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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

MobiCom 2026

AmbientRider: Towards Infrastructure-Independent Ambient LoRa Backscatter

Huixin Dong, Lingyun Jin, Renyi Zhi, Zhenkai Peng, Qian Zhang, Wei Wang

The vision of ambient LoRa backscatter is to achieve long-range, pervasive connectivity by leveraging existing LoRa signals. However, requiring transmitter-side coordination limits widespread deployment because LoRa infrastructure needs to generate tag-specific traffic or allocate airtime and spectrum resources, thereby disrupting its primary services. We present AmbientRider, an infrastructure-independent LoRa backscatter system that operates over the signals without any transmitter-side coordination. Our key insight is that commodity LoRa chirps contain exploitable timing and phase structure that enables blind synchronization, channel adaptation, and interference cancellation, thereby supporting infrastructure-independent LoRa backscatter. We implement AmbientRider with off-the-shelf components and evaluate it over uncoordinated commercial LoRa transmitters. The system achieves up to 19.0 kbps throughput with the wake-up sensitivity of -24 dBm when uncooperative ambient gateways transmit across the entire 2.4 GHz ISM band with randomly varying spreading factors (SFs) and channels.

MobiCom 2026

CRISP: Co-designing Pruning and Scheduling for Efficient MoE Inference on Edge Servers

Congwei Zhang, Youming Tao, Yifei Zou, Yu Yang, Ruirui Zhang, Xiaoyu Zhang, Falko Dressler, Xiuzhen Cheng, Dongxiao Yu

Mixture-of-Experts (MoE) inference on memory-constrained edge platforms is often dominated by expert-weight movement rather than computation. Although each token activates only a small subset of experts, routing is input-dependent, so all experts must remain accessible and memory movement dominates latency. Existing approaches such as uniform pruning and expert dropping overlook two properties of MoE execution: expert combinations are highly skewed in frequency, and experts contribute asymmetrically within each combination. We present CRISP, a combination-aware framework that co-designs pruning and runtime scheduling for efficient MoE serving on memory-constrained edge platforms. CRISP reorders neurons by importance so one stored expert can support multiple execution widths, assigns differentiated execution to hot, warm, and cold combinations based on frequency and expert role, and exploits heterogeneous widths to overlap expert loading with computation. Across Mixtral-8x7B and DeepSeek-MoE-16B, CRISP improves throughput by up to 2.1× at the same memory budget while limiting accuracy loss to 3.6% relative to full-width inference. Our results highlight that efficient edge MoE serving requires jointly optimizing which expert capacity is preserved and how expert execution is scheduled.

MobiCom 2026

ExoMem: OS-Governed Memory for Local Large-LLM Inference on Mobile Edge Devices

Jun You, Kun Wang, Jiesong Chen, Zhidan Liu, ZhenJiang Li

High-quality large language model inference is increasingly used on mobile edge, from satellites to smart homes, where limited connectivity or privacy requirements motivate local model inference. In many such settings, token generation is inherently compute-limited and added latency may be acceptable, but output quality, memory feasibility and crash-free execution are essential. However, on today's mobile edge with unified-memory architectures, running a large model near the device limit can trigger non-deterministic out-of-memory failures. This is because deep learning frameworks retain freed buffers inside caching pools, thus continuing to occupy shared DRAM, which the OS cannot reclaim. During inference, fragmentation and conservative reuse can produce growing memory usage, eventually exceeding the memory availability. In this paper, we present ExoMem, an OS-governed middleware that restores OS authority over physical pages without sacrificing framework compatibility. ExoMem keeps model topology and operators in the framework, while anchoring weights in OS-managed mappings, and introduces a dormant state, whose virtual addresses remain stable for correctness but whose physical pages are immediately reclaimable when needed, leveraging standard OS page caching as an implicit multi-tier hierarchy. Implemented as a plug-and-play extension, ExoMem enables stable local inference for substantially larger LLMs on mobile edge, including 72B-class models on 64GB NVIDIA Jetson devices, while preserving headroom for concurrent workloads.

