This paper introduces M3oCap, a millimeter-wave (mmWave) tag-based motion capture system that delivers accurate 6 degrees of freedom motion tracking. M3oCap utilizes a single commercial mmWave radar and custom-designed mmWave backscatter tags (2.5 cm × 3 cm) to localize and track the motion of tagged objects. Our system features novel algorithms that effectively isolate weak backscattered signals while accurately recovering phase changes induced by motion, allowing high-rate tracking of tag movements. Experiments show that M3oCap achieves performance close to commercial motion capture systems – it provides 1000 measurements per second with a median range tracking accuracy of 250 μ m, median localization accuracies of 3.34 mm, 3.95 mm, and 4.20 mm in x, y, z dimensions, and median orientation tracking accuracies of 1.7°, 2.3°, and 2.0° across pitch, roll, and yaw. We further demonstrate the system’s fine-grained movement tracking capabilities through various motion capture tasks.

A stable environment is critical for preserving product quality in industries such as pharmaceuticals and perishable goods logistics. While current RFID-based sensors enable wireless monitoring of temperature and moisture, they lack the ability to record threshold violations without continuous reader power or battery-supported logging. This paper presents AntSense, a battery-free RFID sensor that leverages stimuli-responsive structures to physically record environment violation events during the transit even when there is no reader available. When exposed to temperature or moisture beyond thresholds, memory alloy or solvable materials undergo irreversible geometric deformation, altering the integrated antenna’s characteristics. These changes remain detectable via standard RFID readers even after environment conditions normalize, enabling verification of transit integrity. Our prototype evaluations demonstrate reliable detection of threshold violations in real-world scenarios. By combining passive operation with persistent event recording, AntSense offers a scalable solution for supply chain monitoring without compromising the maintenance-free advantages of RFID technology.

Intelligent irrigation based on measurements of soil moisture levels in every pot in a greenhouse can not only improve plant productivity and quality but also save water. However, existing soil moisture sensors are too expensive to deploy in every pot. We therefore introduce GreenTag, a low-cost RFID-based soil moisture sensing system whose accuracy is comparable to that of an expensive soil moisture sensor. Our key idea is to attach two RFID tags to a plant’s container so that changes in soil moisture content are reflected in their Differential Minimum Response Threshold (DMRT) metric at the reader. We show that a low-pass filtered DMRT metric is robust to changes both in the RF environment (e.g., from human movement) and in pot locations. In addition, we propose a fast DMRT acquisition algorithm and a time-efficient tag query protocol, which can reduce the sensing latency by 90%. In a realistic setting, GreenTag achieves a 90-percentile moisture estimation errors of 5%, which is comparable to the 4% errors using expensive soil moisture sensors. Moreover, this accuracy is maintained despite changes in the RF environment and container locations. We also show the effectiveness of GreenTag in a real greenhouse.

We present NeWRF, a novel deep-learning-based framework for predicting wireless channels. Wireless channel prediction is a long-standing problem in the wireless community and is a key technology for improving the coverage of wireless network deployments. Today, a wireless deployment is evaluated by a site survey which is a cumbersome process requiring an experienced engineer to perform extensive channel measurements. To reduce the cost of site surveys, we develop NeWRF, which is based on recent advances in Neural Radiance Fields (NeRF). NeWRF trains a neural network model with a sparse set of channel measurements, and predicts the wireless channel accurately at any location in the site. We introduce a series of techniques that integrate wireless propagation properties into the NeRF framework to account for the fundamental differences between the behavior of light and wireless signals. We conduct extensive evaluations of our framework and show that our approach can accurately predict channels at unvisited locations with significantly lower measurement density than prior state-of-the-art.

