“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

National Institute of Standards and Technology における超伝導研究及び生活

This paper presents the design and implementation of the first in-band full duplex WiFi radios that can simultaneously transmit and receive on the same channel using standard WiFi 802.11ac PHYs and achieves close to the theoretical doubling of throughput in all practical deployment scenarios. Our design uses a single antenna for simultaneous TX/RX (i.e., the same resources as a standard half duplex system). We also propose novel analog and digital cancellation techniques that cancel the self interference to the receiver noise floor, and therefore ensure that there is no degradation to the received signal. We prototype our design by building our own analog circuit boards and integrating them with a fully WiFi-PHY compatible software radio implementation. We show experimentally that our design works robustly in noisy indoor environments, and provides close to the expected theoretical doubling of throughput in practice.

https://dl.acm.org/doi/pdf/10.1145/2486001.2486033

Full duplex radios

This paper presents the design and implementation of SpotFi, an accurate indoor localization system that can be deployed on commodity WiFi infrastructure. SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems. SpotFi makes two key technical contributions. First, SpotFi incorporates super-resolution algorithms that can accurately compute the angle of arrival (AoA) of multipath components even when the access point (AP) has only three antennas. Second, it incorporates novel filtering and estimation techniques to identify AoA of direct path between the localization target and AP by assigning values for each path depending on how likely the particular path is the direct path. Our experiments in a multipath rich indoor environment show that SpotFi achieves a median accuracy of 40 cm and is robust to indoor hindrances such as obstacles and multipath.

https://dl.acm.org/doi/pdf/10.1145/2785956.2787487

SpotFi: Decimeter Level Localization Using WiFi

Self-interference cancellation invalidates a long-held fundamental assumption in wireless network design that radios can only operate in half duplex mode on the same channel. Beyond enabling true in-band full duplex, which effectively doubles spectral efficiency, self-interference cancellation tremendously simplifies spectrum management. Not only does it render entire ecosystems like TD-LTE obsolete, it enables future networks to leverage fragmented spectrum, a pressing global issue that will continue to worsen in 5G networks. Self-interference cancellation offers the potential to complement and sustain the evolution of 5G technologies toward denser heterogeneous networks and can be utilized in wireless communication systems in multiple ways, including increased link capacity, spectrum virtualization, any-division duplexing (ADD), novel relay solutions, and enhanced interference coordination. By virtue of its fundamental nature, self-interference cancellation will have a tremendous impact on 5G networks and beyond.

Applications of self-interference cancellation in 5G and beyond

Wireless communication has become an intrinsic part of modern implantable medical devices (IMDs). Recent work, however, has demonstrated that wireless connectivity can be exploited to compromise the confidentiality of IMDs' transmitted data or to send unauthorized commands to IMDs---even commands that cause the device to deliver an electric shock to the patient. The key challenge in addressing these attacks stems from the difficulty of modifying or replacing already-implanted IMDs. Thus, in this paper, we explore the feasibility of protecting an implantable device from such attacks without modifying the device itself. We present a physical-layer solution that delegates the security of an IMD to a personal base station called the shield. The shield uses a novel radio design that can act as a jammer-cum-receiver. This design allows it to jam the IMD's messages, preventing others from decoding them while being able to decode them itself. It also allows the shield to jam unauthorized commands---even those that try to alter the shield's own transmissions. We implement our design in a software radio and evaluate it with commercial IMDs. We find that it effectively provides confidentiality for private data and protects the IMD from unauthorized commands.

