Over-the-air analog computation allows offloading computation to the wireless environment through carefully constructed transmitted signals. In this paper, we design and implement the first-of-its-kind convolution that uses over-the-air computation and demonstrate it for inference tasks in a convolutional neural network (CNN). We engineer the ambient wireless propagation environment through reconfigurable intelligent surfaces (RIS) to design such an architecture, which we call ’AirNN’. AirNN leverages the physics of wave reflection to represent a digital convolution, an essential part of a CNN architecture, in the analog domain. In contrast to classical communication, where the receiver must react to the channel-induced transformation, generally represented as finite impulse response (FIR) filter, AirNN proactively creates the signal reflections to emulate specific FIR filters through RIS. AirNN involves two steps: first, the weights of the neurons in the CNN are drawn from a finite set of channel impulse responses (CIR) that correspond to realizable FIR filters. Second, each CIR is engineered through RIS, and reflected signals combine at the receiver to determine the output of the convolution. This paper presents a proof-of-concept of AirNN by experimentally demonstrating convolutions with over-the-air computation. We then validate the entire resulting CNN model accuracy via simulations for an example task of modulation classification.

We propose a novel distributed beamforming framework for UAVs, called SABRE, wherein airborne transmitters synchronize their operations for data communication with target receivers. SABRE chooses the best-suited subset of transmitters that maximizes user-defined QoS, considering relative distances from receivers, traffic characteristics, cumulative SNR desired at the receiver, and individual SNR estimated for each link. This paper makes three main contributions: (i) It shows how to achieve distributed beamforming in challenging, aerial hovering conditions by accurately synchronizing start-times and eliminating relative clock offsets. (ii) It proposes an algorithm with polynomial complexity that groups transmitters and chooses the receiver, maximizing the number of satisfied receivers in each round. (iii) It experimentally validates the concept of aerial beamforming in a testbed composed of four DJI-M100 UAVs in realistic outdoor environments. We follow this up with at-scale emulation involving beamforming with multiple candidate UAV transmitters in Colosseum, the world’s largest RF emulator. SABRE keeps the overall network frame error rate below 10% with a probability of 0.95 and manifests a 40% improvement in meeting user QoS thresholds over classical resource allocation methods. From a community viewpoint, the beamforming code, UAV interfacing designs, and the Colosseum container will be released publicly, allowing further independent investigations.

Retailers are aiming to enhance customer experience by automating the checkout process. The key impediment here is the effort to manually align the product barcode with the scanner, requiring sequential handling of items without blocking the line-of-sight of the laser beam. While recent systems such as Amazon Go eliminate human involvement using an extensive array of cameras, we propose a privacy-preserving alternative, RFGo, that identifies products using passive RFID tags. Foregoing continuous monitoring of customers throughout the store, RFGo scans the products in a dedicated checkout area that is large enough for customers to simply walk in and stand until the scan is complete (in two seconds). Achieving such low-latency checkout is not possible with traditional RFID readers, which decode tags using one antenna at a time. To overcome this, RFGo includes a custom-built RFID reader that simultaneously decodes a tag's response from multiple carrier-level synchronized antennas enabling a large set of tag observations in a very short time. RFGo then feeds these observations to a neural network that accurately distinguishes the products within the checkout area from those that are outside. We build a prototype of RFGo and evaluate its performance in challenging scenarios. Our experiments show that RFGo is extremely accurate, fast and well-suited for practical deployment in apparel stores.

We propose AirBeam, the first complete algorithmic framework and systems implementation of distributed air-to-ground beamforming on a fleet of UAVs. AirBeam synchronizes software defined radios (SDRs) mounted on each UAV and assigns beamforming weights to ensure high levels of directivity. We show through an exhaustive set of the experimental studies on UAVs why this problem is difficult given the continuous hovering-related fluctuations, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power and cost (SWaP-C) constraints for UAVs. AirBeam addresses these challenges through: (i) a channel state estimation method using Gold sequences that is used for setting the suitable beamforming weights, (ii) adaptively starting transmission to synchronize the action of the distributed radios, (iii) a channel state feedback process that exploits statistical knowledge of hovering characteristics. Finally, AirBeam provides insights from a systems integration viewpoint, with reconfigurable B210 SDRs mounted on a fleet of DJI M100 UAVs, using GnuRadio running on an embedded computing host.

The growing spectrum crunch has motivated exploratory efforts in the use of LTE in the 5 GHz bands for downlink traffic. However, this paradigm raises concerns of fair sharing of the spectrum and the adverse impact of scheduled LTE frames on Wi-Fi Packet Success Rates (PSR). To address this issue, we propose E-Fi, an interference-evasion mechanism that allows Wi-Fi devices to survive LTE transmissions without any cooperation between these two different standards. Different from existing approaches, we argue that the simple use of Almost Blank Subframes (ABS) within the LTE standard offering short channel access windows overestimates opportunities for Wi-Fi. The pilots embedded in the ABS not only interfere with Wi-Fi but also adversely impact the carrier sensing function. E-Fi mitigates this problem through a two-fold approach. It uses a combination of (i) Wi-Fi Direct with packet relaying and (ii) classical distributed coordination function to reach distant nodes. Second, it ensures load balancing for both Wi-Fi uplink and downlink traffic with high PSR by creating node-groups based with dedicated contention-based medium access intervals. Our approach is validated by comprehensive simulation and experimental results that indicate significantly higher throughput in E-Fi compared to classical Wi-Fi.