Advancements in computing have enabled emerging applications such as telesurgery, robot automation, holographic telepresence, and extended reality, which require gigabitper-second throughput, sub-millisecond latency, and highly reliable wireless connectivity. Millimeter wave (mmWave) technology has promised to enable such connectivity by operating over a large bandwidth in the high-frequency spectrum bands (24 GHz and above). However, due to the short wavelength and high directionality of mmWave signals, mmWave networks have limited coverage and are highly susceptible to blockage. In particular, high-data-rate mmWave networks work reliably only when there is a clear line-of-sight (LOS) path between users and base stations. Unfortunately, due to this problem, mmWave networks have not been able to scale and become ubiquitous. Past work has proposed mmWave repeaters and intelligent surfaces to solve this issue by rerouting signal around blockages. However, these solutions are expensive and complex to build, consume high power, or/and require constant feedback from the network to operate since they use active techniques for beam steering. In this paper, we present the first mmWave repeater which uses passive beamforming technique. Our repeater is low-cost, low-power, and can support multiple users simultaneously. Most importantly, it does not require any feedback from the network to operate. Hence, it can be easily deployed on-demand to solve the coverage and blockage problem of mmWave networks whenever and wherever high-data-rate and low-latency connectivity is needed.

WiFi communication should be possible only between devices inside the same network. However, we find that all existing WiFi devices send back acknowledgments (ACKs) to even fake packets received from unauthorized WiFi devices outside of their network. Moreover, we find that an unauthorized device can manipulate the power-saving mechanism of WiFi radios and keep them continuously awake by sending specific fake beacon frames to them. Our evaluation of over 5000 devices from 186 vendors confirms that these are widespread issues. We believe these loopholes cannot be prevented, and hence they create privacy and security concerns. Finally, to show the importance of these issues and their consequences, we implement and demonstrate two attacks where an adversary performs battery drain and WiFi sensing attacks just using a tiny WiFi module which costs less than ten dollars.

Side-channels have traditionally been exploited as a means of uncovering sensitive information such as cryptographic keys from a computing device. In particular, past work has shown that electromagnetic (EM) radiation from a device’s processor and memory during the execution of code and data can be used by attackers to extract private information. In contrast, instead of considering side-channels and electromagnetic radiation as vulnerabilities, we see them as opportunities for wireless communication on resource-limited IoT devices. We present SideComm, a side-channel-based communication system that leverages processors’ EM side-channels to enable resource-limited IoT devices to wirelessly send their data without having any radios. The main advantage of this approach is completely eliminating the need for a conventional radio and antenna, which offers energy savings, simplicity, and flexibility for IoT devices. Our evaluation demonstrates SideComm’s ability to achieve a communication range of more than 10m (enabling ≥ 3 dB SNR at 15m) and to work in non-line-of-sight scenarios, such as around corners and through walls. We believe SideComm can enable increased connectivity for many resource-constrained IoT devices in smart environments.

Traditionally, side channels have been exploited to uncover sensitive information such as cryptographic keys from computing devices. An attacker can extract private information from a device’s processor and memory by using electromagnetic (EM) radiation while code and data are being executed. Rather than seeing side-channels and electromagnetic radiation as vulnerabilities, we consider them as potential wireless communication channels for resource-constrained devices. The main advantage of this approach is completely eliminating the need for a conventional radio and antenna, which offers energy savings, simplicity, and flexibility for resource-constrained devices.

With the advancement of the Internet of Things (IoT), many devices will be connected to the Internet, enabling digital twin and smart home applications. However, currently, these IoT devices are operating at lower frequency bands of the wireless spectrum, typically ranging from a few hundred MHz (such as RFID and LoRa) to a few GHz (such as BLE and WiFi). As a result, the current IoT devices not only place a huge strain on these bands, but also cannot benefit from the large bandwidth available in the higher frequencies of the spectrum such as mmWave bands. In this paper, our goal is to bring mmWave technology to existing IoT devices so they can benefit from the advantages this technology offers, such as high network capacity, low interference, and Space Division Multiple Access. To this end, we design mmPlug, a novel plug-and-play module which is simple and energy-efficient. mmPlug can be easily connected to the antenna port of any IoT device, enabling it to operate in the mmWave band. mm-Plug is compatible with different wireless technologies (such as WiFi, Lora, etc.) and does not require any modification to the circuit, firmware or communication protocols of the existing IoT devices. mmPlug achieves this by a novel design which can seamlessly be connected to the antenna port of the IoT device. We have implemented mmPlug on PCB and empirically evaluated its performance. Our results show that mmPlug enables existing IoT devices (such as WiFi and Lora) to operate at mmWave band while achieving accurate localization, uplink and downlink even when they are more than 30 m far from the access point.