MobiCom 2026

LeanStream: A Speculate-and-Refine Streaming Framework for Efficient on-Device LLM Inference

Renyuan Liu, Yuyang Leng, Kaiyan Liu, Yuzhou Zhong, Shaohan Hu, Chun-Fu Chen, Peijun Zhao, Heechul Yun, Shuochao Yao

On-device LLM inference is attractive for privacy and responsiveness, but remains challenging on mobile and embedded devices because model weights far exceed available DRAM. Prior systems exploit activation sparsity and offload weights to SSD or flash storage, but face a fundamental systems tradeoff: accurate sparse execution decisions require the latest context, whereas efficient computation-I/O overlap requires early prediction. As a result, existing designs either serialize execution or incur redundant weight fetches, extra computation, and large cache overheads. We present LeanStream, a streaming speculate-and-refine framework for efficient on-device LLM inference. LeanStream progressively refines computation, loading, and cache-retention priorities using partial GPU results, enabling fine-grained overlap between GPU execution and storage I/O. We implement LeanStream on both mobile and embedded platforms. Compared with prior on-device LLM inference systems, LeanStream reduces memory usage by 4.8×-7.5× at the best throughput achieved by prior work, while further improving token generation throughput by 1.6×-2.1×.

MobiCom 2026

Group-Shared Low-Rank Approximation for Mobile-Efficient Pointwise Convolutions in Large-Kernel CNNs

Hao Luo, Yiting Yang, Wenyi Zhao, Man Jiang, Zhijun Lin, Ghulam Mohiuddin, Ting Jiang, Kunming Luo, Zihao Zhang, Qingsen Yan, Guoqing Wang, Wei Dong, Peng Wang

Large-kernel Convolutional Neural Networks (CNNs) deliver remarkable performance in vision tasks by significantly expanding receptive fields, yet their quadratic parameter growth critically impedes storage-efficient edge deployment. While existing efficient architectures adopt parameter-efficient depthwise separable convolution backbones that leverage techniques like low-rank approximation and weight sharing to compress depthwise convolutions, we identify a critical oversight: pointwise convolutions dominate parameter volume (>87% in models like RepLKNet-31B) and constitute the primary deployment bottleneck on resource-constrained edge devices. This results in prohibitive storage costs and severe memory-loading constraints on resource-limited devices (e.g., smartphones with 4–12 GB Random Access Memory (RAM)). To overcome this, we propose Channel Group-Shared (CGS) low-rank approximation, a novel Singular Value Decomposition (SVD)-based parameter-sharing strategy. CGS constructs a structured low-rank paradigm isomorphic to SVD decomposition, comprising shared (high-parameter-cost) down/up-projection matrices across channel groups within a layer and channel-group-specific (low-parameter-cost) scalable diagonal matrices. This group-sharing design achieves significant parameter reduction. Extensive experiments demonstrate that large-kernel CNNs (RepLKNet, ConvNeXt, SLaK) enhanced with CGS strike an empirically favorable balance between competitive performance and substantially reduced storage costs. Crucially, by alleviating storage constraints, reducing memory bandwidth pressure during loading, and minimizing model loading latency, CGS enables the feasible deployment of pre-trained large-kernel CNN models on edge devices, thereby bridging the gap between high-performance vision models and practical edge deployment.

