We present MiHazeFree3D , a system that leverages millimeter-wave (mmWave) radar signals to predict 3D bounding boxes of vehicles and pedestrians in real-world traffic scenarios. While current 3D object detection methods rely primarily on cameras and LiDARs, their performance degrades significantly in rain, fog, or poor lighting conditions. Our system exploits mmWave radar’s ability to operate reliably in these challenging conditions, offering a complement to existing sensors without increasing computational costs. The key challenge in using mmWave for 3D detection lies in handling motion-induced errors and the specular reflection of mmWave signals. To address these issues, we developed a deep learning architecture with multiple feature fusion layers and trained it on diverse real-world scenarios. We evaluated MiHazeFree3D using data collected from mmWave radars mounted on the dashboard of an ego-vehicle driving through urban environments. Our results show that MiHazeFree3D detects and bounds both vehicles and pedestrians in tested conditions, including fog and low-light scenarios, highlighting the potential of mmWave radar for 3D object detection in autonomous driving systems.

We propose a millimeter-wave (mmWave) wireless signal-based sleep monitoring system aimed at providing information about a person's sleep by detecting sleep events, such as bed entry and exit times, as well as the duration of bed stay. It overcomes the limitations of existing vision-based systems by operating in low-light conditions without invading privacy. It uses spatial-temporal information and signal processing techniques to determine the duration, and our preliminary results indicate that our system can accurately detect bed events.

Gait speed is an important indicator of human health. Monitoring patients' gait speed can help doctors assess the recovery process, but traditional clinician observation fails to track in home scenarios. Compared to vision-based and wearable approaches, radio frequency signals offer an easily deployable and light free solution protecting user privacy in home scenarios. Therefore, we proposed a millimeter-wave (mmWave) system to accurately extract walking periods from collected trials and calculate gait speeds. To evaluate the robustness and reliability of our system and determine the optimal mounting position, we collected data from 5 volunteers with normal walking speeds and imitated various abnormal gait patterns and walking speeds. The results show that the mmWave device mounted near the ground outperforms across all volunteers than the one mounted near the ceiling, achieving an average estimation error of 0.02 m/s in abnormal gait evaluations.

We propose AutoSense, a millimeter-wave (mmWave) wireless signal-based system for predicting 3D bounding boxes of vehicles. While cameras and LiDAR can be adversely affected by challenging weather conditions such as heavy rain, fog, or snow, mmWave signals are less susceptible to these environmental factors, making them more resilient. As a result, AutoSense can complement other sensors for accurate 3D bounding box predictions in all weather conditions.

Millimeter-wave is the core technology to enable multi-Gbps throughput and ultra-low latency connectivity. But the devices need to operate at very high frequency and ultra-wide bandwidth: They consume more energy, dissipate more power, and subsequently heat up faster. Device overheating is a common concern of many users, and millimeter-wave would exacerbate the problem. In this work, we first thermally characterize millimeter-wave devices. Our measurements reveal that after only 10 s of data transfer at 1.9 Gbps bit-rate, the millimeter-wave antenna temperature reaches 68 °C; it reduces the link throughput by 21%, increases the standard deviation of throughput by 6×, and takes 130 s to dissipate the heat completely. Besides degrading the user experience, exposure to high device temperature also creates discomfort. Based on the measurement insights, we propose Aquilo, a temperature-aware, multi-antenna network scheduler. It maintains relatively high throughput performance but cools down the devices substantially. Our testbed experiments under both static and mobile conditions demonstrate that Aquilo achieves a median peak temperature only 0.5 °C to 2 °C above the optimal while sacrificing less than 10% of throughput.

