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This paper presents Metamorph, a system that generates imperceptible audio that can survive over-the-air trans- mission to attack the neural network of a speech recognition system. The key challenge stems from how to ensure the added perturbation of the original audio in advance at the sender side is immune to unknown signal distortions during the transmission process. Our empirical study reveals that signal distortion is mainly due to device and channel frequency selectivity but with different characteristics. This brings a chance to capture and further pre-code this impact to generate adversarial examples that are robust to the over-the-air transmission. We leverage this opportunity in Metamorph and obtain an initial perturbation that captures the core distortion’s impact from only a small set of prior measurements, and then take advantage of a domain adaptation algorithm to refine the perturbation to further im- prove the attack distance and reliability. Moreover, we consider also reducing human perceptibility of the added perturbation. Evaluation achieves a high attack success rate (90%) over the attack distance of up to 6 m. Within a moderate distance, e.g., 3 m, Metamorph maintains this high success rate, yet can be further adapted to largely improve the audio quality, confirmed by a human perceptibility study. 

Conventional thinking treats the wireless channel as a given constraint. Therefore, wireless network designs to date center on the problem of the endpoint optimization that best utilizes the channel, for example, via rate and power control at the transmitter or sophisticated decoding mechanisms at the receiver. We instead explore whether it is possible to reconfigure the environment itself to facilitate wireless communication. In this work, we instrument the environment with a large array of inexpensive antennas (LAIA) and design algorithms to configure them in real time. Our system achieves this level of programmability through rapid adjustments of an on-board phase shifter in each LAIA device. We design a channel decomposition algorithm to quickly estimate the wireless channel due to the environment alone, which leads us to a process to align the phases of the array elements. Variations of our core algorithm can then optimize wireless channels on the fly for single- and multi-antenna links, as well as nearby networks operating on adjacent frequency bands. We design and deploy a 36-element passive array in a real indoor home environment. Experiments with this prototype show that, by reconfiguring the wireless environment, we can achieve a 24% TCP throughput improvement on average and a median improvement of 51.4% in Shannon capacity over the baseline single-antenna links. Over the baseline multi-antenna links, LAIA achieves an improvement of 12.23% to 18.95% in Shannon capacity. 

Conventional thinking treats the wireless channel as a constraint, so wireless network designs to date target endpoint designs that best utilize the channel. Examples include rate and power control at the transmitter, sophisticated receiver decoder designs, and high-performance forward error correction for the data itself. We instead explore whether it is possible to reconfigure the environment itself to facilitate wireless communication. In this work, we instrument the environment with a large array of inexpensive antenna (LAIA) elements, and design algorithms to configure LAIA elements in real time. Our system achieves a high level of programmability through rapid adjustments of an on-board phase shifter in each LAIA element. We design a channel decomposition algorithm to quickly estimate the wireless channel due to the environment alone, which leads us to a process to align the phases of the LAIA elements. Variations of our core algorithm then improve wireless channels on the fly for singleand multi-antenna links, as well as nearby networks operating on adjacent frequency bands. We implement and deploy a 36-element LAIA array in a real indoor home environment. Experiments in this setting show that, by reconfiguring the wireless environment, we can achieve a 24% TCP throughput improvement on average and a median improvement of 51.4% in Shannon capacity over baseline single-antenna links. Over baseline multi-antenna links, LAIA achieves an improvement of 12.23% to 18.95% in Shannon capacity. 

Smart spaces, such as smart homes and smart offices, are common Internet of Things (IoT) scenarios for building automation with networked sensors. In this paper, we suggest a different notion of smart spaces, where the radio environment is programmable to achieve desirable link quality within the space. We envision deploying low-cost devices embedded in the walls of a building to passively reflect or actively transmit radio signals. This is a significant departure from typical approaches to optimizing endpoint radios and individual links to improve performance. In contrast to previous work combating or leveraging per-link multipath fading, we actively reconfigure the multipath propagation. We sketch design and implementation directions for such a programmable radio environment, highlighting the computational and operational challenges our architecture faces. Preliminary experiments demonstrate the efficacy of using passive elements to change the wireless channel, shifting frequency "nulls" by nine Wi-Fi subcarriers, changing the 2 x 2 MIMO channel condition number by 1.5 dB, and attenuating or enhancing signal strength by up to 26 dB.