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.

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.

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.