MobiCom 2026

nn-Live: Predicting Runtime Latency for Concurrent DNN Inference on Mobile Devices

Zilin Ye, Qianyi Huang, Zhicheng Luo, Xu Chen, Qian Zhang

Modern mobile applications (e.g., AR/XR, assistant robots) increasingly rely on multiple concurrent DNN models, yet their inference latency becomes highly unpredictable under dynamic resource contention from concurrent workloads. In mobile SoCs with heterogeneous CPU architectures, such as big. LITTLE designs, the latency is affected by a complex interplay of factors, including heterogeneous core performance, cache hierarchies and shared memory bandwidth. Existing latency prediction methods either fail to capture runtime dynamics or overlook the heterogeneity in mobile processors. In this paper, we propose nn-Live that can predict live inference latency for DNN models on mobile CPUs under dynamically changing operating conditions. nn-Live leverages per-core hardware counter statistics to quantify the interference from concurrent tasks, and utilizes a lightweight linear model to estimate each core's interference contribution to a specific task, enabling robust latency prediction across dynamic execution conditions. Given the large pool of DNN models and huge potential task placement configurations, nn-Live further adopts an adaptive hierarchical sampling strategy to reduce profiling cost while preserving configuration diversity and representativeness. We build a large-scale interference analysis dataset1 across three mainstream SoCs. Evaluation results show that nn-Live achieves high prediction accuracy under diverse runtime environments, with a clear advantage over existing methods. We further demonstrate the extensibility of nn-Live to mobile accelerators (e.g., GPU/NPU).

MobiCom 2026

SpatialMind: Spatially Aware On-Device Embodied AI via Viewpoint Integration​

Haoming Wang, Qiyao Xue, Weichen Liu, Boyuan Yang, Xiangyu Yin, Wei Gao

On-device embodied AI allows local processing of visual data and prompt responses to user requests. Applying embodied AI to more complex domains requires spatial cognition, to properly comprehend the 3D environment and geometric relations between objects. However, existing vision-language models (VLMs) are weak in spatial cognition due to their limitations to 2D image understandings. In this paper, we present SpatialMind, a new on-device embodied AI technique that bridges the gap between 2D visual inputs and 3D physical reality, by injecting 3D spatial knowledge into on-device VLMs in a sparse form of 2D allocentric spatial memory. This memory is constructed by systematically aligning cross-frame visual features to a unified global viewpoint, and we prune the scope of on-device computation to maximize the compute efficiency in memory construction and VLM reasoning. Experiment results in diverse indoor environments show that SpatialMind greatly enhances the performance of spatially aware on-device embodied AI tasks, with high adaptability, robustness and compute efficiency.

MobiCom 2026

MORSE: Multipath Resilient One-Shot Localization for RIS via Spatial Spectral Encoding

Qiufeng Rui, Ruiyi Shen, Jurui Qi, Jacob Drewniak, Gabriel Rebeiz, Upamanyu Madhow, Yasaman Ghasempour

Reconfigurable intelligent surfaces (RIS) offer a promising way to control wireless propagation by engineering reflected paths. However, realizing this vision requires real-time knowledge of users' location relative to the RIS. This is challenging because RIS lack the capability to sense the channel, while existing approaches rely on beam search with high overhead. This paper presents MORSE, a novel one-shot localization framework that leverages the inherent frequency-dependent radiation patterns of RIS to create spatial-spectral encoding, where each spatial direction corresponds to a unique spectral signature. A user can then infer its angular position relative to the RIS by observing the received power spectrum. To ensure robustness in multipath environments, MORSE excites the RIS using an FMCW chirp, allowing the receiver to separate propagation paths by delay and isolate the spectral signature of the direct RIS-UE path. We implement MORSE on a 12×12 RIS prototype operating from 3 to 6 GHz and evaluate it through extensive over-the-air experiments in three environments. Our results demonstrate accurate single-shot joint azimuth and elevation angle estimation and robustness to multipath interference, enabling RIS adaptation in dynamic wireless environments.

MobiCom 2026

RadioSight: Predictive mmWave XR Network Optimization from Dynamic Neural Radio Fields

Lihao Zhang, Paul Kudyba, Zhenlin An, Haijian Sun

Next-generation extended reality (XR) networks rely on mmWave communication for multi-gigabit throughput, yet highly directional links are vulnerable to user mobility and blockages, causing frequent outages under reactive beam management. Emerging neural radio fields can predict radio propagation, but prior work remains limited to offline channel reconstruction. We introduce RadioSight, a real-time multi-modal radio field system for predictive mmWave optimization and proactive Multi-User MIMO beamforming. RadioSight combines backward beam-tracing with real-time semantic object synchronization to anticipate RF geometry changes without full model retraining. Implemented as an edge-executable pipeline for commercial 28 GHz arrays, RadioSight determines each scheduling window's beams during the preceding window without exhaustive beam sweeps. Experiments show that RadioSight reduces beam-search error by up to ~50%, improves median throughput by 2×, and enhances link stability.