In this work, we propose MiSleep , a deep learning augmented millimeter-wave (mmWave) wireless system to monitor human sleep posture by predicting the 3D location of the body joints of a person during sleep. Unlike existing vision- or wearable-based sleep monitoring systems, MiSleep is not privacy-invasive and does not require users to wear anything on their body. MiSleep leverages knowledge of human anatomical features and deep learning models to solve challenges in existing mmWave devices with low-resolution and aliased imaging and specularity in signals. MiSleep builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since a practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction, MiSleep designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn and predict the posture only during the rest states. We evaluate MiSleep with real data collected from Commercial-Off-The-Shelf mmWave devices for eight volunteers of diverse ages, genders, and heights performing different sleep postures . We observe that MiSleep identifies the toss-turn events start time and duration within 1.25 s and 1.7 s of the ground truth, respectively, and predicts the 3D location of body joints with a median error of 1.3 cm only and can perform even under the blankets, with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.

Object detection plays a pivotal role in various fields, for example, a smart traffic system relies on the detected results for decision-making. However, existing studies predominately utilize optical camera and LiDAR, which exhibit limitations in adverse outdoor environments, such as foggy weather. To address these challenges, millimeter-waves (mmWaves) attract researchers’ attention to detect objects in severe conditions since they can work effectively in low-visibility conditions and overcome small obstacles. Yet, previous mmWave-based works have shown limited performance, such as no shape information for objects. Therefore, we design and implement a two-stage system, mmBox , to accurately predict bounding boxes with depth for vehicles and pedestrians, which first generates heatmaps in different dimensions and then leverages a deep learning model to extract features for predictions. To evaluate the performance of mmBox , we collected real-world mmWave reflections from urban traffic intersections and dense-fog environments. The extensive evaluation metrics show remarkable accuracy and the low latency of our model.

We present CoSense , a system that enables coexistence of networking and sensing on next-generation millimeter-wave (mmWave) picocells for traffic monitoring and pedestrian safety at intersections in all weather conditions. Although existing wireless signal-based object detection systems are available, they suffer from limited resolution and their outputs may not provide sufficient discriminatory information in complex scenes, such as traffic intersections. CoSense proposes using 5G picocells, which operate at mmWave frequency bands and provide higher data rates and higher sensing resolution than traditional wireless technology. However, it is difficult to run sensing applications and data transfer simultaneously on mmWave devices due to potential interference, and using special-purpose sensing hardware can prohibit deployment of sensing applications to a large number of existing and future inexpensive mmWave devices. Additionally, mmWave devices are vulnerable to weak reflectivity and specularity challenges, which may result in loss of information about objects and pedestrians. To overcome these challenges, CoSense design customized deep learning models that not only can recover missing information about the target scene but also enable coexistence of networking and sensing. We evaluate CoSense on diverse data samples captured at traffic intersections and demonstrate that it can detect and locate pedestrians and vehicles, both qualitatively and quantitatively, without significantly affecting the networking throughput.

Millimeter-Wave (mmWave) networks rely on carefully placed small base stations called “picocells” for optimal network performance. However, the process of conducting site surveys to identify suitable picocell locations is both expensive and time-consuming. The current low-cost approaches for indoor surveying are often unsuitable for outdoor environments due to the presence of various environmental factors. To address this issue, we present Theia, a drone-based system that predicts outdoor mmWave Signal Reflection Profiles (SRPs) and facilitates picocell placement for optimal network coverage. The drone platform integrates optical systems and a mmWave transceiver to collect depth images and mmWave SRPs of the environment. These datasets are fed into a machine learning model that maps the depth data to SRPs, allowing SRPs to be predicted at previously unseen parts of the environment. Theia then leverages these predictions to identify optimal picocell locations that maximize network coverage and minimize link outages. We evaluate Theia in three large-scale outdoor environments and demonstrate that the proposed design can generalize the deployment method with a little refinement of the model.

Millimeter-wave technology's unique advantages, in-cluding low-light functionality, cost-effectiveness, and penetration of small objects, make it perfect for outdoor object detection. But traditional methods like likelihood clustering have faced challenges in determining target objects' extent and distance. This work presents mmBox, a two-stage system tailored for precise bounding boxes of vehicles and pedestrians outdoors. We assess mmBox's effectiveness through extensive testing in outdoor street scenes using multiple metrics.

