BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Kyle Jamieson

Kyle Jamieson contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Networking and Internet Architecturequant-pheess.SPEmerging TechnologiesInformation TheoryMachine Learningmath.ITphysics.app-ph

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
15works
0followers
8topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

15 published item(s)

preprint2026arXiv

NeuralEmu: in situ Measurement-Driven, ML-based, High-Fidelity 5G Network Emulation

Current and future applications demand ultra-low latency and consistent throughput, yet frequently traverse 5G cellular networks, so cope with volatile packet dynamics, as 5G base station schedulers dynamically react to user workloads and wireless channel conditions. The task of evaluating network algorithms in these environments is hamstrung by current tools: record-and-replay emulators sever the feedback interaction that exists between application end points and a commercial operator's proprietary 5G scheduler, while full-stack simulators rely on overly simplistic scheduling logic. To bridge this reality gap, we present NeuralEmu, a high-fidelity, machine learning-based emulation framework that learns complex 5G scheduler resource allocation behaviors directly from extremely high-resolution network telemetry tools. The first emulator to handle multiple clients, NeuralEmu utilizes machine learning to dynamically predict resource block allocations and modulation schemes based on instantaneous user buffer occupancy and channel states. To capture realistic cross-user contention, a traffic reconstruction model inverts cellular network scheduling results to recover the underlying traffic patterns of uncontrolled background users. Implemented as an high-performance Linux middlebox emulator, NeuralEmu reduces emulation error relative to the state of the art for various network applications including but not limited to 55% for web-page load time, 57% for WebRTC encoder bit rate, and 51% for cloud gaming packet one-way delay, providing an accurate, standardized testing ground for tomorrow's real-time interactive network protocols and applications.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Physics-Inspired Discrete-Phase Optimization for 3D Beamforming with PIN-Diode Extra-Large Antenna Arrays

Large antenna arrays can steer narrow beams towards a target area, and thus improve the communications capacity of wireless channels and the fidelity of radio sensing. Hardware that is capable of continuously-variable phase shifts is expensive, presenting scaling challenges. PIN diodes that apply only discrete phase shifts are promising and cost-effective; however, unlike continuous phase shifters, finding the best phase configuration across elements is an NP-hard optimization problem. Thus, the complexity of optimization becomes a new bottleneck for large-antenna arrays. To address this challenge, this paper suggests a procedure for converting the optimization objective function from a ratio of quadratic functions to a sequence of more easily solvable quadratic unconstrained binary optimization (QUBO) sub-problems. This conversion is an exact equivalence, and the resulting QUBO forms are standard input formats for various physics-inspired optimization methods. We demonstrate that a simulated annealing approach is very effective for solving these sub-problems, and we give performance metrics for several large array types optimized by this technique. Through numerical experiments, we report 3D beamforming performance for extra-large arrays with up to 10,000 elements.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Cost and Power Feasibility Analysis of Quantum Annealing for NextG Cellular Wireless Networks

In order to meet mobile cellular users' ever-increasing data demands, today's 4G and 5G networks are designed mainly with the goal of maximizing spectral efficiency. While they have made progress in this regard, controlling the carbon footprint and operational costs of such networks remains a long-standing problem among network designers. This paper takes a long view on this problem, envisioning a NextG scenario where the network leverages quantum annealing for cellular baseband processing. We gather and synthesize insights on power consumption, computational throughput and latency, spectral efficiency, operational cost, and feasibility timelines surrounding quantum technology. Armed with these data, we analyze and project the quantitative performance targets future quantum annealing hardware must meet in order to provide a computational and power advantage over CMOS hardware, while matching its whole-network spectral efficiency. Our quantitative analysis predicts that with quantum annealing hardware operating at a 102 $μ$s problem latency and 3.1M qubits, quantum annealing will achieve a spectral efficiency equal to CMOS computation while reducing power consumption by 41 kW (45% lower) in a representative 5G base station scenario with 400 MHz bandwidth and 64 antennas, and an 8 kW power reduction (16% lower) using 1.5M qubits in a 200 MHz-bandwidth 5G scenario.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Finite-Range Search Formulation of Maximum Likelihood MIMO Detection for Coherent Ising Machines