MobiCom 2026

When Butterfly Meets Photoacoustic Effect

Lupeng Zhang, Minhao Cui, Qing Wang, Jie Xiong

The photoacoustic effect converts light into sound, bridging optical excitation and acoustic generation. However, existing photoacoustic systems remain largely confined to specialized applications because the generated signals are weak and typically require expensive lasers and delicate materials. In this paper, we demonstrate for the first time that natural butterfly wings can function as highly effective photoacoustic materials, enabling strong and practical photoacoustic generation using an ordinary LED. The key principle is that butterfly wings are not only excellent light absorbers but also support efficient light-heat-acoustic conversion, owing to their unique ultra-thin and lightweight membrane structures. In this work, we establish a model of the butterfly photoacoustic effect, providing a theoretical foundation for future research. We address critical technical challenges, including weak signal, as well as practical challenges such as environmental interference, realizing the first end-to-end butterfly photoacoustic system. Results show that our system achieves strong photoacoustic outputs exceeding 70 dB—comparable to a speaking voice—using off-the-shelf LEDs and naturally deceased butterfly specimens. We demonstrate the application potential of the proposed butterfly photoacoustic effect by using the butterfly as a bio-speaker for audible information delivery and for fine-grained ultrasound sensing. Our system harnesses this intriguing cross-energy phenomenon for practical applications and opens new opportunities for bio-inspired designs.

MobiCom 2026

MoiréSkin: Ultra-Sensitive Sensor–Actuator Visuo-Tactile Skin using Moiré Patterns

Linzhen Zhu, Liuxi Xing, Runqiu Wang, Ke Sun

Robots increasingly must perform dexterous object manipulation in real-world environments, yet existing VTS offer rich camera-based spatial readout while rigid optics limit force sensitivity, force range, and active compliance regulation. We present MoiréSkin, an inflatable visuo-tactile skin using flexible Moiré optics to unify ultra-sensitive tactile sensing and active compliance regulation. Dual-sided submillimeter gratings in a soft membrane amplify microscopic contact-induced strain into large, high-contrast Moiré fringe variations for tactile readout. To balance reliability and sensitivity, our tailored soft-material grating pipeline combines internal illumination with fluorescent doping to suppress ambient-light interference. Self-supervised calibration compensates for air-pressure-dependent sensing drift during pneumatic regulation, while a neural pipeline uses pre- and post-contact fringes to estimate contact position and force. Experiments show that MoiréSkin achieves 0.094 mm position and 0.0038 N contact force MAE, outperforming prior VTS by an order of magnitude. It also reaches 99.7% 10-shot classification accuracy over 7 ultra-soft materials spanning 8.73–21.72 kPa Young's modulus, and measures shear-force-related liquid mass variation with ~4 g precision, demonstrating a new path toward ultra-sensitive sensor-actuator visuo-tactile skins.

MobiCom 2026

ViPoser: Sparse-IMU Based Human Pose Estimation with Distilled Vision Foundation Priors