We propose Argosleep, a millimeter-wave (mmWave) wireless sensors based sleep posture monitoring system that predicts the 3D location of body joints of a person during sleep. Argosleep leverages deep learning models and knowledge of human anatomical features to solve challenges with low-resolution, specularity, and aliasing in existing mmWave devices. Argosleep builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction, Argosleep designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn, and predict the posture only during the rest states. We evaluate Argosleep with real data collected from COTS mmWave devices for 8 volunteers of diverse ages, gender, and height performing different sleep postures. We observe that Argosleep identifies the toss-turn events accurately and predicts 3D location of body joints with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.

Millimeter wave (mmWave) 3D imaging has been applied for point cloud data (PCD) generation due to its valuable attributes, such as working under low light, compact size, and low-cost. However, past works have focused on transforming millimeter wave reflection signals into other data structures, like polar images and coarse PCDs before applying neural network to produce dense PCDs. Those algorithms will filter some useful features. To address this issue, our paper proposes an innovative prototype: mmWaveNet, a deep learning model that directly uses reflection signals as input and generates high-quality PCDs. We have experimentally evaluated mmWaveNet in a large indoor environment.

We propose ReSense, a residual network for spectrum sensing high-frequency signals with low-frequency samplers. ReSense first transforms the aliased signal from low-frequency samplers into image-like inputs and uses multiple convolution layers and skip connections to predict the signal’s frequency components to enable spectrum sensing. We evaluate ReSense on the signal dataset with single and double frequencies and achieve 95% and 40% accuracy in detecting modulation type on respective datasets, indicating accurate spectrum sensing.

We propose D3PicoNet, which allows network deployers to quickly complete realistic indoor site surveys at D-band (mmWave) frequency. D3PicoNet models the mmWave reflection profile of a given environment, considering the primary reflecting objects. It then utilizes this model to identify places that optimize the efficiency of the reflectors. D3PicoNet understands an environment and deploys D-band picocells at such locations that picocells provide coverage with Non-Line-of-Sight (NLoS) paths when Line-of-Sight (LoS) is obstructed. The core module of D3PicoNet is a deep learning network that learns the relationship between the visual depth images to the mmWave signal reflection profiles and can accurately predict signal reflection profiles at any unobserved location, which allows D3PicoNet to find the best deployment locations maximizing the coverage and data rate with a minimum number of picocells in an environment. We implement and evaluate D3PicoNet on two buildings with multiple indoor environments. D3PicoNet can adapt to new environments, allowing it to be used in other indoor environments with minimal adjustments.

We propose mmSleep, a millimeter-wave (mmWave) wireless signal based sleep posture monitoring system that can assist in tracking 3D location of body joints of a person during sleep. mmSleep overcomes the limitations of existing vision-based sleep monitoring and can work under low-light without being privacy-invasive. mmSleep uses a customized Convolutional Neural Network to learn diverse sleep postures, and our preliminary results show that mmSleep can consistently predict 3D joint locations with high accuracy.

Millimeter-wave (mmWave) imaging has been difficult to implement on handheld devices since imaging algorithms rely on millimeter-scale device self-tracking, which existing systems cannot achieve reliably. We propose CompenSAR, a handheld system which integrates a mmWave transceiver and a tracking camera, and overcomes the self-tracking limitations to enable handheld mmWave imaging.

At-home exercise monitoring is vital to applications like rehabilitative care and physical therapy. In this work, we use millimeter-wave signal reflections to assess the exercise, where we classify the exercise type by designing a supervised deep learning model, and estimate the number of repetitions by leveraging phase information embedded in the reflections.

Soluble Sugar Content (SSC) of a fruit is indicative of its ripeness and is used in the fruit industry for quality control in the production chain. We present the design and implementation of SSCense, a low-cost, non-destructive system to estimate a fruit's SSC using the millimeter-wave wireless technology in 5G-and-beyond devices.