Millimeter wave (mmWave) technology enables wireless devices to communicate using very high-frequency signals. Operating at those frequencies provides larger bandwidth which can be used to enable high-data-rate links, and very accurate localization of devices. However, radios operating at high-frequencies consume significant amount of power, making them unsuitable for applications with limited energy sources. This paper presents MilBack, a backscatter network operating at mmWave bands. Backscattering is the most energy-efficient wireless communication technique, where nodes piggyback their data on an access point's signal instead of generating their own signals. Eliminating the need for signal generation significantly reduces the energy-consumption of the nodes. In contrast to past mmWave backscatter work which supports only uplink, MilBack is the first mmWave backscatter network which supports uplink, downlink, and accurate localization. MilBack addresses the key challenges that prevent existing backscatter networks to enable both uplink and downlink at mmWave bands. We implemented MilBack and evaluated its performance empirically. Our results show that MilBack is capable of achieving accurate localization, uplink, and downlink communication at up to 8 m while consuming only 32 mW and 18 mW, respectively.

Recent years have seen significant advances in WiFi Localization. However, existing systems require either multiple access points to cooperate with each other or a single access point to have multiple antennas and transceiver chains. Therefore, they cannot be integrated into most IoT WiFi chipsets which have only a single transceiver chain. This paper presents WiSight, a novel approach to bringing WiFi localization to any WiFi devices, especially those with a single RF chain. We propose a WiFi antenna design and use the inherent properties of the 802.11 protocol to measure Angle-of-Arrival (AoA) and Time-of-Flight (ToF) using a single transceiver chain. Our proof-of-concept simulation and real world experiments promise the feasibility of this approach.

Radio-Frequency IDentification (RFID) technology permits a reader to wirelessly query a tag for its embedded globally unique identifier. Passive RFID tags, which are small, low-cost (a few cents each), and batteryless, can be reliably read only when they are within a few meters of the reader since the tag must power up itself by harvesting energy from the reader. Past work attempts to increase the RFID range by providing them with more energy, such as by synchronizing multiple custom design RFID readers and performing beamforming. However, we demonstrate that a passive tag's range is limited not only by the need for the tag to harvest energy but also by the need for the tag to decode the reader's transmission, and vice versa. Thus, instead of modifying readers, we ask if a tag's manufacturer can increase passive RFIDs' range by lowering the data rate. Our results show that the working range can be increased by a factor of about 10 by simply using a low data rate. Our real-world experiments using customized tag prototypes have a range of ~40 m, with an SNR exceeding 12 dB.

RFID and WiFi backscatter systems use similar techniques to enable battery-free wireless communication. Despite their similarities, existing WiFi backscatter systems achieve a much shorter range than RFID systems. The main reason for this limitation is self-interference. In particular, in any backscatter communication, the reader needs to transmit and receive at the same time. RFID systems do this by using full-duplex hardware. Unfortunately, existing WiFi devices do not have full-duplex capabilities. Therefore, to enable backscatter communication using existing WiFi devices, today's WiFi backscatter systems use two WiFi devices where one transmits and the other one receives. However, since the tag's reflection is very weak compared to the query signal, it is very challenging to detect and extract the tag's reflection from the query signal. This problem is known as self-interference. In this paper, we propose a novel approach for eliminating this problem in WiFi backscatter systems without any hardware modifications on existing WiFi devices. Our empirical evaluations show that our technique improves the range of in-channel WiFi backscatter systems to that of RFID in both line-of-sight and non-line-of-sight scenarios. Moreover, our approach enables WiFi backscatter communication even in scenarios where the tag has no LOS path to any WiFi device. None of existing WiFi backscatter systems work in this scenario.

WiFi backscatter communication has been proposed to enable battery-free sensors to transmit data using WiFi networks. The main advantage of WiFi backscatter technologies over RFID is that data from their tags can be read using existing WiFi infrastructures instead of specialized readers. This can potentially reduce the complexity and cost of deploying battery-free sensors. Despite extensive work in this area, none of the existing systems are in widespread use today. We hypothesize that this is because WiFi-based backscatter tags do not scale well and their range and capabilities are limited when compared with RFID. To test this hypothesis we conduct several real-world experiments.