Hanyu Zeng, Hui Ji, Pengfei Zhou

Human pose estimation using inertial measurement units (IMUs) has gained increasing attention due to its ability to enable camera-free motion capture for numerous human-centered applications. However, estimating full-body pose from only one to three daily carried commercial-off-the-shelf IMU devices (e.g., smartphones, smartwatches, and earphones) is highly under-constrained, as sparse sensor coverage and noisy signals often lead to unstable predictions and physically implausible body configurations. To address this challenge, we propose ViPoser, a lightweight pose estimation framework that transfers human-structure priors from a large vision foundation model into an IMU-based model through knowledge distillation. Specifically, we analyze the human-centric vision model Sapiens to identify layers rich in biomechanical and skeletal knowledge, distill these representations into a compact student Transformer, and integrate the distilled priors into a multi-stage pose estimation pipeline that performs local joint estimation, full-body masked completion, and global motion recovery. This design enables the model to infer anatomically plausible full-body poses even under sparse IMU observations while remaining suitable for real-time edge deployment. Experiments on multiple real-world datasets show that ViPoser consistently outperforms state-of-the-art IMU-based methods such as IMUPoser and MobilePoser, achieving 10~30% lower pose estimation error. In addition, ViPoser produces more anatomically realistic poses, significantly reducing failure cases observed in prior methods where predicted limbs intersect the body or violate biomechanical constraints.

MobiCom 2026

UltraLEGO: A Modular Ultra-Wideband Phased Array Platform for Wireless Sensing and Localization.

Fangwei Zhang, Yimeng Liu, Franklin Wu, Yi Ding, Zhichao Cao, Ruofeng Liu

Diversified wireless sensing and localization tasks impose distinct requirements on the underlying perception platform. However, existing wireless platforms are typically fabricated with a fixed array configuration, requiring substantial hardware redesign to adapt to different applications. In this paper, we present UltraLEGO, a modular ultra-wideband phasedarray architecture that enables flexible array reconfiguration without circuit redesign while simultaneously supporting heterogeneous sensing and localization paradigms. To address phase incoherence among modularized array elements, we leverage multipath correlations in wireless channel estimation and incorporate the mechanism into a unified sensing and localization framework. Extensive experiments demonstrate that UltraLEGO adapts efficiently to diverse tasks while achieving state-of-the-art perception accuracy.

MobiCom 2026

Sparsity-Aware Scheduling for Efficient MoE-Based LLM Serving on Edge Servers

Yan Zhuang, Chen Gong, Zhenzhe Zheng, Jiale Lei, Xiaoyao Huang, Jie Wu, Fan Wu, Guihai Chen

To support real-time and domain-customized edge intelligent applications, LLMs are increasingly deployed on edge computing servers. However, we observe that current MoE-based LLM serving systems, inherited from the cloud-based architectures, face a critical sparsity-vanishing problem when serving at the edge: tokens within an input batch often activate a large fraction of experts, which incurs significant I/O overhead from frequent expert swapping between CPU and GPU memory, ultimately degrading system throughput. In this work, we propose Saber, a sparsity-aware scheduling framework optimized for efficient MoE-based LLM serving on edge servers. The core idea of Saber is to introduce a group-level batching strategy that dynamically identifies requests with similar activated experts, and coalesces them to construct sparsity-preserving input batches. To ensure efficient online execution, activation patterns are precomputed and clustered offline, enabling fast request grouping through a lightweight classifier at runtime. Additionally, Saber incorporates a utility-driven priority scheduling mechanism to maximize inference throughput while maintaining low latency by intelligently adjusting group priorities. Experiments on representative MoE-based LLMs demonstrate that Saber significantly outperforms existing sparsity-agnostic schedulers, achieving up to 3.8× lower batch execution time and 3.27× higher throughput at comparable latency levels.

MobiCom 2026

Shape-Reconfigurable Charger: Magnetic Power Pathway Programming via Computed Morphing

