Data Driven Radio Frequency Learning and Spectrum Sharing in Congested and Contested Wireless Environment
Abstract
In modern warfare, coexistence of heterogeneous wireless systems with large number of inter-connected devices and adversarial wireless systems will result in a highly congested and contested radio frequency (RF) environment. Therefore, an ecient and secure use of spec- trum is essential. However, there exist many challenges: (1) multiple wireless transmissions may happen in the same frequency band at the same time (either due to spectrum sharing, say NOMA, or attacks) that result in superposed RF traces, and there is very limited or no prior knowledge of the potential (adversarial) wireless systems; (2) robust throughput at the physical and MAC layer is hard to achieve due to the competition by large number of wireless devices and diverse wireless technologies they employ; (3) impersonation attacks are hard to detect or prevent when an attacker choose to mimic legitimate users by using the same wire- less devices and the same waveform; (4) validation needs to be done using real-world data. The proposed research will address these fundamental challenges by a data-driven approach and an RF learning framework is proposed to achieve situational awareness in the RF domain for complex wireless environment. Speci cally, four research tasks are proposed: (1) a multi- task learning based approach is proposed to infer the number of wireless transmissions and the types of waveforms they use in a given spectrum from raw I/Q data; equipped with this knowledge, potential throughput gain at the physical layer will be studied; (2) an optimized MAC protocol will be designed to take advantage of the inferred knowledge from task 1 so that a robust throughput can be achieved; (3) a reconstruction based approach is proposed to detect attacker in RF domain using adversarial autoencoders and generative adversarial networks; (4) extensive simulations using MATLAB and NS-2/3 will be performed. Real world RF traces representing typical wireless environment will be collected from a Universal Software Radio Peripheral (USRP) based testbed and the POWDER testbed. The data will be curated and stored in a repository. Deep learning experiments using various software plat- forms will be carried out and a visualization tool will be developed to validate the proposed approach. Together they provide secure and intelligent spectrum sharing through RF learning to improve robustness and security of future wireless systems. It will foster cross-fertilization of ideas from di erent research societies such as wireless networks and machine learning, using a joint theoretical and experimental approach. The proposed activities will support the Department of ECE at Prairie View A&M University (PVAMU), an HBCU, to build a strong research program and increase student enrollment and graduation. Furthermore, the example will lead PVAMU to implement a new paradigm in research and education for building institutional capacity beyond traditional teaching programs. It will greatly improve African American students participation in high-tech engineering research and train them to be the future workforce that is extremely valuable to the DOD and the nation.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 13, 2023
- Source ID
- W911NF2310214
Entities
People
- Lijun Qian
Organizations
- Army Contracting Command
- Prairie View State College
- United States Army