Recent advances in IoT, machine learning and cloud computing have placed a huge strain on wireless networks. In particular, many emerging applications require streaming rich content (such as videos) in real time, while they are constrained by energy sources. A wireless network which supports high data-rate while consuming low-power would be very attractive for these applications. Unfortunately, existing wireless networks do not satisfy this requirement. For example, WiFi backscatter and Bluetooth networks have very low power consumption, but their data-rate is very limited (less than a Mbps). On the other hand, modern WiFi and mmWave networks support high throughput, but have a high power consumption (more than a watt).

People often have to remove their phone from an inaccessible location like a pocket to view things like notifications and directions. We explore the idea of viewing such information through the fabric of a pocket using low resolution bright LED matrix displays. A survey confirms viewing information on inaccessible phones is desirable, and establishes types of pockets in garments worn by respondents and what objects are typically put in pockets. A technical evaluation validates that LED light can shine through many common garment fabrics. Based on these results, functional hardware prototypes are constructed to demonstrate different form factors of through-fabric display devices, such as a phone, wallet, a key fob, a pen, and earbud headphone case. A simple interaction vocabulary for viewing key information on these devices is described, and the social and technical aspects of the approach are discussed.

WiFi networks employ authentication and encryption mechanisms to protect the network from being accessed by unauthorized devices. Therefore, WiFi communication should be possible only between devices inside the same network. However, we have found that all existing WiFi devices send back acknowledgments (ACK) to even fake packets received from WiFi devices outside of their network. We call this behavior Polite WiFi since WiFi devices respond to all packets even those coming from strangers!

Backscatter networks (such as RFID, and WiFi backscatter) are very attractive for IoT applications due to their ultra-low energy consumption. In fact, their required energy to operate is low enough that it can be harvested from the environment without having a battery. However, existing backscatter networks offer very limited data-rates (i.e. at most one Mbps). Hence, despite their energy benefit, their applications are very limited. This paper presents the design of mmTag, a backscatter network which can achieve Gbps data-rates. mmTag achieves this by developing a backscatter technology operating in the mmWave spectrum band. mmWave promises to enable high throughput wireless links by offering massive chunks of high-frequency spectrum. However, to use mmWave frequencies in backscatter networks, we need to address a fundamental challenge: beam alignment. mmWave devices require highly directional antennas with very narrow beams, and communication is possible only when the transmitter's beam is aligned with the receiver's beam. However, existing beam searching techniques require power hungry components, and most importantly require the node to transmit a signal which is not possible for a backscatter device. mmTag solves this problem by building a mmWave backscatter tag which performs beam alignment without using any active component. Finally, we implement mmTag and empirically demonstrate some results.

Intelligent irrigation based on measurements of soil moisture levels in every pot in a greenhouse can not only improve plant productivity and quality but also save water. However, existing soil moisture sensors are too expensive to deploy in every pot. We therefore introduce GreenTag, a low-cost RFID-based soil moisture sensing system whose accuracy is comparable to that of an expensive soil moisture sensor. Our key idea is to attach two RFID tags to a plant's container so that changes in soil moisture content are reflected in their Differential Minimum Response Threshold (DMRT) metric at the reader. We show that a low-pass filtered DMRT metric is robust to changes both in the RF environment (e.g., from human movement) and in pot locations. In a realistic setting, GreenTag achieves a 90-percentile moisture estimation errors of 5%, which is comparable to the 4% errors using expensive soil moisture sensors. Moreover, this accuracy is maintained despite changes in the RF environment and container locations. We also show the effectiveness of GreenTag in a real greenhouse.

MAC-layer frame aggregation has significantly improved the efficiency of IEEE 802.11n/ac networks by placing multiple MAC-layer data units in a large PHY-layer frame. In this paper, we focus on finding the optimal length of an Aggregated MAC Protocol Data Unit (A-MPDU) in order to maximize throughput. This problem has proved to be extremely challenging because of the chain of dependencies between consecutive A-MPDUs due to software retransmissions and because error rates can be higher in the later part of the A-MPDU.