The proliferation of smart, ubiquitous devices has inspired many researchers to develop at-home personal documentation systems. One application of such systems is at-home exercise monitoring, which is important for remote healthcare and fitness regimens. This work explores a millimeter-wave (mmWave) wireless sensing based at-home exercise monitoring using commodity devices. We leverage the mmWave signals reflected off a person exercising and design a deep-learning network that uses a combination of CNN and LSTM to classify the activities. We evaluate the performance of our classifier extensively, using several input signal representations.

We propose FlexVAA, a system for identifying roadside infrastructure using flexible, passive wireless retroreflectors. FlexVAA can be easily attached to any surface, allowing roadside infrastructure to be upgraded for autonomous systems without impairing existing operations. Preliminary results show that attaching FlexVAA to a surface reflects more power than without FlexVAA attached. In the future, we plan to arrange multiple FlexVAA elements in order to embed data in the passively reflected signal, allowing identification for multiple unique tags.

We spend one-third of our lives sleeping, and sleep quality plays an important role in our overall health. Sleep posture monitoring can help medical professionals prevent negative health outcomes associated with certain sleep postures. In this work, we propose using millimeter-wave wireless signals to classify the sleep posture using a supervised deep learning model and preliminarily evaluate the performance for 7 volunteers and 5 broad classes of postures.

During convergence, after a link state change in traditional networks with a distributed control plane, packets may get caught in transient forwarding loops. Such loops can be avoided by imposing a certain order among the routers in updating their forwarding information bases (FIBs), but it requires some form of coordination among routers. As an alternative, a progressive link metric increment method has been proposed for loop-free forwarding without ordered FIB updates, but it takes longer to converge to the target state. In this paper, we show that the order of updates rarely matters for loop-free convergence when the failure inference-based fast reroute (FIFR) scheme with interface-specific forwarding is employed for dealing with link failures. The key insight is to have each router install the traditional interface-independent forwarding entries as soon as they are recomputed during convergence and install the recomputed interface-specific backwarding entries post-convergence. Our evaluation of 280 real and random topologies confirms that the order of updates does not matter with the proposed approach for 17336 out of 17339 links in those topologies. To handle such rare cases where the order matters, it can be coupled with progressive link metric increments to ensure loop-freedom with unordered FIB updates. Thus, the proposed approach, referred to as FIFR++, makes it possible to achieve disruption-free fast convergence and fast reroute without requiring any modification to the IP datagram and without needing any coordination between routers.

Respiratory diseases, like Asthma, COPD, have been a significant public health challenge over decades. Portable spirometers are effective in continuous monitoring of respiratory syndromes out-of-clinic. However, existing systems are either costly or provide limited information and require extra hardware. In this paper, we present mmFlow, a low-barrier means to perform at-home spirometry tests using 5G smart devices. mmFlow works like regular spirometers, where a user forcibly exhales onto a device; but instead of relying on special-purpose hardware, mmFlow leverages built-in millimeter-wave technology in general-purpose, ubiquitous mobile devices. mmFlow analyzes the tiny vibrations created by the airflow on the device surface and combines wireless signal processing with deep learning to enable a software-only spirometry solution. From empirical evaluations, we find that, when device distance is fixed, mmFlow can predict the spirometry indicators with performance comparable to inclinic spirometers with <5% prediction errors. Besides, mmFlow generalizes well under different environments and human conditions, making it promising for out-of-clinic daily monitoring.

We present MilliCam, a system that captures the shape of small metallic objects, such as a gun, through obstructions, like clothing. MilliCam builds on the millimeter-wave (mmWave) imaging systems, which are widely used today in airport security checkpoints. Existing systems achieve high-resolution using a Synthetic Aperture Radar (SAR) principle, but require bulky motion controllers to position the mmWave device precisely. In contrast, MilliCam emulates the SAR principle by pure hand-swiping. However, alias-free, high-resolution imaging requires a linear, error-free hand-swiping motion. Furthermore, image focusing on an object of interest requires steering perfectly-shaped beam over the target-scene; but it is unavailable in off-the-shelf devices. We design a set of algorithms to enable high-quality handheld imaging: compensating for the errors in hand-swipe motion; and focusing the target-scene digitally without beam-steer. We have prototyped MilliCam on a 60 GHz testbed. Our experiments demonstrate that MilliCam can effectively combat motion errors and focus on the object in target-scene.

