The complexity of operator networks and myriad of specialized metrics produced by network function providers present a formidable challenge in retrieving and analyzing operator data, a vital component for network operations. This necessitates specialist intervention, which is time-consuming and limits customization. This paper proposes Data Intelligence for Operators Copilot, a natural language interface for retrieval and analytics tasks on operator data, leveraging foundation models. It addresses the challenges posed by operator data through a novel application of semantic search to effectively provide necessary context regarding specialized metrics. The system has outperformed state-of-the-art natural language interfaces for databases, when applied to an operator-specific benchmark dataset of expert-generated representative queries, with 66% execution accuracy.

Open RAN is an emerging paradigm in mobile networks where the Radio Access Network (RAN) functions are disaggregated and virtualized on commodity servers. Despite the importance of Open RAN research, existing platforms often lack the fidelity and stability required to address a wide range of research problems. In response to this limitation, we have developed an enterprise-scale Open RAN testbed aimed at conducting state-of-the-art research in key areas that have received limited attention due to the lack of suitable platforms. In this poster, we provide an overview of the testbed we have created and examples of the research it has enabled, with the hope of catalyzing future open RAN research and innovation.

Virtualized radio access networks (vRANs), which allow running RAN processing on commodity servers instead of proprietary hardware, are gaining adoption in cellular networks. Two properties of the vRAN's "Distributed Unit (DU)" that implements the lower RAN layers---its real-time deadlines and its black-box nature---make it challenging to provide resilience features such as upgrades and failover without long service disruptions. These properties preclude the use of existing resilience techniques like virtual machine migration or state replication that are used for typical workloads. This paper presents Atlas, the first system that provides resilience for the DU. The central insight in Atlas is to repurpose existing cellular mechanisms for wireless resilience, namely handovers and cell reselection, to provide software resilience for the DU. For planned resilience events like upgrades, we design a novel technique that simultaneously serves cells from both the old and new DUs via the same radio, and uses handovers between these cells to migrate user devices. For unplanned failures, we identify deficiencies in existing RAN protocols that disrupt cell reselection after DU failure, and show how we can eliminate these disruptions using a middlebox between the DU and higher layers. Our evaluation with a state-of-the-art 5G vRAN testbed shows that Atlas achieves minimal disruption to cellular connectivity during resilience events, while incurring low overhead.

This paper asks: “Can we detect whether a fragile product, made of porcelain or glass is damaged as it travels along the supply chain, without opening its packaging?” We ask this question in the context of the multi-billion dollar global supply chain industry of fragile products that experience large overheads due to product returns. This paper presents MiLTOn, a novel acoustic and mm-wave based solution for through-box non-invasive product integrity sensing that is sensitive to even minute sub-mm cracks in the object. MiLTOn is inspired by acoustic vibrometry used for instance to monitor cracks in railroads. Unlike traditional vibrometry, MiL-TOn is unique in its ability to sense products non-invasively using an external transducer and microphone, neither of which are in direct physical contact of the object within the box. MiLTOn pro-cesses measurements from the microphone to design a robust and environment-independent product signature that can be used to sense presence of product defects. Our extensive evaluation on a large number of fragile products of diverse materials demonstrates 97% accuracy in identifying product damage.

Location information is crucial for enabling many applications in the era of Internet-of-Things (IoT). Localizing IoT devices using existing ubiquitous WiFi infrastructure enables rapid and universal deployment of IoT devices empowered with localization service. However, current WiFi-based localization systems require the target, that needs to be localized, to be equipped with a WiFi radio that actively transmits WiFi signals. Since a WiFi radio consumes lots of power for data transmission, such radio cannot be used in energy-impoverish IoT devices (for example, locating a pill bottle in an elder's apartment). This paper presents WiTag, the first system that reuses existing WiFi infrastructures for localizing low-power backscatter tags. We build upon recent work on low power tags that communicate via backscattering ambient WiFi signals and introduce novel modeling of the backscattered signal received at WiFi access points to localize the target IoT device. We prototype WiTag using off-the-shelf WiFi chips and a customized backscatter tag and show that it achieves sub-meter localization error. Thus, WiTag achieves localization error comparable to that of state-of-the-art WiFi based localization systems while consuming orders of magnitude lower power at the target IoT devices.

Today, experiencing virtual reality (VR) is a cumbersome experience which either requires dedicated infrastructure like infrared cameras to track the headset and hand-motion controllers (e.g., Oculus Rift, HTC Vive), or provides only 3-DoF (Degrees of Freedom) tracking which severely limits the user experience (e.g., Samsung Gear). To truly enable VR everywhere, we need position tracking to be available as a ubiquitous service. This paper presents WiCapture, a novel approach which leverages commodity WiFi infrastructure, which is ubiquitous today, for tracking purposes. We prototype WiCapture using off-the-shelf WiFi radios and show that it achieves an accuracy of 0.88 cm compared to sophisticated infrared-based tracking systems like the Oculus, while providing much higher range, resistance to occlusion, ubiquity and ease of deployment.

Today, experiencing virtual reality (VR) is a cumbersome experience which either requires dedicated infrastructure like infrared cameras to track the headset and hand-motion controllers (e.g. Oculus Rift, HTC Vive), or provides only 3-DoF (Degrees of Freedom) tracking which severely limits the user experience (e.g. Samsung Gear VR). To truly enable VR everywhere, we need position tracking to be available as a ubiquitous service. This paper demonstrates WiCapture, a novel approach which leverages commodity WiFi infrastructure, which is ubiquitous today, for tracking purposes. We prototyped WiCapture using off-the-shelf WiFi radios and demonstrated that it achieves an accuracy of 0.88 cm compared to sophisticated infrared-based tracking systems like the Oculus Rift, while providing much higher range, resistance to occlusion, ubiquity and ease of deployment.

We present BackFi, a novel communication system that enables high throughput, long range communication between very low power backscatter devices and WiFi APs using ambient WiFi transmissions as the excitation signal. Specifically, we show that it is possible to design devices and WiFi APs such that the WiFi AP in the process of transmitting data to normal WiFi clients can decode backscatter signals which the devices generate by modulating information on to the ambient WiFi transmission. We show via prototypes and experiments that it is possible to achieve communication rates of up to 5 Mbps at a range of 1 m and 1 Mbps at a range of 5 meters. Such performance is an order to three orders of magnitude better than the best known prior WiFi backscatter system [27,25]. BackFi design is energy efficient, as it relies on backscattering alone and needs insignificant power, hence the energy consumed per bit is small.

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.