Xiang Cui, Yuxuan Sun, Hao Zhou, Mujun Li, Xiang-Yang Li

Wireless power transfer (WPT) enables contactless power delivery, with magnetic resonance coupling (MRC-WPT) as a leading approach. However, existing MRC-WPT systems rely on fixed transmitter (TX) geometries, limiting their tolerance to receiver (RX) displacement. While circuit-level tuning can improve power flow, it operates within fixed magnetic pathways and cannot reconfigure the field at the source, thus offering limited adaptability. This paper presents Shape-Reconfigurable Charger (SRC), an in-plane MRC-WPT charging system that senses its pose, morphs its structure to optimize power pathways, and adapts its configuration. SRC employs silicone tubes filled with liquid metal (LM) as coils and attaches liquid crystal elastomer (LCE) fibers as tendons, enabling morphable TX coils. We formulate online coil configuration as a dynamic black-box optimization and develop a distributed Bayesian optimization framework that runs on each TX, requiring no RX communication or inter-TX coordination. A hardware prototype demonstrates our performance across diverse RX placements: SRC improves power delivered to load (PDL) by 174% over frequency tuning and 96% over motorized rotation on average. By reframing the TX design as adaptive field shaping, SRC moves MRC-WPT beyond rigid transmission toward programmable magnetic pathways, advancing the vision of truly unconstrained wireless charging.

MobiCom 2026

D-Fault: Uncovering Disaggregation Faults in 5G RAN

Sixu Tan, Kuangyu Chen, Zhaowei Tan

The disaggregation of the 5G Radio Access Network (RAN) decomposes traditional base stations into distributed components such as the centralized unit (CU) and distributed unit (DU). While this architecture improves flexibility and vendor interoperability, it also turns many control procedures into distributed workflows executed by independent state machines. We show that such executions can lead to Disaggregation Faults (DF), where specification-compliant procedure interactions drive different RAN components into inconsistent control-plane states. We present D-Fault, a testing framework that systematically uncovers DF bugs by generating adversarial interleavings of control procedures and initiating them with targeted timing. A model-guided exploration reduces the intractable search space, while verification based on externally observable behavior detects inconsistencies without access to internal states. We implement D-Fault and evaluate it on SDR-based testbeds, uncovering 17 unique DF bugs that lead to serious effects such as resource corruption and denial of service.

MobiCom 2026

VasTrack: Anchoring Nocturnal Drift for Physics-Informed Blood Pressure Monitoring

Zixin Zheng, He Hao, Chuanjie Yue, Haozhen Lin, Huadong Ma, Xiaotong Hou, Zhongtao Du, Hao Cui, Anfu Zhou

Nocturnal blood pressure (BP) is a stronger predictor of cardiovascular risk than daytime measurements, yet its unobtrusive monitoring remains a formidable challenge. For practical convenience, existing cuffless methods typically rely on daytime calibration. However, through a retrospective analysis of a 1,328-patient ICU dataset, we reveal a pronounced day-night domain gap: vascular states undergo highly personalized, non-stationary drifts during sleep. Consequently, conventional daytime calibrations fail to capture nocturnal hemodynamic shifts, leading to substantial measurement errors. To address this, we propose VasTrack, a mmWave wearable system that bridges the day-night gap via a novel “Anchor-and-Track” architecture. Specifically, to establish a vascular anchor, we design a full-spectrum pressure scan method to extract the user's intrinsic vascular compliance during a standard cuff measurement, serving as a personalized biomechanical prior for nocturnal BP tracking. To track nocturnal vascular dynamics, our multi-granular tracking network transforms temporal signals into 2D phase-aligned physiological manifolds. It then employs biomechanical-based rhythmic gating to dynamically compensate for vascular tone variations using cardiopulmonary rhythms as proxies. Evaluation on 43 participants spanning healthy individuals and clinical patients over 340 hours shows that VasTrack achieves a mean error and standard deviation of 0.24±7.31 mmHg for systolic blood pressure and 0.10±4.87 mmHg for diastolic blood pressure. It also captures acute BP fluctuations exceeding 30 mmHg and a preliminary six-month evaluation suggests feasibility for extended monitoring periods, offering a potential pathway toward long-term cardiovascular health management.

