/064848, filed on March 23, 2023

N. Bahadori, Y. Matsubara, M. Levorato and F. Restuccia, “Beamforming in Wireless Networks Via Split Computing”, PCT Application No. PCT/US23/65300, filed on April 4, 2023.

C. Puligheddu, F. Restuccia, C.F. Chiasserini, “SEM-O-RAN: Semantic NextG O-RAN Slicing for Data-Driven Edge-Assisted Mobile Applications”, PCT Application No.: PCT/US2023/064901, filed on March 24, 2023. 

F. Restuccia, K. F. Haque and  M. Zhang, “SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals,” U.S. Application No.: 63/380,254,  filed on October 21, 2022.

S. Rifat, J. Ashdown, K. Turck and F. Restuccia, “DAZDA: Domain-Aware Zero-Shot Dynamic Adaptation of Neural Networks in Edge Computing,” U.S. Application No.: 63/580,492,  filed on Sept 5, 2023.

Educational Activities and Outreach

Master students involvement: a team of 6 master students is expanding the evaluation of our split computing algorithms to a wider set of autonomous platforms (e.g., rovers) to enable a comprehensive evaluation of data representation strategies in response to different contexts.

Undergrad and Junior students involvement: several undergraduate students (of which 5from minorities) and senior high-school student has been involved in the development of autonomous navigation algorithms.

NSF Abstract: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2134973&HistoricalAwards=false


CCRI: Principled Dataset Generation, Sharing and Maintenance Tools for the Wireless Community

Applied machine learning (ML) research in wireless faces challenges due the inability of domain experts to easily access existing well-curated, well-structured, and open-access datasets. Furthermore, there is a lack of direct access to a software framework that automates dataset creation and distribution based on detailed user requirements. RFDataFactory is a collaborative project that brings together investigators from Northeastern University and Rice University to bridge this gap. RFDataFactory aims to make available categorized datasets suitable for research related to ML in 5G and beyond networks, and advance fundamental understanding and design tools for accessing, creating, sharing and storing wireless datasets.

RFDataFactory will enable easy collection and preprocessing of physical layer to packet-level datasets through high-level directives and application programming interfaces. This will enable dataset generation for several NSF-funded experimentation platforms, such as the Colosseum emulator and NSF Platforms for Advanced Wireless Research. The project will significantly advance autonomous statistical analysis of RF spectrum activity, which will reduce data storage needs. Moreover, it will create pre-processing tools for removing device identifying information and facilitate generating standards compliant metadata headers. The project will also result in a search-able, centralized repository of both project-supported and user-contributed datasets with the focus on re-usability.

RFDataFactory will accelerate interdisciplinary research at the intersection of machine learning and the wireless domain, as well as bridging different communities and train a new generation of professionals for wireless dataset creation and sharing. The project will seek to involve underrepresented students in research and learning activities, support annual dataset gathering challenges, update advanced course materials with hands-on tutorials and laboratory sessions. Through targeted high-school outreach, the project will increase awareness and excitement in the next generation of researchers. The project will also generate value for other large-scale infrastructure investments already made by the NSF.

Project Url: All datasets, meta-data files, software application programming interfaces, tutorial materials, webinar recordings and other digital outcomes of this project will be maintained for 3 years, accessible via the project website after the completion of the project.

NSF Abstract: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120447&HistoricalAwards=false


RINGS: Internet of Things Resilience through Spectrum-Agile Circuits, and Maintenance Tools for the Wireless Community