The last couple of years have seen an emergence of physics-inspired computing for maximum likelihood MIMO detection. These methods involve transforming the MIMO detection problem into an Ising minimization problem, which can then be solved on an Ising Machine. Recent works have shown promising projections for MIMO wireless detection using Quantum Annealing optimizers and Coherent Ising Machines. While these methods perform very well for BPSK and 4-QAM, they struggle to provide good BER for 16-QAM and higher modulations. In this paper, we explore an enhanced CIM model, and propose a novel Ising formulation, which together are shown to be the first Ising solver that provides significant gains in the BER performance of large and massive MIMO systems, like $16\times16$ and $16\times32$, and sustain its performance gain even at 256-QAM modulation. We further perform a spectral efficiency analysis and show that, for a $16\times16$ MIMO with Adaptive Modulation and Coding, our method can provide substantial throughput gains over MMSE, achieving $2\times$ throughput for SNR $\leq25$ dB, and up to $1.5\times$ throughput for SNR $\geq 30$ dB.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

CurvingLoRa to Boost LoRa Network Capacity via Concurrent Transmission

LoRaWAN has emerged as an appealing technology to connect IoT devices but it functions without explicit coordination among transmitters, which can lead to many packet collisions as the network scales. State-of-the-art work proposes various approaches to deal with these collisions, but most functions only in high signal-to-interference ratio (SIR) conditions and thus does not scale to many scenarios where weak receptions are easily buried by stronger receptions from nearby transmitters. In this paper, we take a fresh look at LoRa's physical layer, revealing that its underlying linear chirp modulation fundamentally limits the capacity and scalability of concurrentLoRa transmissions. We show that by replacing linear chirps with their non-linear counterparts, we can boost the capacity of concurrent LoRa transmissions and empower the LoRa receiver to successfully receive weak transmissions in the presence of strong colliding signals. Such a non-linear chirp design further enables the receiver to demodulate fully aligned collision symbols - a case where none of the existing approaches can deal with. We implement these ideas in a holistic LoRaWAN stack based on the USRP N210 software-defined radio platform. Our head-to-head comparison with two state-of-the-art research systems and a standard LoRaWAN baseline demonstrates that CurvingLoRa improves the network throughput by 1.6-7.6x while simultaneously sacrificing neither power efficiency nor noise resilience. An open-source dataset and code will be made available before publication.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dashlet: Taming Swipe Uncertainty for Robust Short Video Streaming

Short video streaming applications have recently gained substantial traction, but the non-linear video presentation they afford swiping users fundamentally changes the problem of maximizing user quality of experience in the face of the vagaries of network throughput and user swipe timing. This paper describes the design and implementation of Dashlet, a system tailored for high quality of experience in short video streaming applications. With the insights we glean from an in-the-wild TikTok performance study and a user study focused on swipe patterns, Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages a simple, non machine learning-based model of users' swipe statistics to determine the pre-buffering order and bitrate. The net result is a system that achieves 77-99% of an oracle system's QoE and outperforms TikTok by 43.9-45.1x, while also reducing by 30% the number of bytes wasted on downloaded video that is never watched.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

NG-Scope: Fine-Grained Telemetry for NextG Cellular Networks

Accurate and highly-granular channel capacity telemetry of the cellular last hop is crucial for the effective operation of transport layer protocols and cutting-edge applications, such as video on demand and videotelephony. This paper presents the design, implementation, and experimental performance evaluation of NG-Scope, the first such telemetry tool able to fuse physical-layer channel occupancy readings from the cellular control channel with higher-layer packet arrival statistics and make accurate capacity estimates. NG-Scope handles the latest cellular innovations, such as when multiple base stations aggregate their signals together to serve mobile users. End-to-end experiments in a commercial cellular network demonstrate that wireless capacity varies significantly with channel quality, mobility, competing traffic within each cell, and the number of aggregated cells. Our experiments demonstrate significantly improved cell load estimation accuracy, missing the detection of less than 1% of data capacity overall, a reduction of 82% compared to OWL, the state-of-the-art in cellular monitoring. Further experiments show that MobileInsight-based CLAW has a root-mean-squared capacity error of 30.5 Mbit/s, which is 3.3x larger than NG-Scope (9.2 Mbit/s)