We introduce a passive Radio Frequency IDentification (RFID) based system to detect finger gesture input for Human-Computer Interaction applications. The device is simple, inexpensive and does not require calibration to accommodate changes in the device location or the Radio Frequency (RF) environment. This is achieved by connecting the chips of two RFID tags together using a strip transmission line. The key observation is that touching different positions along the transmission line changes the impedance matching between each chip and its antenna, changing Received Signal Strength (RSS) values for each tag. When a finger slides in different directions between key positions along the transmission line, there are relative RSS patterns and trends that are robust to changes in the device location and the RF environment. We implemented and evaluated an detection algorithm and system using a commercial RFID reader and two commercial RFID chips. Results show that precision and recall are greater than 95% and 94% when detecting 10 finger gesture inputs across 48 different device locations.

WiFi backscatter communication has the potential to enable battery-free sensors which can transmit data using a WiFi network. In order for WiFi backscatter systems to be practical they should be compatible with existing WiFi networks without any hardware or software modifications. Moreover, they should work with networks that use encryption. In this paper, we present WiTAG which achieves these requirements, making the implementation and deployment of WiFi backscatter communication more practical. In contrast with existing systems which utilize the physical layer for backscatter communication, we take a different approach by leveraging features of the MAC layer to communicate. WiTAG is designed to send data by selectively interfering with subframes (MPDUs) in an aggregated frame (A-MPDU). This enables standard compliant communication using modern, open or encrypted 802.11n and 802.11ac networks without requiring hardware or software modifications to any devices. We implement WiTAG using off-the-shelf components and evaluate its performance in line-of-sight and non-line-of-sight scenarios. We show that WiTAG achieves a throughput of up to 4 Kbps without impacting other devices in the network.

Despite the ubiquity of WiFi devices, Bluetooth is widely used for communication in low-power, low data-rate devices. This is because Bluetooth consumes much less power than WiFi which results in longer battery life. The higher power consumption of WiFi devices is due to overheads from either establishing or maintaining connections with the access point. Surprisingly, Bluetooth devices require nearly three times as much energy to transmit a bit of data at the physical layer than WiFi devices.

Passive Radio Frequency IDentification (RFID) tags are commonly used to provide Radio Frequency (RF) accessible unique identifiers for physical objects due to their low-cost, lack of battery, and small size. Besides this basic function, many novel RFID-based sensing applications have been proposed in the last decade, including localization, gesture sensing, and touch sensing, among others. Nevertheless, none of these systems are in widespread use today. We hypothesize that this is because the accuracy of these systems does not meet application requirements when there are even minor changes in the RF environment or in tag geometry, i.e., changes in a tag's orientation or flexing. This paper uses both theoretical analysis and real-world experiments to test this hypothesis. Our theoretical analysis shows that even a small phase or RSS noise level can result in significant estimation errors. Our extensive real-world experiments find that both the absolute and differential values of phase and RSS readings of an RFID tag's signal can vary as much as by π radians and 10 dB, respectively, due to small changes in the tag's orientation or flexing. Because of these large variations, RFID-based application systems relying on the signal phase or RSS cannot meet application requirements, confirming our hypothesis. In addition to this strong negative result, we also present some insights into designing robust RFID systems that are suitable for use in the real world.

With the advent of the Internet of Things (IoT), billions of new connected devices will come online, placing a huge strain on today's WiFi and cellular spectrum. This problem will be further exacerbated by the fact that many of these IoT devices are low-power devices that use low-rate modulation schemes and therefore do not use the spectrum efficiently. Millimeter wave (mmWave) technology promises to revolutionize wireless networks and solve spectrum shortage problem through the usage of massive chunks of high-frequency spectrum. However, adapting this technology presents challenges. Past work has addressed challenges in using mmWave for emerging applications, such as 5G, virtual reality and data centers, which require multiple-gigabits-per-second links, while having substantial energy and computing power. In contrast, this paper focuses on designing a mmWave network for low-power, low-cost IoT devices. We address the key challenges that prevent existing mmWave technology from being used for such IoT devices. First, current mmWave radios are power hungry and expensive. Second, mmWave radios use directional antennas to search for the best beam alignment. Existing beam searching techniques are complex and require feedback from access points (AP), which makes them unsuitable for low-power, low-cost IoT devices. We present mmX, a novel mmWave network that addresses existing challenges in exploiting mmWave for IoT devices. We implemented mmX and evaluated it empirically.

