arXiv subjects

Yaxiong Xie

Publications and source records attributed to Yaxiong Xie.

13 recordsLinked to original sources

arXiv2026

RANPilot: Making AI Functionalities Robust to Dynamic O-RAN Reconfigurations

The Open Radio Access Network (O-RAN) promises unprecedented flexibility through its reconfigurable architecture and AI-driven control. However, this agility exposes a critical fragility: AI models trained on one network configuration suffer significant performance degradation after an upgrade due to dramatic data drift. The standard solution, reactive retraining, is unacceptably slow, leaving the network in a suboptimal state for tens of minutes and undermining the core benefits of O-RAN's dynamism. This paper introduces RANPilot, the first framework to address this challenge through proactive AI adaptation. RANPilot constructs a lightweight "virtual O-RAN" (a trace-driven emulator) to synthesize high-fidelity training data representing the post-reconfiguration state before the physical change occurs, allowing AI models to be adapted in advance. Extensive experiments on a real-world 5G testbed demonstrate that RANPilot achieves near interruption-free AI services upon reconfiguration, reducing AI downtime by 85% to 94% against reactive baselines. By shifting the AI evolution paradigm from reactive redevelopment to proactive preparation, RANPilot explores a digital-leadoff approach to enable robust AI in reconfigurable O-RAN deployments.

cs.NI↗

arXiv2026

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.

cs.NI↗

arXiv2026

AutORAN: LLM-driven Natural Language Programming for Agile xApp Development

Traditional RAN systems are closed and monolithic, stifling innovation. The openness and programmability enabled by Open Radio Access Network (O-RAN) are envisioned to revolutionize cellular networks with control-plane applications--xApps. The development of xApps (typically by third-party developers), however, remains time-consuming and cumbersome, often requiring months of manual coding and integration, which hinders the roll-out of new functionalities in practice. To lower the barrier of xApp development for both developers and network operators, we present AutORAN, the first LLM-driven natural language programming framework for agile xApps that automates the entire xApp development pipeline. In a nutshell, AutORAN turns high-level user intents into swiftly deployable xApps within minutes, eliminating the need for manual coding or testing. To this end, AutORAN builds a fully automated xApp generation pipeline, which integrates multiple functional modules (from user requirement elicitation, AI/ML function design and validation, to xApp synthesis and deployment). We design, implement, and comprehensively evaluate AutORAN on representative xApp tasks. Results show AutORAN-generated xApps can achieve similar or even better performance than the best known hand-crafted baselines. AutORAN drastically accelerates the xApp development cycle (from user intent elicitation to roll-out), streamlining O-RAN innovation.

cs.NI↗

arXiv2026

BLADE: Adaptive Wi-Fi Contention Control for Next-Generation Real-Time Communication

Next-generation real-time communication (NGRTC) applications, such as cloud gaming and XR, demand consistently ultra-low latency. However, through our first large-scale measurement, we find that despite the deployment of edge servers, dedicated congestion control, and loss recovery mechanisms, cloud gaming users still experience long-tail latency in Wi-Fi networks. We further identify that Wi-Fi last-mile access points (APs) serve as the primary latency bottleneck. Specifically, short-term packet delivery droughts, caused by fundamental limitations in Wi-Fi contention control standards, are the root cause. To address this issue, we propose BLADE, an adaptive contention control algorithm that dynamically adjusts the contention windows (CW) of all Wi-Fi transmitters based on the channel contention level in a fully distributed manner. Our NS3 simulations and real-world evaluations with commercial Wi-Fi APs demonstrate that, compared to standard contention control, BLADE reduces Wi-Fi packet transmission tail latency by over 5X under heavy channel contention and significantly stabilizes MAC throughput while ensuring fast and fair convergence. Consequently, BLADE reduces the video stall rate in cloud gaming by over 90%.

cs.NI↗

arXiv2025

Seeing Through the Fog: Empowering Mobile Devices to Expose and Mitigate RAN Buffer Effects on Delay-Sensitive Protocols

Delay-based protocols rely on end-to-end delay measurements to detect network congestion. However, in cellular networks, Radio Access Network (RAN) buffers introduce significant delays unrelated to congestion, fundamentally challenging these protocols' assumptions. We identify two major types of RAN buffers - retransmission buffers and uplink scheduling buffers - that can introduce delays comparable to congestion-induced delays, severely degrading protocol performance. We present CellNinjia, a software-based system providing real-time visibility into RAN operations, and Gandalf, which leverages this visibility to systematically handle RAN-induced delays. Unlike existing approaches that treat these delays as random noise, Gandalf identifies specific RAN operations and compensates for their effects. Our evaluation in commercial 4G LTE and 5G networks shows that Gandalf enables substantial performance improvements - up to 7.49x for Copa and 9.53x for PCC Vivace - without modifying the protocols' core algorithms, demonstrating that delay-based protocols can realize their full potential in cellular networks.