/pdf/they-can-hear-your-heartbeats-non-invasive-security-for-3i8bz9ngw0.pdf

They can hear your heartbeats: non-invasive security for implantable medical devices

We consider the sparse Fourier transform problem: given a complex vector x of length n, and a parameter k, estimate the k largest (in magnitude) coefficients of the Fourier transform of x. The problem is of key interest in several areas, including signal processing, audio/image/video compression, and learning theory.We propose a new algorithm for this problem. The algorithm leverages techniques from digital signal processing, notably Gaussian and Dolph-Chebyshev filters. Unlike the typical approach to this problem, our algorithm is not iterative. That is, instead of estimating "large" coefficients, subtracting them and recursing on the reminder, it identifies and estimates the k largest coefficients in "one shot", in a manner akin to sketching/streaming algorithms. The resulting algorithm is structurally simpler than its predecessors. As a consequence, we are able to extend considerably the range of sparsity, k, for which the algorithm is faster than FFT, both in theory and practice.

/pdf/simple-and-practical-algorithm-for-sparse-fourier-transform-3fk4pqgg1s.pdf

Simple and practical algorithm for sparse Fourier transform

There is a long-standing vision of embedding backscatter nodes like RFIDs into everyday objects to build ultra-low power ubiquitous networks. A major problem that has challenged this vision is that backscatter communication is neither reliable nor efficient. Backscatter nodes cannot sense each other, and hence tend to suffer from colliding transmissions. Further, they are ineffective at adapting the bit rate to channel conditions, and thus miss opportunities to increase throughput, or transmit above capacity causing errors. This paper introduces a new approach to backscatter communication. The key idea is to treat all nodes as if they were a single virtual sender. One can then view collisions as a code across the bits transmitted by the nodes. By ensuring only a few nodes collide at any time, we make collisions act as a sparse code and decode them using a new customized compressive sensing algorithm. Further, we can make these collisions act as a rateless code to automatically adapt the bit rate to channel quality --i.e., nodes can keep colliding until the base station has collected enough collisions to decode. Results from a network of backscatter nodes communicating with a USRP backscatter base station demonstrate that the new design produces a 3.5× throughput gain, and due to its rateless code, reduces message loss rate in challenging scenarios from 50% to zero.

/pdf/efficient-and-reliable-low-power-backscatter-networks-1bobnw3uv0.pdf

Efficient and reliable low-power backscatter networks

We consider the problem of computing the k-sparse approximation to the discrete Fourier transform of an n-dimensional signal. We show: 
* An O(k log n)-time randomized algorithm for the case where the input signal has at most k non-zero Fourier coefficients, and 
* An O(k log n log(n/k))-time randomized algorithm for general input signals. 
Both algorithms achieve o(n log n) time, and thus improve over the Fast Fourier Transform, for any k = o(n). They are the first known algorithms that satisfy this property. Also, if one assumes that the Fast Fourier Transform is optimal, the algorithm for the exactly k-sparse case is optimal for any k = n^{\Omega(1)}. 
We complement our algorithmic results by showing that any algorithm for computing the sparse Fourier transform of a general signal must use at least \Omega(k log(n/k)/ log log n) signal samples, even if it is allowed to perform adaptive sampling.

Nearly Optimal Sparse Fourier Transform

We consider the problem of computing the k-sparse approximation to the discrete Fourier transform of an n-dimensional signal. We show: An O(k log n)-time randomized algorithm for the case where the input signal has at most k non-zero Fourier coefficients, and An O(k log n log(n/k))-time randomized algorithm for general input signals. Both algorithms achieve o(n log n) time, and thus improve over the Fast Fourier Transform, for any k=o(n). They are the first known algorithms that satisfy this property. Also, if one assumes that the Fast Fourier Transform is optimal, the algorithm for the exactly k-sparse case is optimal for any k = nΩ(1). We complement our algorithmic results by showing that any algorithm for computing the sparse Fourier transform of a general signal must use at least Ω(k log (n/k) / log log n) signal samples, even if it is allowed to perform adaptive sampling.

/pdf/nearly-optimal-sparse-fourier-transform-2hy1kv14z4.pdf

Haitham Hassanieh

Papers

They can hear your heartbeats: non-invasive security for implantable medical devices

Simple and practical algorithm for sparse Fourier transform

Efficient and reliable low-power backscatter networks

Nearly Optimal Sparse Fourier Transform

Nearly optimal sparse fourier transform