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Dual-band Reconfigurable Metamaterial Surfaces for Satellite Networking

The first low earth orbit satellite networks for internet service have recently been deployed and are growing in size, yet will face deployment challenges in many practical circumstances of interest. This paper explores how a dual-band, electronically tunable smart surface can enable dynamic beam alignment between the satellite and mobile users, make service possible in urban canyons, and improve service in rural areas. Our design is the first of its kind to target dual channels in the Ku radio frequency band with a novel dual Huygens resonator design that leverages radio reciprocity to allow our surface to simultaneously steer and modulate energy in the satellite uplink and downlink directions, and in both reflective and transmissive modes of operation. Our surface, Wall-E, is designed and evaluated in an electromagnetic simulator and demonstrates 94% transmission efficiency and an 85% reflection efficiency, with at most 6 dB power loss at steering angles over a 150-degree field of view for both transmission and reflection. With a 75 sq cm surface, our link budget calculations predict a 4 dB and 24 dB improvement in the SNR of a link entering the window of a rural home in comparison to the free-space path and brick wall penetration, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

TreeStep: Tree Search for Vector Perturbation Precoding under per-Antenna Power Constraint

Vector Perturbation Precoding (VPP) can speed up downlink data transmissions in Large and Massive Multi-User MIMO systems but is known to be NP-hard. While there are several algorithms in the literature for VPP under total power constraint, they are not applicable for VPP under per-antenna power constraint. This paper proposes a novel, parallel tree search algorithm for VPP under per-antenna power constraint, called \emph{\textbf{TreeStep}}, to find good quality solutions to the VPP problem with practical computational complexity. We show that our method can provide huge performance gain over simple linear precoding like Regularised Zero Forcing. We evaluate TreeStep for several large MIMO~($16\times16$ and $24\times24$) and massive MIMO~($16\times32$ and $24\times 48$) and demonstrate that TreeStep outperforms the popular polynomial-time VPP algorithm, the Fixed Complexity Sphere Encoder, by achieving the extremely low BER of $10^{-6}$ at a much lower SNR.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

mmWall: A Reconfigurable Metamaterial Surface for mmWave Networks

To support faster and more efficient networks, mobile operators and service providers are bringing 5G millimeter wave (mmWave) networks indoors. However, due to their high directionality, mmWave links are extremely vulnerable to blockage by walls and human mobility. To address these challenges, we exploit advances in artificially engineered metamaterials, introducing a wall-mounted smart metasurface, called mmWall, that enables a fast mmWave beam relay through the wall and redirects the beam power to another direction when a human body blocks a line-of-sight path. Moreover, our mmWall supports multiple users and fast beam alignment by generating multi-armed beams. We sketch the design of a real-time system by considering (1) how to design a programmable, metamaterial-based surface that refracts the incoming signal to one or more arbitrary directions, and (2) how to split an incoming mmWave beam into multiple outgoing beams and arbitrarily control the beam energy between these beams. Preliminary results show the mmWall metasurface steers the outgoing beam in a full 360-degrees, with an 89.8% single-beam efficiency and 74.5% double-beam efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Quantum Annealing for Large MIMO Downlink Vector Perturbation Precoding

In a multi-user system with multiple antennas at the base station, precoding techniques in the downlink broadcast channel allow users to detect their respective data in a non-cooperative manner. Vector Perturbation Precoding (VPP) is a non-linear variant of transmit-side channel inversion that perturbs user data to achieve full diversity order. While promising, finding an optimal perturbation in VPP is known to be an NP-hard problem, demanding heavy computational support at the base station and limiting the feasibility of the approach to small MIMO systems. This work proposes a radically different processing architecture for the downlink VPP problem, one based on Quantum Annealing (QA), to enable the applicability of VPP to large MIMO systems. Our design reduces VPP to a quadratic polynomial form amenable to QA, then refines the problem coefficients to mitigate the adverse effects of QA hardware noise. We evaluate our proposed QA based VPP (QAVP) technique on a real Quantum Annealing device over a variety of design and machine parameter settings. With existing hardware, QAVP can achieve a BER of $10^{-4}$ with 100$μ$s compute time, for a 6$\times$6 MIMO system using 64 QAM modulation at 32 dB SNR.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

