Recent Preprints

  • Wall-Street: Smart Surface-Enabled 5G mmWave for Roadside Networking (arxiv)
    May 10, 2024
    Authors: Kun Woo Cho, Prasanthi Maddala, Ivan Seskar, Kyle Jamieson. Abstract: 5G mmWave roadside networks promise high-speed wireless connectivity, but face significant challenges in maintaining reliable connections for users moving at high speed. Frequent handovers, complex beam alignment, and signal attenuation due to obstacles like car bodies lead to service interruptions and degraded performance. We present Wall-Street, a smart surface installed on vehicles to enhance 5G mmWave connectivity for users inside. Wall-Street improves mobility management by (1) steering outdoor mmWave signals into the vehicle, ensuring coverage for all users; (2) enabling simultaneous serving cell data transfer and candidate handover cell measurement, allowing seamless handovers without service interruption; and (3) combining beams from source and target cells during a handover to increase reliability. Through its flexible and diverse signal manipulation capabilities, Wall-Street provides uninterrupted high-speed connectivity for latency-sensitive applications in challenging mobile environments. We have implemented and integrated Wall-Street in the COSMOS testbed and evaluated its real-time performance with four gNBs and a mobile client inside a surface-enabled vehicle, driving on a nearby road. Wall-Street achieves a 2.5-3.4x TCP throughput improvement and a 0.4-0.8x reduction in delay over a baseline 5G Standalone handover protocol.
  • Multi Digit Ising Mapping for Low Precision Ising Solvers (arxiv)
    April 8, 2024
    Authors: Abhishek Kumar Singh, Kyle Jamieson. Abstract: The last couple of years have seen an ever-increasing interest in using different Ising solvers, like Quantum annealers, Coherent Ising machines, and Oscillator-based Ising machines, for solving tough computational problems in various domains. Although the simulations predict massive performance improvements for several tough computational problems, the real implementations of the Ising solvers tend to have limited precision, which can cause significant performance deterioration. This paper presents a novel methodology for mapping the problem on the Ising solvers to artificially increase the effective precision. We further evaluate our method for the Multiple-Input-Multiple-Output signal detection problem.
  • X-ResQ: Reverse Annealing for Quantum MIMO Detection with Flexible Parallelism (arxiv)
    February 28, 2024
    Authors: Minsung Kim, Abhishek Kumar Singh, Davide Venturelli, John Kaewell, Kyle Jamieson. Abstract: Quantum Annealing (QA)-accelerated MIMO detection is an emerging research approach in the context of NextG wireless networks. The opportunity is to enable large MIMO systems and thus improve wireless performance. The approach aims to leverage QA to expedite the computation required for theoretically optimal but computationally-demanding Maximum Likelihood detection to overcome the limitations of the currently deployed linear detectors. This paper presents X-ResQ, a QA-based MIMO detector system featuring fine-grained quantum task parallelism that is uniquely enabled by the Reverse Annealing (RA) protocol. Unlike prior designs, X-ResQ has many desirable system properties for a parallel QA detector and has effectively improved detection performance as more qubits are assigned. In our evaluations on a state-of-the-art quantum annealer, fully parallel X-ResQ achieves near-optimal throughput (over 10 bits/s/Hz) for $4\times6$ MIMO with 16-QAM using six levels of parallelism with 240 qubits and $220~\mu$s QA compute time, achieving 2.5--5$\times$ gains compared against other tested detectors. For more comprehensive evaluations, we implement and evaluate X-ResQ in the non-quantum digital setting. This non-quantum X-ResQ demonstration showcases the potential to realize ultra-large $1024\times1024$ MIMO, significantly outperforming other MIMO detectors, including the state-of-the-art RA detector classically implemented in the same way.
  • Evolving Mobile Cloud Gaming with 5G Standalone Network Telemetry (arxiv)
    February 6, 2024
    Authors: Haoran Wan, Kyle Jamieson. Abstract: Mobile cloud gaming places the simultaneous demands of high capacity and low latency on the wireless network, demands that Private and Metropolitan-Area Standalone 5G networks are poised to meet. However, lacking introspection into the 5G Radio Access Network (RAN), cloud gaming servers 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 Telesa, a passive, incrementally-deployable, and independently-deployable Standalone 5G network telemetry system that streams 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. We design, implement, and evaluate a Telesa telemetry-enhanced game streaming platform, demonstrating exact congestion-control that can better adapt game video bitrate while simultaneously controlling end-to-end latency, thus maximizing game quality of experience. Our experimental evaluation on a production 5G Standalone network demonstrates a 178-249% Quality of Experience improvement versus two state-of-the-art cloud gaming applications.
  • LoLa: Low-Latency Realtime Video Conferencing over Multiple Cellular Carriers (arxiv)
    December 19, 2023
    Authors: Sara Ayoubi, Giulio Grassi, Giovanni Pau, Kyle Jamieson, Renata Teixeira. Abstract: LoLa is a novel multi-path system for video conferencing applications over cellular networks. It provides significant gains over single link solutions when the link quality over different cellular networks fluctuate dramatically and independently over time, or when aggregating the throughput across different cellular links improves the perceived video quality. LoLa achieves this by continuously estimating the quality of available cellular links to decide how to strip video packets across them without inducing delays or packet drops. It is also tightly coupled with state-of-the-art video codec to dynamically adapt video frame size to respond quickly to changing network conditions. Using multiple traces collected over 4 different cellular operators in a large metropolitan city, we demonstrate that LoLa provides significant gains in terms of throughput and delays compared to state-of-the-art real-time video conferencing solution.