SWIFT: Electric Field Controlled Integrated Multiferroic Radio Frequency Devices for Interference Immune Broadband Wireless Systems

Wireless networks are a mainstay of modern society, enabling communication, synchronization, and organization though 5th generation networks (5G) and the Internet-of-Things (IoT). Growth in the number of network participants and therefore the amount of machine-to-machine communication has exacerbated radio spectrum scarcity, making it increasingly valuable: its efficient utilization is vital.

Devices like smartphones, radios, televisions, routers, vehicles, and computers are approaching the limits of existing approaches to spectrum sharing. Smartphone developers already require upwards of 100 unique filters to serve the needs of different communication schemes such as Bluetooth, LTE, AM and FM radio, 5G, and Zigbee. This project will investigate the use of novel self-biased, multiferroic materials in conjunction with novel algorithms to learn how other network participants are using time and spectrum and cooperatively adapt to increase the communication possible within a limited spectrum.

The new multiferroic materials, devices, and circuits will enable very rapidly adaptable filters, which in turn will enable monitoring, predicting, adapting to, and coexisting with other network participants. Instead of requiring that all network participants conform to a particular spectrum sharing protocol, the project advances a decentralized framework within which "smart" network participants learn predictive models of other, potentially legacy or passive, participants and use these models to maximize spectrum sharing efficiency.

The end result is more communication among more network participants using the same amount of wireless spectrum, with greater resistance to jamming and more compact wireless devices such as smartphones and wireless sensors.

The project uses novel material and device technologies to enable flexible, rapidly tunable filters within receivers that in turn enable implicit wireless environment monitoring and prediction. The gathered information will be used by jamming prevention techniques and transmission schedule prediction algorithms that enable scheduling of future transmissions to maximize spectrum sharing efficiency.

Conventional ferromagnetic based RF components require a DC or variable external magnetic field bias for operation and tuning, making them power hungry, bulky, and impractical to integrate. This project leverages self-biased multiferroic hetero-structures enabled by enhancing the self-biasing of a magnetic insulator through interface exchange coupling between a stratified thin, highly magnetostrictive, metallic ferromagnetic layer and a layer of ferroelectric material to achieve total electric field control without needing any fixed or variable magnetic bias.

The total electric field control of ferromagnetic resonators enables tunable, very low latency, integrated compact filters that require no DC magnetic bias. Algorithms will be developed alongside these highly reconfigurable filters to realize receivers that are impervious to various kinds of interference and more capable of characterizing their wireless environments, and this information will be used to learn predictive models of transmission patterns, enabling efficient spectrum sharing among network participants, without requiring them to adhere to a particular sharing protocol.

The project will evaluate conventional approaches to transmission timeseries prediction such as long shortterm memories, but will also consider alternatives better supporting rapid search for efficient transmission schedules, including causal and distributed learning. These predictions will be used to inform the optimization of transmission schedules and channel use to avoid interference and improve goodput.

The project will make scientific and engineering contributions in the areas of multiferroic materials, analog RF circuit design, network spectrum sharing, decentralized network protocols, and machine learning for predictive modeling.

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.