All Publications

88 Publications

Forthcoming

Rapid delay variations in today's access networks impair the QoE of low-latency, interactive applications, such as video conferencing. To tackle this problem, we propose Athena, a framework that correlates high-resolution measurements from Layer 1 to Layer 7 to remove the fog from the window through which today's video-conferencing congestion-control algorithms see the network. This cross-layer view of the network empowers the networking community to revisit and re-evaluate their network designs and application scheduling and rate-adaptation algorithms in light of the complex, heterogeneous networks that are in use today, paving the way for network-aware applications and application-aware networks.

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.

Forward Error Correction (FEC) provides reliable data flow in wireless networks despite the presence of noise and interference. However, its processing demands significant fraction of a wireless network’s resources, due to its computationally-expensive decoding process. This forces network designers to compromise between performance and implementation complexity. In this paper, we investigate a novel processing architecture for FEC decoding, one based on the quantum approximate optimization algorithm (QAOA), to evaluate the potential of this emerging quantum compute approach in resolving the decoding performance–complexity tradeoff.

We present FDeQ, a QAOA-based FEC Decoder design targeting the popular NextG wireless Low Density Parity Check (LDPC) and Polar codes. To accelerate QAOA-based decoding towards practical utility, FDeQ exploits temporal similarity among the FEC decoding tasks. This similarity is enabled by the fixed structure of a particular FEC code, which is independent of any time-varying wireless channel noise, ambient interference, and even the payload data. We evaluate FDeQ at a variety of system parameter settings in both ideal (noiseless) and noisy QAOA simulations, and show that FDeQ achieves successful decoding with error performance at par with state-of-the-art classical decoders at low FEC code block lengths. Furthermore, we present a holistic resource estimation analysis, projecting quantitative targets for future quantum devices in terms of the required qubit count and gate duration, for the application of FDeQ in practical wireless networks, highlighting scenarios where FDeQ may outperform state-of-the-art classical FEC decoders.

We present the design and implementation of WaveFlex, the first smart surface that enhances Private 5G networks operating under the shared-license framework in the Citizens Broadband Radio Service frequency band. WaveFlex works in the presence of frequency diversity: multiple nearby base stations operating on different frequencies, as dictated by a Spectrum Access System coordinator. It also handles time dynamism: due to the dynamic sharing rules of the CBRS band, base stations occasionally switch channels, especially when priority users enter the network. Finally, WaveFlex operates independently of the network itself, not requiring access to nor modification of the gNB or UEs, yet it
remains compliant with and effective on prevailing cellular protocols. We have designed and fabricated WaveFlex on a custom multi-layer PCB, software defined radio based network monitor, and supporting control software and hardware. Our experimental evaluation benchmarks operational Private 5G and LTE networks running at full line rate. In a realistic indoor office scenario, 5G experimental results demonstrate an 8.58~dB average SNR gain, and an average throughput gain of 10.77 Mbps under a single gNB, and 12.84 Mbps under three gNBs, corresponding to throughput improvements of 18.4% and 19.5%, respectively.

2024

Today’s wireless networks are evolving rapidly, experiencing an unprecedented surge in traffic volume, radio density, and spectral efficiency demands. This thesis addresses the critical challenges arising from this evolution of next-generation (NextG) wireless networks, focusing on three primary objectives: achieving high data rates, ultra-low latency, and massive connectivity.

To meet these diverse and demanding requirements, this thesis poses a central question: Can we build a smarter radio environment controlled and learned by software, capable of self-configuring in real-time to meet different application needs? Current approaches to handle uncontrolled wireless signals are end-to-end, but communication endpoints are limited in their ability to shape inherent propagation behavior. By focusing on changing the environment itself rather than endpoints, this thesis seeks to enhance key aspects of modern wireless networks.

Millimeter-wave technology enables multi-Gbps data rates, but its high-frequency signals are vulnerable to blockage, limiting its practical use. This thesis presents two innovative solutions to overcome this challenge. mmWall is a programmable smart surface, installed on buildings and composed of over 4,000 metamaterial elements. It can steer signals through the surface to extend outdoor mmWave signals indoors or reflect them to bypass obstacles. Wall-Street is a vehicle-mounted smart surface designed to provide robust mmWave connectivity in high-mobility environments, ensuring reliable communication even in dynamic scenarios. Extending our smart radio concepts to ultra-reliable, low-latency satellite networks, we introduce Wall-E, a dual-band smart surface that mitigates signal blockage by relaying full-duplex satellite-to-ground links, and Monolith, a smart surface that boosts data rates for inter-satellite communication. To address the growing overhead in massive Internet of Things (IoT) networks, we propose CLCP, a machine learning technique that predicts the radio environment to reduce communication overhead. This AI-driven approach complements our programmable surfaces, forming a comprehensive smart radio solution.

Given the highly complex nature of real-world systems, conceptual models alone are insufficient to fully explain them. Our solutions are implemented in physical hardware prototypes, integrated with existing network protocols, and rigorously tested through experimentation. This thesis thus offers a concrete answer to the above central question, laying the foundation for software-controlled smart radio environments in NextG wireless networks.