MobiCom 2026

EarLog: Opportunistic Ear Physiology Sensing toward Personalized Hearing Risk Awareness

Yongzhi Huang, Jiayi Zhao, Kaishun Wu

Many headphone users risk irreversible hearing damage during high-load listening, yet current protection still relies on sound-pressure limits. Clinical tests detect damage after it becomes measurable, leaving the risk-accumulating process unobserved. The acoustic reflex (AR), an involuntary response to high acoustic load, marks when the ear enters a protective state. AR traces are weak, and existing measurements require dedicated probes and controlled conditions that are difficult to maintain during everyday audio. We present EarLog, which turns playback, the dominant interference, into an opportunistic probe. EarLog introduces content-conditioned residual sensing to suppress variations caused by nonstationary content, individual ear acoustics, and device response. It identifies weak AR through loudness-dependent changes and localized spectral irregularities in the residual. A resonance-constrained sparse finite impulse response (FIR) model captures the short-window response. Dual-microphone observations, mild-distortion recovery, and cross-session reference updates keep detection usable under low signal-to-noise ratio (SNR), fit changes, and environmental drift. EarLog runs in real time on an embedded controller using a low-cost dual-microphone add-on. We evaluate it with 81 participants, 16 headphone models, and more than 400 hours of recordings. EarLog achieves 99.0% sensitivity and 98.7% specificity on a clinically referenced benchmark. EarLog records listener-specific AR events during everyday listening, providing data for future longitudinal studies of hearing risk.

MobiCom 2026

CFMoE: Cache-Friendly Mixture of Experts Model Serving System on Edge Devices

Haoyu Zhang, Hongyi Wang, Jianwei Zheng, Yu Li, Tianyang Jiang, Zhenhua Li, Ning Jia

Deploying large language models (LLMs) on edge devices has garnered significant attention due to their inherent privacy and security benefits. However, the enormous parameter scale of modern LLMs poses fundamental challenges for resource-constrained edge devices. Mixture-of-Experts (MoE) models emerge as a promising solution by dynamically activating only a subset of experts during inference, and thus enhance computational efficiency. Nevertheless, the runtime uncertainty in expert activation still leads to high memory demands during inference. To address this, prior works concentrate on either compressing or offloading experts, all striving around the tradeoffs among the model precision, memory consumption, and serving throughput, and thus form an “impossible triangle”.

MobiCom 2026

XRLoc3D: Single UWB Receiver Array for Real-Time 3D Localization

Ryotaro Hada, Shunsuke Saruwatari, Misaki Sakuta, Kazuhiro Kizaki, Takuya Fujiwara, Takuya Fujihashi, Aditya Arun, Dinesh Bharadia

Accurate 3D localization is critical for indoor applications such as XR and drone operation, requiring centimeter-level accuracy, real-time updates, and deployment from a single site. In many indoor XR-style deployments, these requirements arise in LoS-dominant, room-scale environments where the direct path remains observable or separable. Existing optical, acoustic, WiFi/BLE, and prior UWB systems address parts of these requirements but typically struggle to satisfy them simultaneously due to infrastructure cost, limited bandwidth, or computational complexity. We present XRLoc3D, a single-anchor UWB localization framework that combines algorithmic innovation, hardware design, and bias-aware modeling. Instead of brute-force or stochastic likelihood search, XRLoc3D analytically enumerates consistent TDoA/PDoA hyperboloid intersections, collapsing billions of volumetric candidates into a tractable set and achieving maximum-likelihood accuracy with over 107× fewer evaluations than a volumetric maximum-likelihood reference. On the hardware side, we build a compact two-tier array that enables near-field observability from a single site. We further incorporate lightweight calibration to remove angle-dependent antenna biases, sustaining centimeter-level accuracy in room-scale indoor environments where the direct path remains separable. In a 5 m × 5 m × 3 m indoor space, XRLoc3D achieves a median error of 1.51 cm and a 90th-percentile error of 5.76 cm at 60 updates/s, using DW3000-class radios on the globally standardized 8 GHz UWB band. These results demonstrate practical real-time 3D localization for room-scale indoor deployments using a single-site prototype built from off-the-shelf UWB evaluation modules.

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