We describe Tip-Tap, a wearable input technique that can be implemented without batteries using a custom RFID tag. It recognizes 2-dimensional discrete touch events by sensing the intersection between two arrays of contact points: one array along the index fingertip and the other along the thumb tip. A formative study identifies locations on the index finger that are reachable by different parts of the thumb tip, and the results determine the pattern of contacts points used for the technique. Using a reconfigurable 3x3 evaluation device, a second study shows eyes-free accuracy is 86% after a very short period, and adding bumpy or magnetic passive haptic feedback to contacts is not necessary. Finally, two battery-free prototypes using a new RFID tag design demonstrates how Tip-Tap can be implemented in a glove or tattoo form factor.

WiFi-based backscatter systems provide the potential to deliver battery-free sensors (tags) which can transmit data using a WiFi network. Existing backscatter systems have several problems which make them impractical to deploy and operate using existing WiFi networks. First, they require software or hardware modifications to WiFi access points and devices. Second, they do not work with WiFi networks that use a security protocol such as WPA. Third, they interfere with existing WiFi communication because they reflect their signal to another channel without implementing channel sensing. In this paper, we present WiTAG which addresses these problems, making the implementation and deployment of backscatter systems significantly more practical. In contrast with existing systems that build tags to communicate using the physical layer, we take a radically different approach by building tags that leverage features of the MAC layer to communicate. We design tags which can selectively interfere with subframes (MPDUs) in an aggregated frame (A-MPDU). This enables standard compliant communication using modern 802.11n and 802.11ac networks with minimal infrastructure and without requiring hardware or software modifications to any devices. The evaluation of our prototype system shows that with a client and an access point that are 8 meters apart, a tag can achieve data rates of 40 Kbps when located anywhere between the two devices.

Recently there has been a huge interest in performing research on millimeter wave (mmWave) communications. Prior work utilize this technology in enabling Gbps wireless links. In contrast, we exploit mmWave technology in designing high-density IoT networks, where there are hundreds of nodes, but each requiring only a Mbps wireless link. However, existing mmWave radios are costly and have high power consumption which makes them unsuitable for IoT sensors. We have built mmPi: a low-cost and low-power mmWave radio that operates as a daughterboard for the Raspberry Pi platform. We believe that mmPi helps advance mmWave research in the IoT domain.

Passive radio frequency identification (RFID) tags are ubiquitous today due to their low cost (a few cents), relatively long communication range ($\sim$7-11~m), ease of deployment, lack of battery, and small form factor. Hence, they are an attractive foundation for environmental sensing. Although RFID-based sensors have been studied in the research literature and are also available commercially, manufacturing them has been a technically-challenging task that is typically undertaken only by experienced researchers. In this paper, we show how even hobbyists can transform commodity RFID tags into sensors by physically altering (`hacking') them using COTS sensors, a pair of scissors, and clear adhesive tape. Importantly, this requires no change to commercial RFID readers. We also propose a new legacy-compatible tag reading protocol called Differential Minimum Response Threshold (DMRT) that is robust to the changes in an RF environment. To validate our vision, we develop RFID-based sensors for illuminance, temperature, touch, and gestures. We believe that our approach has the potential to open up the field of batteryless backscatter-based RFID sensing to the research community, making it an exciting area for future work.

There is much interest in integrating millimeter wave radios (mmWave) into wireless LANs and 5G cellular networks to benefit from their multi-GHz of available spectrum. Yet, unlike existing technologies, e.g., WiFi, mmWave radios require highly directional antennas. Since the antennas have pencil-beams, the transmitter and receiver need to align their beams before they can communicate. Existing systems scan the space to find the best alignment. Such a process has been shown to introduce up to seconds of delay, and is unsuitable for wireless networks where an access point has to quickly switch between users and accommodate mobile clients.