As the Internet of Things (IoT) continues to grow at a fast pace, the increasing number of wireless devices in the frequency spectrum up to 6 GHz creates a compelling need to securely adapt IoT communications based on availability in the wireless spectrum environment. Autonomous coordination of wireless transmissions to avoid congestions becomes particularly important when numerous IoT devices with stringent power consumption restrictions communicate with an edge device connected to the cloud; collecting information with relatively low data rates such as biomedical signals, detected gases/chemicals levels, temperature, humidity, or vibration data. Such low-power IoT device applications include medical and health care, smart homes, transportation, manufacturing, agriculture, and environmental monitoring. It is imperative to design IoT networks with resilience features deeply embedded across layers from the integrated circuit level to the wireless system level. When IoT devices are employed with sensors in increasingly crowded environments to transmit sensed information, it is essential to increase their awareness of incumbent spectrum users and avoid interference. An overarching goal of this project is to create spectrum-agile IoT networks with low-power adaptive radio frequency (RF) circuits at the sensor nodes, and with coordinated optimization and enhanced security at the edge device. The synergies between the circuits, computing, and wireless networking components of this research are anticipated to create a paradigm for resilient next-generation IoT networks with energy-efficient secure communication between sensor nodes and edge devices. Research and education will be integrated by incorporating the obtained knowledge into graduate and undergraduate education. In addition, high school interns will be engaged through the Center for STEM Education at Northeastern University.

The project entails the research and development of a coordinated cross-layer design methodology for agile communication between edge devices and IoT sensor nodes. This is achieved by distributing spectrum sensing and real-time reconfiguration as follows: fast coarse spectrum sensing and reconfiguration in the sub-6 GHz frequency range on the analog/RF circuit level within low-power IoT devices, fine carrier sensing and network level optimizations on the edge device, and enhancement of high-level authentication and anomaly detection with the computing capabilities on the edge device; all aided by wirelessly transmitted information from temperature sensors used as activity detectors embedded in the IoT device transceiver. This cross-layer approach aims at enabling adaptive edge networks by providing the device-level ability to quickly respond to disruptive interference events by changing the transmit and receive frequencies at the IoT nodes, while performing intelligent real-time machine learning (ML) functions for coordinated communication within the network on the edge device with a software-defined radio (SDR) and field-programmable gate array (FPGA). Security will be enhanced at the wireless system level through ML-based RF fingerprinting, while robustness will be enhanced through federated learning techniques. At the hardware level, security will be enhanced through monitoring of power dissipation via embedded temperature sensors. The cross-cutting approach is not only expected to increase the component-level trust that can be established when new IoT devices are introduced into the network, but also to improve run-time reliability by capturing abnormal operations due to malicious intrusions or hardware faults based on the wirelessly transmitted on-chip temperature profiles from the IoT devices.

NSF Abstract: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146754&HistoricalAwards=false


CC*: A Software-Defined Edge Infrastructure Testbed for Full-stack Data-Driven Wireless Network Applications

Interdisciplinary research advances often require devices to collect, process, and transfer large scientific datasets over high bandwidth links. The overarching goal of this project is to build a wireless virtual network testbed at Saint Louis University, in collaboration with Northeastern University, to evaluate network management solutions that integrate the use of machine learning and artificial intelligence with programmable radios and programmable network switches. To evaluate the proposed innovation in computer networking, the cyberinfrastructure will be used to prototype network protocols and systems in support of a few interdisciplinary initiatives on campus.

In particular, this project’s contributions will be developed around the integration of learning techniques with network mechanisms such as medium access control, routing, and transport services. First, the team will explore the design and implementation of effective transport and routing protocols that integrate the network stack at different scopes using recent advances in reinforcement learning. Second, novel network architectures will be proposed integrating edge network mechanisms with federated and split learning techniques. Third, cross-layer distributed learning protocols will be designed to create self-adaptive wireless networks. Such solutions will be tested on campus and on other network testbeds.

By combining synergies from the fields of data science and network virtualization protocols and architectures, this work will lay the foundation for further research in adaptive resource management for (wireless) edge computing applications that can improve the quality of life in our society. This project’s results will be valuable for other fields interested in real-time prediction, such as robotics, medicine, anthropology, and finance. The research in this project will be impactful also thanks to the planned industry and international collaborations. Students from underrepresented groups will be involved with research activities and hackathon events on campuses in Missouri and Maine.

