Kyle Jamieson

The real-time knowledge of mmWave device orientation offers dual benefits for wireless networks and Internet of Things (IoT) applications: it enhances communication and localization performance through link diagnosis, and it enables context inference with asset and wearable tracking. This article presents metasurface-assisted polarization sensing (MetaPol), a novel system architecture that augments commodity mmWave access points (APs) with a carefully designed ultralow-cost anisotropic metasurface to noninvasively extract the orientation of client devices, without hindering data reception or modifying hardware. MetaPol leverages the polarization of transmitted electromagnetic (EM) waves as an accurate indicator of the orientation of linearly polarized antenna arrays, prevalent in commercial mmWave mobile and IoT devices. Yet, polarization sensing is rarely supported by commodity APs due to the need for two orthogonally polarized antenna arrays to capture the incident electric field. Instead, MetaPol creates virtual polarization channels to characterize impinging electric fields, through the conversions of wave polarization on the fly as it interacts with the metasurface. To design MetaPol, we model and exploit the properties of a unique anisotropic metamaterial based on C-shaped split-ring resonators (SRRs). We discover that, when grouped in certain configurations, these meta-elements can convert the incident polarization in a deterministic way. We show that three polarization channels are sufficient for unambiguous orientation sensing, and we provide a corresponding three-shot noncoherent protocol that extracts user orientation by comparing the power received from distinct surface areas. Through extensive over-the-air experiments with more than 1000 measurements in the mmWave band, we demonstrate that MetaPol achieves a mean error of 2.6° across practical settings with negligible compromise to the underlying data communication link.

Design for low latency networking is essential for tomorrow's interactive applications, but it is essential to deploy incrementally and universally at the network's last mile. While wired broadband ISPs are rolling out the leading queue occupancy signaling mechanisms, the cellular Radio Access Network (RAN), another important last mile to many users, lags behind these efforts. This paper proposes a new RAN design, L4Span, that abstracts the complexities of RAN queueing in a simple interface, thus tying the queue state of the RAN to end-to-end low-latency signaling all the way back to the content server. At millisecond-level timescales, L4Span predicts the RAN's queuing occupancy and performs ECN marking for both low-latency and classic flows. L4Span is lightweight, requiring minimal RAN modifications, and remains 3GPP and O-RAN compliant for maximum ease of deployment. We implement a prototype on the srsRAN open-source software in C++. Our evaluation compares the performance of low-latency as well as classic flows with or without the deployment of L4Span in various wireless channel conditions. Results show that L4Span reduces the one-way delay of both low-latency and classic flows by up to 98%, while simultaneously maintaining near line-rate throughput. The code is available at https://github.com/PrincetonUniversity/L4Span .

Tomorrow’s massive-scale Internet-of-Things (IoT) sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP’s design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users. Moreover, we measure its performance with four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2x throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.

NextG cellular networks are designed to meet Quality of Service requirements for various applications in and beyond smartphones and mobile devices. However, lacking introspection into the 5G Radio Access Network (RAN application and transport layer designers are ill-poised to cope with the vagaries of the wireless last hop to a mobile client, while 5G network operators run mostly closed networks, limiting their potential for co-design with the wider internet and user applications. This paper presents NR-Scope, a passive, incrementally-deployable, and independently-deployable Standalone 5G network telemetry system that can stream fine-grained RAN capacity, latency, and retransmission information to application servers to enable better millisecond scale, application-level decisions on offered load and bit rate adaptation than end-to-end latency measurements or end-to-end packet losses currently permit. Our experimental evaluation on various 5G Standalone base stations demonstrates NR-Scope can achieve less than 0.1% throughput error estimation for every UE in a RAN. The code is available at https://github.com/PrincetonUniversity/NR-Scope.

Congestion-control algorithms for video-conferencing applications work well in wired networks but are fragile in cellular networks due to high delay variations and variable capacity in these networks. This paper investigates the causes of delay variations in cellular networks using a cross-layer approach. By measuring a WebRTC application over LTE at both the physical and network layers, we identify the effects of such delay inflation caused by physical-layer resource scheduling and link-layer retransmissions.