The Princeton Advanced Wireless Systems (PAWS) research group builds, experiments, and evaluates wireless systems that enable data networking, the localization of people, objects, and devices, and intuitive interaction with machines. Our work covers all aspects of wireless computer networks, from the basic architecture of the wireless physical layer to the reliable flow of data between Internet endpoints.

We thank the National Science Foundation, Google, InterDigital Corporation, and the Microsoft Corporation for funding our work.

We are grateful for the financial support of the Princeton University Dean of Engineering, School of Engineering and Applied Sciences, and Office of Technology Licensing.

Spotlight: Cellular Congestion Control 

Yaxiong Xie, Fan Yi, Kyle Jamieson

Cellular networks are becoming ever more sophisticated and overcrowded, imposing the most delay, jitter, and throughput damage to end-to-end network flows in today’s internet. We therefore argue for fine-grained mobile endpoint-based wireless measurements to inform a precise congestion control algorithm through a welldefined API to the mobile’s cellular physical layer. Our proposed congestion control algorithm is based on Physical-Layer Bandwidth measurements taken at the Endpoint (PBE-CC), and captures the latest 5G New Radio innovations that increase wireless capacity, yet create abrupt rises and falls in available wireless capacity that the PBE-CC sender can react to precisely and rapidly. Results show 6.3% higher average throughput than BBR, while simultaneously reducing 95th percentile delay by 1.8×.


Spotlight: Quantum Belief Propagation

Srikar Kasi, and Kyle Jamieson

Quantum Belief Propagation (MobiCom'20) is a Quantum Annealing based decoder design for Low Density Parity Check error control codes. QBP reduces the LDPC decoder to a discrete optimization problem, then embeds that reduced design onto quantum annealing hardware. Our design eschews the sequential iterative nature of the traditional belief propagation decoding. Experiments on a real-world quantum adiabatic optimizer show that QBP achieves a performance improvement over FPGA-based belief propagation decoding at channel SNRs above 6 dB.


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