The project will have a web presence at: https://cs.slu.edu/testbed/. Such website will be maintained by the Computer Science Department at Saint Louis University, and will be active at least 5 years beyond the end date of this project. The website will contain links to datasets collected with the testbed, technical reports, scientific publications, and code repositories developed by students and collaborators.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

NSF Abstract: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2201536&HistoricalAwards=false


SHF: Self-Adaptive Interference-Avoiding Wireless Receiver Hardware through Real-Time Learning-Based Automatic Optimization of Power-Efficient Integrated Circuits

The sheer number of devices in the Internet of Things (IoT) is creating an extremely crowded and dynamic spectrum environment. As such, continuous and seamless adaptation of wireless-communication parameters will become essential in the next few years. On the other hand, typical radio-frequency (RF) integrated circuits are statically optimized for a fixed set of parameters and communication standards, which leaves limited room for real-time optimization at the intersection of hardware and software. Indeed, many of today’s devices are tailored for worst-case scenarios associated with a particular communication standard, which leads to limited performance and excessive power consumption. Conversely, real-time optimization allows to quickly reconfigure circuits for energy-efficient operation while achieving system-level performance goals. This challenge will be addressed by devising Radio Real-Time Machine Learning (RadioRTML), a platform that will demonstrate the feasibility of automatic RF integrated-circuit optimization through machine learning (ML) techniques directly implemented with reconfigurable hardware. This research will transform how the optimization of radio frequency systems is done today by demonstrating that real-time ML-based adaptation of RF parameters is able to achieve significant performance improvements. The outcomes will have long-lasting benefits for the design and optimization of low-power RF circuits for adaptive energy-efficient communication. Furthermore, the project will provide unique training for graduate and undergraduate students at the crossroads of machine learning and integrated circuit design.

Automatic machine-learning-based optimization of RF integrated circuits will be investigated through digital control of analog RF front-end circuits. Novel deep reinforcement-learning (DRL) algorithms will be developed to deliver unprecedented flexibility while improving energy efficiency and minimizing interference impacts. A key challenge in the application of DRL is to design a policy network expressive enough to achieve the required performance, yet implementable in a resource-constrained embedded IoT platform. For this reason, new techniques for effective and efficient policy network design will be created. Since DRL is known to exhibit slow convergence times and high energy consumption, this research will include the design of novel transfer-learning techniques to speed up DRL convergence, and it will leverage edge-computing techniques to significantly reduce the energy consumption of the platform. At the RF circuit level, customized topologies and design techniques will be created to construct a flexible receiver front-end. RadioRTML will be prototyped on a System-on-Chip (SoC)-based software-defined radio (SDR) connected to a custom-designed printed circuit board for the RF front-end chip. To thoroughly train and test the RadioRTML algorithms, large-scale data collection will be performed utilizing Arena (a 64-antenna 24-SDR system located at Northeastern University), Colosseum (the world’s largest network emulator), and the NSF POWDER testbed.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

NSF Abstract: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218845


SWIFT: AI-based Sensing for Improved Resiliency via Spectral Adaptation with Lifelong Learning

Resilience to interference via improved spectrum access requires fast sensing, cognition, and actionable intelligence to algorithmically enforce compliance in real-time. The ability to measure spectrum usage, quantify legitimate uses, detect violations and enforce compliance directly leads to improved spectrum utilization, coexistence of multiple competing users, and enhanced security. To this end, this SWIFT project will demonstrate a system for spectral situational awareness through radio frequency (RF) machine learning (ML). The key objective is to obtain actionable spectrum intelligence in the sub-6 GHz legacy bands through a real-time understanding of waveform shapes, spectral content, and modulation schemes. The research will create lifelong incremental learning approaches to spectrum management and dynamic spectrum access, enabled by advanced hardware innovations.

