Millimeter-wave (mmWave) radar has become instrumental in diverse consumer applications. Yet current radar architectures face major limitations. While full-MIMO structures are feature-rich, their cost and complexity rise rapidly with more antennas. Phased-MIMO radars promise enhanced scalability by combining large phased arrays with a small number of RF chains. Nevertheless, the phased-MIMO research thus far primarily relies on simulation or theoretical analysis. In this paper, we introduce HybRadar, a novel programmable phased-MIMO radar platform to address this experimental gap. HybRadar repurposes the phased arrays on a low-cost 802.11ad radio to create a scalable low-cost array of phased subarrays. It further incorporates transmit/receive front-end, control channel, and hardware synchronization mechanisms to enable a modular phased-MIMO system. By extending recent MIMO array synthesis models, we optimize the placement of phased subarrays to maximize the spatial resolution. Our prototype validation and case studies confirm the capability and versatility of HybRadar.

Radar sensors have recently been explored in the industrial and consumer Internet of Things (IoT). However, such applications often require self-sustainable or untethered operations, which are at odds with the high power consumption of radar. This paper proposes NeuroRadar, a neuromorphic radar sensor, to achieve low-power wireless sensing. NeuroRadar jointly optimizes the analog hardware and the computation model, in order to mimic the highly efficient biological sensing and neural processing system. NeuroRadar features a highly simplified radar front end, which eliminates the power-hungry components in conventional radars. It directly "encodes" ambient motion into spiking signals, which can be processed using spiking neural networks running on energy-efficient neuromorphic computing platforms. We have prototyped NeuroRadar and evaluated its performance in two use cases: gesture sensing and localization. Our experiments demonstrate that NeuroRadar can achieve high sensing accuracy, at orders of magnitude lower power consumption compared with traditional radar.

A large number of traffic collisions occur as a result of non-line-of-sight (NLoS) obstructions. Recent work has explored NLoS automotive radar sensing systems to detect objects in occluded regions. However, current NLoS radars require substantial ambient reflectors, whose size needs to scale with the desired angular resolution and coverage, impeding their deployment in real-world scenarios. In this paper, we propose Metasight, which leverages carefully designed passive millimeter-wave metasurface reflectors and a novel angular encoding scheme to dramatically reduce the reflector size. The Metasight metasurfaces are fully passive, low cost, and can be fabricated by simply using a 3D printer and copper tape. By processing the reflected signals with a robust angle decoding algorithm on the radar, Metasight achieves high NLoS sensing resolution and wide coverage, with an asymptotically higher space-efficiency than conventional natural or artificial reflectors.

A large number of traffic collisions occur as a result of obstructed sight lines, such that even an advanced driver assistance system would be unable to prevent the crash. Recent work has proposed the use of around-the-corner radar systems to detect vehicles, pedestrians, and other road users in these occluded regions. Through comprehensive measurement, we show that these existing techniques cannot sense occluded moving objects in many important real-world scenarios. To solve this problem of limited coverage, we leverage multiple, curved reflectors to provide comprehensive coverage over the most important locations near an intersection. In scenarios where curved reflectors are insufficient, we evaluate the relative benefits of using additional flat planar surfaces. Using these techniques, we more than double the probability of detecting a vehicle near the intersection in three real urban locations, and enable NLoS radar sensing using an entirely new class of reflectors.

Millimeter-wave (mmWave) technologies represent a cornerstone for emerging wireless network infrastructure, and for RF sensing systems in security, health, and automotive domains. Through a MIMO array of phased arrays with hundreds of antenna elements, mmWave can boost wireless bit-rates to 100+ Gbps, and potentially achieve near-vision sensing resolution. However, the lack of an experimental platform has been impeding research in this field. We propose to fill the gap with M3 (M-Cube), the first mmWave massive MIMO software radio [1]. M3 features a fully reconfigurable array of phased arrays, with up to 8 RF chains and 256 antenna elements. Despite the orders of magnitude larger antenna arrays, its cost is orders of magnitude lower, even when compared with state-of-the-art single RF chain mmWave software radios. In this demo, we will show M3's hardware modules, and demonstrate its usage in mmWave MIMO communication and sensing.

mmWave 5G networks promise to enable a new generation of networked applications requiring a combination of high throughput and ultra-low latency. However, in practice, mmWave performance scales poorly for large numbers of users due to the significant overhead required to manage the highly-directional beams. We find that we can substantially reduce or eliminate this overhead by using out-of-band infrared measurements of the surrounding environment generated by a LiDAR sensor. To accomplish this, we develop a ray-tracing system that is robust to noise and other artifacts from the infrared sensor, create a method to estimate the reflection strength from sensor data, and finally apply this information to the multiuser beam selection process. We demonstrate that this approach reduces beam-selection overhead by over 95% in indoor multi-user scenarios, reducing network latency by over 80% and increasing throughput by over 2× in mobile scenarios.

Millimeter-wave (mmWave) technologies represent a cornerstone for emerging wireless network infrastructure, and for RF sensing systems in security, health, and automotive domains. Through a MIMO array of phased arrays with hundreds of antenna elements, mmWave can boost wireless bit-rates to 100+ Gbps, and potentially achieve near-vision sensing resolution. However, the lack of an experimental platform has been impeding research in this field. This paper fills the gap with M3 (M-Cube), the first mmWave massive MIMO software radio. M3 features a fully reconfigurable array of phased arrays, with up to 8 RF chains and 288 antenna elements. Despite the orders of magnitude larger antenna arrays, its cost is orders of magnitude lower, even when compared with state-of-the-art single RF chain mmWave software radios. The key design principle behind M3 is to hijack a low-cost commodity 802.11ad radio, separate the control path and data path inside, regenerate the phased array control signals, and recreate the data signals using a programmable baseband. Extensive experiments have demonstrated the effectiveness of the M3 design, and its usefulness for research in mmWave massive MIMO communication and sensing.

Millimeter-wave (mmWave) technologies represent a cornerstone for emerging wireless network infrastructure, and for RF sensing systems in security, health, and automotive domains. Through a MIMO array of phased arrays with hundreds of antenna elements, mmWave can boost wireless bit-rates to 100+ Gbps, and potentially achieve near-vision sensing resolution. However, the lack of an experimental platform has been impeding research in this field. We propose to fill the gap with M3 (M-Cube), the first mmWave massive MIMO software radio. M3 features a fully reconfigurable array of phased arrays, with up to 8 RF chains and 256 antenna elements. Despite the orders of magnitude larger antenna arrays, its cost is orders of magnitude lower, even when compared with state-of-the-art single RF chain mmWave software radios. In this demo, we will show M3's hardware modules, and demonstrate its usage in mmWave MIMO communication and sensing.