Millimeter-wave is the core technology to enable multi-Gbps throughput and ultra-low latency wireless connectivity. But the devices need to operate at very high frequency and ultra-wide bandwidth; so, they consume more energy, dissipate more power, and subsequently heat up faster. Device overheating is a common concern of many users, and millimeter-wave (mmWave) would exacerbate the problem. In this work, we first study the thermal characterization of mmWave devices. Our measurements reveal that after only 10 s. of data transfer at 1.9 Gbps bit-rate, the mmWave antenna temperature reaches 68°C; it reduces the link throughput by 21%, increases the standard deviation of throughput by 6×, and takes 130 s. to dissipate the heat completely. Besides degrading the user experience, exposure to high device temperature also creates discomfort. Based on the measurement insights, we propose Aquilo, a temperature-aware multi-antenna scheduler; it maintains relatively high throughput performance, but cools down the devices substantially. Our testbed experiments in both static and mobile conditions show that Aquilo reaches a median peak temperature just 0.5 to 2°C above the optimal by sacrificing less than 10% of throughput.

Millimeter-wave devices operate at very high frequency and ultra-wide bandwidth. They consume more energy, dissipate more power, and heat up faster. So, millimeter-wave (mmWave) would exacerbate the device overheating problem in the future. In this work, we first perform a thermal characterization of mmWave devices: it reveals that after only 10 s. of data transfer at 1.9 Gbps, the antenna temperature reaches 68°C; it reduces the link throughput by 21%, increases the standard deviation by 6×, and takes 130 s. to dissipate the heat completely. We then propose Aquilo to bring temperature-awareness in mmWave networks; Aquilo maintains relatively high throughput performance and cools down the devices substantially. Our testbed experiments in static conditions show that Aquilo reaches a median peak temperature just 1°C above the optimal with less than 10% throughput sacrifice only.

Abstract Audio-based sensing enables fine-grained human activity detection, such as sensing hand gestures and contact-free estimation of the breathing rate. A passive adversary, equipped with microphones, can leverage the ongoing sensing to infer private information about individuals. Further, with multiple microphones, a beamforming-capable adversary can defeat the previously-proposed privacy protection obfuscation techniques. Such an adversary can isolate the obfuscation signal and cancel it, even when situated behind a wall. AudioSentry is the first to address the privacy problem in audio sensing by protecting the users against a multi-microphone adversary. It utilizes the commodity and audio-capable devices, already available in the user’s environment, to form a distributed obfuscator array. AudioSentry packs a novel technique to carefully generate obfuscation beams in different directions, preventing the multi-microphone adversary from canceling the obfuscation signal. AudioSentry follows by a dynamic channel estimation scheme to preserve authorized sensing under obfuscation. AudioSentry offers the advantages of being practical to deploy and effective against an adversary with a large number of microphones. Our extensive evaluations with commodity devices show that protects the user’s privacy against a 16-microphone adversary with only four commodity obfuscators, regardless of the adversary’s position. AudioSentry provides its privacy-preserving features with little overhead on the authorized sensor.

Millimeter-wave (mmWave) technology is emerging as the most promising solution to meet the multi-fold demand increase for mobile data. Very short wavelength, high directionality, together with sensitivity to rampant blockages and mobility, however, render state-of-the-art mmWave technologies unsuitable for ubiquitous wireless coverage. In this work, we design and implement UbiG - a mmWave wireless access network - that can deliver ubiquitous gigabits per second wireless access consistently to the commercial-off-the-shelf IEEE 802.11ad devices. UbiG has two key design components: (1) a fast probing based beam alignment algorithm that can identify the best beam consistently with guaranteed latency in a mmWave link, and the algorithm scales well even with a very large number of beams; and (2) an infrastructure-side predictive ranking based fast access point switching algorithm to ensure seamless gigabits per second connectivity under mobility and blockage in a dense mmWave deployment. Our IEEE 802.11ad testbed experiments show that UbiG performs close to an "Oracle" solution that instantaneously knows the best beam and access point for gigabits per second data transmission to users.