Backscatter requires zero transmission power, making it a compelling technology for in-body communication and localization. It can significantly reduce the battery requirements (and hence the size) of micro-implants and smart capsules, and enable them to be located on-the-move inside the body. The problem however is that the electrical properties of human tissues are very different from air and vacuum. This creates new challenges for both communication and localization. For example, signals no longer travel along straight lines, which destroys the geometric principles underlying many localization algorithms. Furthermore, the human skin backscatters the signal creating strong interference to the weak in-body backscatter transmission. These challenges make deep-tissue backscatter intrinsically different from backscatter in air or vacuum. This paper introduces ReMix, a new backscatter design that is particularly customized for deep tissue devices. It overcomes interference from the body surface, and localizes the in-body backscatter devices even though the signal travels along crooked paths. We have implemented our design and evaluated it in animal tissues and human phantoms. Our results demonstrate that ReMix delivers efficient communication at an average SNR of 15.2 dB at 1 MHz bandwidth, and has an average localization accuracy of 1.4cm in animal tissues.

Today's virtual reality (VR) headsets require a cable connection to a PC or game console. This cable significantly limits the player’s mobility and hence her/his VR experience. The high data rate requirement of this link (multiple Gbps) precludes its replacement by WiFi. Thus, in this paper, we focus on using mmWave technology to deliver multi Gbps wireless communication between VR headsets and their game consoles. The challenge, however, is that mmWave signals can be easily blocked by the player's hand or head motion. We describe novel algorithms and system design that allow such mmWave links to sustain high data rates even in the presence of a blockage, enabling a high quality untethered VR experience.

Millimeter wave (mmWave) technologies promise to revolutionize wireless networks by enabling multi-gigabit data rates. However, they suffer from high attenuation, and hence have to use highly directional antennas to focus their power on the receiver. Existing radios have to scan the space to find the best alignment between the transmitter’s and receiver’s beams, a process that takes up to a few seconds. This delay is problematic in a network setting where the base station needs to quickly switch between users and accommodate mobile clients.

Recently, there has been significant interest in performing research on millimeter wave (mmWave) communications. However, there do not exist any mmWave radio platforms with phased arrays available to the networking community. All existing mmWave platforms use horn antennas which require mechanical steering and are not suitable for non-static links or multi-user networks. We have built MiRa: a full-fledged mmWave radio with phased arrays capable of beam steering. MiRa operates as a daughterboard for the USRP software radio which enables easy manipulation of mmWave signals using standard GNU-radio software. With its reconfigurable architecture, steerable phased arrays and open SDR platform, MiRa can help advance mmWave research in the mobile and networking community.

Distributed coherent transmission is necessary for a variety of high-gain communication protocols such as distributed MIMO and creating codes over the air. Unfortunately, however, distributed coherent transmission is intrinsically difficult because different nodes are driven by independent clocks, which do not have the exact same frequency. This causes the nodes to have frequency offsets relative to each other, and hence their transmissions fail to combine coherently over the air. This paper presents AirShare, a primitive that makes distributed coherent transmission seamless. AirShare transmits a shared clock on the air and feeds it to the wireless nodes as a reference clock, hence eliminating the root cause for incoherent transmissions. The paper addresses the challenges in designing and delivering such a shared clock. It also implements AirShare in a network of USRP software radios, and demonstrates that it achieves tight phase coherence. Further, to illustrate AirShare's versatility, the paper uses it to deliver a coherent-radio abstraction on top of which it demonstrates two cooperative protocols: distributed MIMO, and distributed rate adaptation.

Electronic toll collection transponders, e.g., E-ZPass, are a widely-used wireless technology. About 70% to 89% of the cars in US have these devices, and some states plan to make them mandatory. As wireless devices however, they lack a basic function: a MAC protocol that prevents collisions. Hence, today, they can be queried only with directional antennas in isolated spots. However, if one could interact with e-toll transponders anywhere in the city despite collisions, it would enable many smart applications. For example, the city can query the transponders to estimate the vehicle flow at every intersection. It can also localize the cars using their wireless signals, and detect those that run a red-light. The same infrastructure can also deliver smart street-parking, where a user parks anywhere on the street, the city localizes his car, and automatically charges his account. This paper presents Caraoke, a networked system for delivering smart services using e-toll transponders. Our design operates with existing unmodified transponders, allowing for applications that communicate with, localize, and count transponders, despite wireless collisions. To do so, Caraoke exploits the structure of the transponders' signal and its properties in the frequency domain. We built Caraoke reader into a small PCB that harvests solar energy and can be easily deployed on street lamps. We also evaluated Caraoke on four streets on our campus and demonstrated its capabilities.