cs.NI↗

arXiv2024

Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting

Diverse Wi-Fi-based wireless applications have been proposed, ranging from daily activity recognition to vital sign monitoring. Despite their remarkable sensing accuracy, the high energy consumption and the requirement for customized hardware modification hinder the wide deployment of the existing sensing solutions. In this paper, we propose REHSense, an energy-efficient wireless sensing solution based on Radio-Frequency (RF) energy harvesting. Instead of relying on a power-hungry Wi-Fi receiver, REHSense leverages an RF energy harvester as the sensor and utilizes the voltage signals harvested from the ambient Wi-Fi signals to enable simultaneous context sensing and energy harvesting. We design and implement REHSense using a commercial-off-the-shelf (COTS) RF energy harvester. Extensive evaluation of three fine-grained wireless sensing tasks (i.e., respiration monitoring, human activity, and hand gesture recognition) shows that REHSense can achieve comparable sensing accuracy with conventional Wi-Fi-based solutions while adapting to different sensing environments, reducing the power consumption by 98.7% and harvesting up to 4.5mW of power from RF energy.

cs.NI↗

arXiv2023

AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices

Mobile video applications today have attracted significant attention. Deep learning model (e.g. deep neural network, DNN) compression is widely used to enable on-device inference for facilitating robust and private mobile video applications. The compressed DNN, however, is vulnerable to the agnostic data drift of the live video captured from the dynamically changing mobile scenarios. To combat the data drift, mobile ends rely on edge servers to continuously evolve and re-compress the DNN with freshly collected data. We design a framework, AdaEvo, that efficiently supports the resource-limited edge server handling mobile DNN evolution tasks from multiple mobile ends. The key goal of AdaEvo is to maximize the average quality of experience (QoE), e.g. the proportion of high-quality DNN service time to the entire life cycle, for all mobile ends. Specifically, it estimates the DNN accuracy drops at the mobile end without labels and performs a dedicated video frame sampling strategy to control the size of retraining data. In addition, it balances the limited computing and memory resources on the edge server and the competition between asynchronous tasks initiated by different mobile users. With an extensive evaluation of real-world videos from mobile scenarios and across four diverse mobile tasks, experimental results show that AdaEvo enables up to 34% accuracy improvement and 32% average QoE improvement.

cs.DC↗

arXiv2023

WaveFlex: A Smart Surface for Private CBRS Wireless Cellular Networks

We present the design and implementation of WaveFlex, the first smart surface that enhances Private LTE/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 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 base station or mobile users, yet it remain 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 an operational Private LTE network running at full line rate. Results demonstrate an 8.50 dB average SNR gain, and an average throughput gain of 4.36 Mbps for a single small cell, and 3.19 Mbps for four small cells, in a realistic indoor office scenario.

cs.NI↗

arXiv2023

SiFall: Practical Online Fall Detection with RF Sensing

Falls present a significant global public health challenge, especially in today's aging society, underscoring the importance of developing an effective fall detection system. Non-invasive radio-frequency (RF) based fall detection has garnered substantial attention due to its wide coverage and privacy-preserving nature. Existing RF-based fall detection systems approach falls as an activity classification problem, assuming that human falls introduce reproducible patterns to the RF signals. However, we argue that falls are inherently accidental, making their impact uncontrollable and unforeseeable. We propose a fundamentally different approach to fall detection by shifting the focus from directly identifying hard-to-quantify falls to recognizing normal, repeatable human activities, thus treating falls as abnormal activities outside the normal activity distribution. We introduce a self-supervised incremental learning system incorporating FallNet, a deep neural network that employs unsupervised learning techniques. Our real-time fall detection system prototype leverages WiFi Channel State Information (CSI) sensing data and has been extensively tested with 16 human subjects.

cs.HC↗

arXiv2022

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.

cs.NI↗

arXiv2022

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)

cs.NI↗

arXiv2020

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.

cs.NI↗

arXiv2018

mD-Track: Leveraging Multi-Dimensionality in Passive Indoor Wi-Fi Tracking

Wi-Fi localization and tracking face accuracy limitations dictated by antenna count (for angle-of-arrival methods) and frequency bandwidth (for time-of-arrival methods). This paper presents mD-Track a device-free Wi-Fi tracking system capable of jointly fusing information from as many dimensions as possible to overcome the resolution limit of each individual dimension. Through a novel path separation algorithm, mD-Track can resolve multipath at a much finer-grained resolution, isolating signals reflected off targets of interest. mD-Track can localize human passively at a high accuracy with just a single Wi-Fi transceiver pair. mD-Track also introduces novel methods to greatly streamline its estimation algorithms, achieving real-time operation. We implement mD-Track on both WARP and cheap off-the-shelf commodity Wi-Fi hardware and evaluate its performance in different indoor environments.

cs.NI↗