REITS: Reflective Surface for Intelligent Transportation Systems

Autonomous vehicles are predicted to dominate the transportation industry in the foreseeable future. Safety is one of the major challenges to the early deployment of self-driving systems. To ensure safety, self-driving vehicles must sense and detect humans, other vehicles, and road infrastructure accurately, robustly, and timely. However, existing sensing techniques used by self-driving vehicles may not be absolutely reliable. In this paper, we design REITS, a system to improve the reliability of RF-based sensing modules for autonomous vehicles. We conduct theoretical analysis on possible failures of existing RF-based sensing systems. Based on the analysis, REITS adopts a multi-antenna design, which enables constructive blind beamforming to return an enhanced radar signal in the incident direction. REITS can also let the existing radar system sense identification information by switching between constructive beamforming state and destructive beamforming state. Preliminary results show that REITS improves the detection distance of a self-driving car radar by a factor of 3.63.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Leveraging Quantum Annealing for Large MIMO Processing in Centralized Radio Access Networks

User demand for increasing amounts of wireless capacity continues to outpace supply, and so to meet this demand, significant progress has been made in new MIMO wireless physical layer techniques. Higher-performance systems now remain impractical largely only because their algorithms are extremely computationally demanding. For optimal performance, an amount of computation that increases at an exponential rate both with the number of users and with the data rate of each user is often required. The base station's computational capacity is thus becoming one of the key limiting factors on wireless capacity. QuAMax is the first large MIMO centralized radio access network design to address this issue by leveraging quantum annealing on the problem. We have implemented QuAMax on the 2,031 qubit D-Wave 2000Q quantum annealer, the state-of-the-art in the field. Our experimental results evaluate that implementation on real and synthetic MIMO channel traces, showing that 10~$μ$s of compute time on the 2000Q can enable 48 user, 48 AP antenna BPSK communication at 20 dB SNR with a bit error rate of $10^{-6}$ and a 1,500 byte frame error rate of $10^{-4}$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

PBE-CC: Congestion Control via Endpoint-Centric, Physical-Layer Bandwidth Measurements

Wireless 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 well-defined API to the mobile's wireless 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 very rapidly. We implement a proof-of-concept prototype of the PBE measurement module on software-defined radios and the PBE sender and receiver in C. An extensive performance evaluation compares PBE-CC head to head against the leading cellular-aware and wireless-oblivious congestion control protocols proposed in the research community and in deployment, in mobile and static mobile scenarios, and over busy and quiet networks. Results show 6.3% higher average throughput than BBR, while simultaneously reducing 95th percentile delay by 1.8x.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Towards Quantum Belief Propagation for LDPC Decoding in Wireless Networks

We present Quantum Belief Propagation (QBP), a Quantum Annealing (QA) based decoder design for Low Density Parity Check (LDPC) error control codes, which have found many useful applications in Wi-Fi, satellite communications, mobile cellular systems, and data storage systems. QBP reduces the LDPC decoding to a discrete optimization problem, then embeds that reduced design onto quantum annealing hardware. QBP's embedding design can support LDPC codes of block length up to 420 bits on real state-of-the-art QA hardware with 2,048 qubits. We evaluate performance on real quantum annealer hardware, performing sensitivity analyses on a variety of parameter settings. Our design achieves a bit error rate of $10^{-8}$ in 20 $μ$s and a 1,500 byte frame error rate of $10^{-6}$ in 50 $μ$s at SNR 9 dB over a Gaussian noise wireless channel. Further experiments measure performance over real-world wireless channels, requiring 30 $μ$s to achieve a 1,500 byte 99.99$\%$ frame delivery rate at SNR 15-20 dB. QBP achieves a performance improvement over an FPGA based soft belief propagation LDPC decoder, by reaching a bit error rate of $10^{-8}$ and a frame error rate of $10^{-6}$ at an SNR 2.5--3.5 dB lower. In terms of limitations, QBP currently cannot realize practical protocol-sized ($\textit{e.g.,}$ Wi-Fi, WiMax) LDPC codes on current QA processors. Our further studies in this work present future cost, throughput, and QA hardware trend considerations.

0 citations0 reviewsNo code linkedOpen access