Despite years of innovative research and development, gigabit-speed 60 GHz wireless networks are still not mainstream. The main concern for network operators and vendors is the unfavorable propagation characteristics due to short wavelength and high directionality, which renders the 60 GHz links highly vulnerable to blockage and mobility. However, the advent of multi-band chipsets opens the possibility of leveraging the more robust WiFi technology to assist 60 GHz in order to provide seamless, Gbps connectivity. In this paper, we design and implement MUST, an IEEE 802.11-compliant system that provides seamless, high-speed connectivity over multi-band 60 GHz and WiFi devices. MUST has two key design components: (1) a WiFi-assisted 60 GHz link adaptation algorithm, which can instantaneously predict the best beam and PHY rate setting, with zero probing overhead; and (2) a proactive blockage detection and switching algorithm which can re-direct ongoing user traffic to the robust interface within sub-10 ms latency. Our experiments with off-the-shelf 802.11 hardware show that MUST can achieve 25-60% throughput gain over state-of-the-art solutions, while bringing almost 2 orders of magnitude cross-band switching latency improvement.

Despite years of innovative research and development, multi-Gbps 60 GHz wireless networks are still not mainstream. The unfavorable propagation characteristics due to short wavelength and high directionality, makes the 60 GHz links highly vulnerable to blockage and mobility. However, the advent of multi-band chipsets opens the possibility of leveraging the more robust WiFi technology to assist 60 GHz in order to provide seamless, Gbps connectivity. In this demonstration, we will present MUST, an 802.11-compliant real-time system that provides seamless, high-speed connectivity over multi-band 60 GHz and WiFi devices. MUST has two key design components: (1) a WiFi-assisted 60 GHz link adaptation algorithm, which can instantaneously predict the best beam and PHY rate setting, with zero probing overhead at 60 GHz; and (2) a proactive blockage detection and switching algorithm which can re-direct ongoing user traffic to the robust interface within sub-10 ms latency. We have implemented MUST on off-the-shelf devices where our experiments show high throughput gain and almost 2 orders of magnitude cross-band switching latency improvement over state-of-the-art solutions.

Multi-User MIMO, the hallmark of IEEE 802.11ac and the upcoming 802.11ax, promises significant throughput gains by supporting multiple concurrent data streams to a group of users. However, identifying the best-throughput MU-MIMO groups in commodity 802.11ac networks poses three major challenges: a) Commodity 802.11ac users do not provide full CSI feedback, which has been widely used for MU-MIMO grouping. b) Heterogeneous channel bandwidth users limit grouping opportunities. c) Limited-resource on APs cannot support computationally and memory expensive operations, required by existing algorithms. Hence, state-of-the-art designs are either not portable in 802.11ac APs, or perform poorly, as shown by our testbed experiments. In this paper, we design and implement MUSE, a lightweight user grouping algorithm, which addresses the above challenges. Our experiments with commodity 802.11ac testbeds show MUSE can achieve high throughput gains over existing designs.

We explore the use of TV White Space (TVWS) wireless networks for providing robust and long range connectivity to vehicles. A key distinctive requirement of TVWS networks is the power asymmetry - the static APs are allowed to transmit at up to 4 W, while the mobile clients in vehicles are limited to only 100 mW. Our measurements reveal that the power asymmetry not only causes severe uplink blackouts but also poses significant coexistence problems, as high-power fixed nodes can easily starve the low-power mobile ones due to carrier sensing loss. To tackle these unique challenges, we propose a cross-layer design of a Direct-Sequence Spread Spectrum (DSSS) based system. We employ an adaptive DSSS mechanism that strategically configures the spreading code, so as to boost uplink coverage while maximizing throughput. We further design a traffic-aware code assignment algorithm for uplink packets to balance the requirement of throughput-intensive and latency-sensitive flows. We have implemented the design on a TVWS software-radio platform on a moving vehicle in an urban environment, and demonstrated that link asymmetry can be completely removed to support realistic application traffic, while the carrier sense loss rate at fixed nodes can be reduced by around 85%.

