ARO ECP: Neural-Approximate Beamforming

This project will establish the scientific foundations of a brand-new approach to MIMO communications named Neural-Approximate Beamforming (NAB). A critical issue of current approaches is that they leverages fixed digital signal processing paradigms to implement beamforming. Ultimately, this aspect (i) makes the wireless system prone to a series of critical security vulnerabilities; (ii) does not scale well as a function of bandwidth and number of antennas; and (iii) cannot dynamically achieve the complex trade-off between concealment, performance and resilience needed by tactical wireless networks. To address these issues, our key intuition is that beams do not need to be perfectly formed to deliver sufficient performance. Therefore, from a theoretical perspective, the communication and computational burden of beamforming can be approximated in the time, antenna and frequency dimensions by using dynamic neural networks adapted in real time based on mission requirements. There are key challenges that need to be solved to concretely realize NAB. First, new theoretical bounds must be computed to find how much beamforming approximation can be tolerated in a given propagation scenario and under different size, weight and power constraints without losing in performance and level of concealment. Next, such theoretical findings need to be incorporated in the design of novel dynamic neural networks architectures that will approximate not only the amount of beamforming feedback sent back to the beamformer, but also the digital signal processing itself, to always deliver the best performance at the right time with minimum computational and communication burden. To acquire the necessary real-world data needed to validate our theoretical findings and train/test the developed dynamic neural networks, we will perform extensive experimental evaluation with state-of-the-art wireless facilities at Northeastern University.