The project is expected to improve at least 100x over software-based systems, through a combination of array processing, reliable AI with lifelong learning algorithms, low-complexity AI, and digital signal processing. Specifically, AI techniques will be used to achieve spectrum intelligence, and more specifically data driven techniques, such as deep learning, towards real-time processing of wideband multi-directional RF signals carrying a diverse set of waveforms, modulations, and protocols. Led by Florida International University (FIU) – South Florida’s largest public research R1 university with 67+% Hispanic students, this SWIFT team will include many under-represented students, who in summer research, will learn key concepts in spectrum sensing. PIs at Embry-Riddle Aeronautical University will spearhead efforts in mentoring women in science, technology, engineering, and mathematics. The PI at Northeastern University will focus on creating graduate teaching materials in wireless communications and RF-ML based on Colosseum (the world’s largest RF emulator) and the PAWR platforms. The team will develop and maintain public open datasets for training AI radios for usability and reproducibility of the scientific community.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

NSF Abstract: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229472


ONR YIP: Polymorphic Wireless Computing for Ultra-Wideband 6G Spectrum Dominance

Achieving technological dominance in 6G wireless networks is of fundamental strategic importance to the economic well-being of the United States. To concretely deploy terabit-per-second communication links, fundamental research breakthroughs are needed to rethink how computing will be conducted in 6G wireless systems. To this end, this project will lay the foundations of a new paradigm named polymorphic wireless computing. We will research novel techniques that will seamlessly adapt not only the underlying algorithmic structure, but also the hardware and software structure of the 6G wireless platform according to ongoing mission-driven objectives, existing network/spectrum operating conditions, and current performance metrics of interest, while being able to operate at several GHz of bandwidth. To this end, we will perform highly interdisciplinary research at the intersection of machine learning, embedded systems, wireless networking and wireless security. To reduce our research to practice, we will prototype our techniques on software-defined radio platforms equipped with reconfigurable hardware; and leverage unique state-of-the-art facilities at Northeastern University to perform extensive experimental evaluation in realistic use-case scenarios.


AFOSR YIP: Dynamic Data Driven Open Radio Access Systems

In this project, we will research the underlying algorithmic foundations and theoretical performance bounds of future dynamic data-driven wireless systems. The key target of this project is to achieve strategic mission-critical application-level objectives by guaranteeing assured wireless communications in congested, contested, concealed, and contaminated environments. First, we mathematically optimize network operations through dynamic allocation of network, computation, and memory resources, taking into account the semantics of the sensed data and the current spectrum conditions into the problem formulation. Importantly, our optimization dynamically compresses sensed data according to application-level semantics, so that network load can be minimized while still guaranteeing key application performance indicators. Next, we investigate novel techniques to guarantee the performance of the proposed data-driven optimization and classification techniques in terms of latency, accuracy, and resource occupation in challenged and constrained scenarios. Our key theoretical findings will be extensively validated through extensive data collection campaigns leveraging several wireless testbeds available at the Institute for the Wireless Internet of Things.

The Air Force Office of Scientific Research, or AFOSR, expands the horizon of scientific knowledge through its leadership and management of the Department of the Air Force’s basic research program. As a vital component of the Air Force Research Laboratory (AFRL), AFOSR’s mission is to discover, shape, champion and transitions high- risk basic research that profoundly impacts the future Air and Space Forces. AFOSR accomplishes its mission through global investment in advanced discovery research efforts in relevant scientific areas. Central to AFOSR’s strategy is the transfer of the fruits of basic research to industry, the supplier of Department of the Air Force acquisitions; to the academic community, which can lead the way to still more accomplishment; and to the other directorates of AFRL that carry the responsibility for applied research leading to acquisition.

AFOSR Young Investigator Award Press Release: https://www.afrl.af.mil/News/Article-Display/Article/3245790/afrlafosr-awards-25-million-via-young-investigator-research-program/