Leveraging the redundancy and parallelism from multiple RF chains, MIMO technology can easily scale wireless link capacity. However, the high power consumption and circuit-area cost prevents MIMO from being adopted by energy-constrained wireless devices. In this paper, we propose Halma, that can boost link capacity using multiple antennas but a single RF chain, thereby, consuming the same power as SISO. While modulating its normal data symbols, a Halma transmitter hops between multiple passive antennas on a per-symbol basis. The antenna hopping pattern implicitly carriers extra data, which the receiver can decode by extracting the index of the active antenna using its channel pattern as a signature.

Line-of-Sight blockage by human body is a severe challenge to enable robust 60 GHz directional links. Beamsteering is one feasible solution to overcome this problem by electronically steering phased-array beam towards Non-Line-of-Sight. However, effectiveness of beamsteering depends on the link deployment and a lack of assessment of steering effectiveness may render the link completely blacked-out during human blockage. In this poster, we propose a new technique called BeamScope, that predicts best possible location for a randomly deployed link in an indoor environment without the need of any explicit war-driving. BeamScope first characterizes the environment exploiting measurement from the randomly deployed reference location and then predicts the performance in unobserved locations to suggest a possible re-deployment. The environment characterization is captured through a novel metric and prediction is achieved via how this metric is shared between the reference location and unobserved locations. Our preliminary results show promising accuracy of identifying the best possible alternate location for 60 GHz link to achieve a robust connection during human blockage.

60 GHz technology holds tremendous potential to upgrade wireless link throughput to Gbps level. To overcome inherent vulnerability to attenuation, 60 GHz radios communicate by forming highly-directional electronically-steerable beams. Standards like IEEE 802.11ad have tailored MAC/PHY protocols to such flexible-beam 60 GHz networks. However, lack of a reconfigurable platform has thwarted a realistic proof-of-concept evaluation. In this paper, we conduct an in-depth measurement of indoor 60 GHz networks using a first-of-its-kind software-radio platform. Our measurement focuses on the link-level behavior with three major perspectives: (i) coverage and bit-rate of a single link, and implications for 60 GHz MIMO; (ii) impact of beam-steering on network performance, particularly under human blockage and device mobility; (iii) spatial reuse between flexible beams. Our study dispels some common myths, and reveals key challenges in maintaining robust flexible-beam connection. We propose new principles that can tackle such challenges based on unique properties of 60 GHz channel and cognitive capability of 60 GHz links.

High-quality, speaker-location-aware audio capturing has traditionally been realized using dedicated microphone arrays. But high cost and lack of portability prevents such systems from being widely adopted. Today's smartphones are relatively more convenient for audio recording, but the audio quality is much lower in noisy environment and speaker location cannot be readily obtained. In this paper, we design and implement Dia, which leverages smartphone cooperation to overcome the above limitations. Dia supports spontaneous setup, by allowing a group of users to rapidly assemble an array of smartphones to emulate a dedicated microphone array. It employs a novel framework to accurately synchronize the audio I/O clocks of the smartphones. The synchronized smartphone array further enables autodirective audio capturing, i.e., tracking the speaker's location, and beamforming the audio capturing towards the speaker to improve audio quality. We implement Dia on a testbed consisting of 8 Android phones. Our experiments demonstrate that Dia can synchronize the microphones of different smartphones with sample-level accuracy. It achieves high localization accuracy, and similar beamforming performance compared with a microphone array with perfect synchronization.