The emergence of data-intensive problems in areas like computational biology, astronomy, medical imaging, etc. has emphasized the need for fast and efficient very large Fourier Transforms. Recent work has shown that we can compute million-point transforms efficiently provided the data is sparse in the frequency domain. Processing input samples at rates approaching 1 GHz would allow real-time processing in several such applications. In this paper, we present a high-throughput FPGA implementation that performs a million-point sparse Fourier Transform on frequency-sparse input data, generating the largest 500 frequency component locations and values every 1.16 milliseconds. This design can process streamed input data at 0.86 Giga samples per second, and does not make any assumptions of the distribution of the frequency components beyond sparsity.

We present BigBand, a technology that can capture GHz of spectrum in realtime without sampling the signal at GS/s - i.e., without high speed ADCs. Further, it is simple and can be implemented on commodity low-power radios. Our approach builds on recent advances in the area of sparse Fourier transforms, which show that it is possible to reconstruct a sparse signal without sampling it at the Nyquist rate. To demonstrate our design, we implement it using 3 software radios, each sampling the spectrum at 50 MS/s, producing a device that captures 0.9 GHz - i.e., 6× larger digital bandwidth than the three software radios combined. Finally, an extension of BigBand can perform GHz spectrum sensing even in scenarios where the spectrum is not sparse.

Implementing distributed wireless protocols at the physical layer today is challenging because different nodes have different clocks, each of which has slightly different frequencies. This causes the nodes to have frequency offset relative to each other. As a result, transmitted signals from these nodes do not combine in a predictable manner over time. Past work tackles this challenge and builds distributed PHY layer systems by attempting to address the effects of the frequency offset and compensating for it in the transmitted signals. In this extended abstract, we address this challenge by addressing the root cause - the different clocks with different frequencies on the different nodes. We present AirClock, a new wireless coordination primitive that enables multiple nodes to act as if they are driven by a single clock that they receive wirelessly over the air. AirClock presents a synchronized abstraction to the physical layer, and hence enables direct implementation of diverse kinds of distributed PHY protocols. We illustrate AirClock's versatility by using it to build two different systems: (1) distributed MIMO, and (2) distributed rate adaptation for wireless sensors, and show that they can provide significant performance benefits over today's systems.

This paper evaluates the impact of circuit impairments on the energy cost and performance limitations of analog-to-information converters (AIC). In applications where signal frequencies are high, but information bandwidths are low, AICs have been proposed as a potential solution to overcome the resolution and performance limitations of sampling jitter in high-speed analog-to-digital converters (ADC). Although the AIC architecture facilitates slower ADCs, the signal encoding, typically realized with a mixer-like circuit, still occurs at the Nyquist frequency of the input to avoid aliasing. We show that the jitter of this mixing stage limits the achievable AIC resolution. In this work, the end-to-end system evaluation framework is designed to analyze these limitations as well as the relative energy-efficiency of AICs versus ADCs across the resolution, receiver gain and signal sparsity. The evaluation shows that AICs improve the resolution by 1 bit when the signal of interest is very sparse, and enable 2× in energy savings when no pre-amplification is required.

A differential, all digital, fifth-order derivative Gaussian pulse generator is designed and fabricated in a standard 0.13μm CMOS technology for Ultra wideband (UWB) system. Usage of the differential pulse generator eliminates the need of any extra circuit for feeding differential on-chip antenna. This elimination has an enormous effect on decreasing the power consumption of transceiver. The average power consumption of proposed pulse generator is 1.63mW and 26.4μW at pulse repeating frequency (PRF) of 300MHz and 4MHz respectively with 1.2V power supply voltage. The measured output pulse amplitude is 540mV peak to peak. The proposed pulse generator fully complies with FCC regulation.