Due to the ability to penetrate darkness, smoke, and fog, wireless sensing technologies offer unique advantages for real-world deployments. Recent studies have mounted wireless sensing devices on mobile systems (such as drones and wheeled robots). Despite their promising potential, these approaches are predominantly designed for non-human platforms that exhibit limited mobility, typically involving translational movement with minimal rotation. When deployed on human carriers, however, the frequent body rotations and complex motion patterns significantly degrade their performance, due to the unpredictable changes in target position. In this paper, we propose Mobi2Still, which utilizes mobile human-equipped millimeter wave (mmWave) radars for people detection and tracking. The core idea is to match recurring environment information across different times to estimate radar motion in reverse, and based on this, enable accurate people detection and tracking. Specifically, Mobi2Still extracts geometric structure of scene objects which is radar-motion-independent to match the recurring environmental objects, thereby adapting both rotation and translation of mmWave radars. Furthermore, to improve Mobi2Still's generalization, we design a velocity-aware calibration mechanism to guide it to focus on scene-independent motion features. Experiments demonstrate that Mobi2Still can accurately detect and track people with 98.10% F1-score and 98.32% MOTA, achieving at least 7.54% F1-score (and 11.20% MOTA) improvements compared with state-of-the-art approaches.

Mobile Edge Computing (MEC) is a key component of 5G ecosystem, designed to support applications with stringent latency requirements. The fundamental idea is to deploy servers closer to end-users, such as on the network edge, rather than in remote clouds. While conceptually sound, operational 5G networks often lack coordination with MEC, leading to intolerably long response latency. In this work, we propose Sonata, which tightly integrates 5G RAN and MEC at the user space to ensure the performance of latency-critical video analytics. To achieve this, Sonata precisely customizes users’ service demands by fusing application-layer content changes from MEC servers with instantaneous physical-layer dynamics from the 5G Radio Access Network (RAN). It then enforces a deadline-strict resource provision to meet these service demands through real-time interactions between the 5G RAN and MEC servers, in a lightweight and standard-compatible manner. We prototype and evaluate Sonata on a software-defined 5G MEC platform. Our results demonstrate that Sonata achieves an average reduction in response latency of 67.82% compared to conventional 5G edge systems.

While 5G has rolled out since 2019 and exhibited versatile advantages, its performance under high/extreme mobility scenes (e.g., driving, high-speed railway or HSR) remains mysterious. In this work, we carry out a large-scale field-trial campaign, taking >13,000 Km round-trips on HSR moving at 250–350 Km/h, with operational 5G cellular coverage along the railway. Our empirical study reveals that coupling interaction among high mobility, 5G handover characteristics, and applications’ sluggish reaction to handover, results in catastrophic damage to user experience: low TCP bandwidth utilization of 26.6% and glitchy 4K VoD streaming. To solve the problem, we propose an edge-assisted mobility management framework called Octopus. Different from previous works, Octopus aims at a standard-compatible and easy-to-deploy solution, thus we take a new design paradigm of exploiting the edge intelligence on multi-access edge computing (MEC). We realize Octopus as a universal MEC service ready for benefiting any third-party mobile applications. We prototype, deploy, and evaluate Octopus in operational 5G, which demonstrates the significant performance gain across the full-range mobile scenarios, e.g., HSR, driving, and walking.

Ambient environment information, including reflectors’ geometrical layout, dimension, and reflectivity, is a key input to versatile millimeter-wave networking and sensing applications. It has found versatile applications in optimizing network coverage and robustness, enhancing mobile link performance, and enabling high-accuracy indoor localization and navigation. Recent approaches of deriving mmWave environment information require heavy infrastructure support and rely on costly software-defined radios, which prevent their usage in practice. In this work, we design and implement e-mmRanger, which can efficiently sense the environment without infrastructure support. e-mmRanger equips a pair of low-cost off-the-shelf mmWave radios in a commodity robot, which constantly samples the ambient environment by exchanging a series of mmWave signals while it moves. It then re-engineers the time-domain signal series to derive the spatial-domain environment structure through novel reflection path extraction and clustering algorithms. Moreover, e-mmRanger accelerates the mapping process by incorporating a novel Space-knit algorithm, which strategically plans an optimal movement route consisting of minimum sampling locations for the robot. Our experiments verify that e-mmRanger can accurately and efficiently sense the surrounding environment, and the learned information can bring multi-fold performance gain over empirical approaches in mmWave networks.

Mobile edge computing (MEC), as a key ingredient of the 5G ecosystem, is envisioned to support demanding applications with stringent latency requirements. The basic idea is to deploy servers close to end-users, e.g., on the network edge-side instead of the remote cloud. While conceptually reasonable, we find that the operational 5G is not coordinated with MEC and thus suffers from intolerable long response latency. In this work, we propose Tutti, which couples 5G RAN and MEC at the user space to assure the performance of latency-critical video analytics. To enable such capacity, Tutti precisely customizes the application service demand by fusing instantaneous wireless dynamics from the 5G RAN and application-layer content changes from edge servers. Tutti then enforces a deadline-sensitive resource provision for meeting the application service demand by real-time interaction between 5G RAN and edge servers in a lightweight and standard-compatible way. We prototype and evaluate Tutti on a software-defined platform, which shows that Tutti reduces the response latency by an average of 61.69% compared with the existing 5G MEC system, as well as negligible interaction costs.

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate simultaneously without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns. In this paper, we extensively measure the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction based on interference-resolving approaches are insufficient. Motivated by the findings, we proposeMDSR, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction,MDSRtakes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfection and reflections. Using the conflict graph,MDSRimproves the spatial reuse from three dimensions: AP association, user scheduling, and beam selection, which can determine the optimal AP-user-beam combination and minimize interference in each scheduling cycle. We prototype and evaluateMDSRon the testbed using commodity mmWave radios. The evaluation results demonstrate thatMDSRimproves network throughput by multi-folds compared with the state-of-the-art one.

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate concurrently without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns generated by commodity mmWave radios and strong reflections. In this paper, we conduct an extensive measurement on the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction-based interference-resolving approaches are insufficient. Motivated by the findings, we propose mmMuxing, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction, mmMuxing takes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfectness and reflections. Using the conflict graph, mmMuxing designs a joint user-beam selection algorithm, which can determine the optimal user-beam combination and lead to the minimum interference in each schedule. We prototype and evaluate mmMuxing over testbed using commodity mmWave radios. The evaluation results demonstrate that mmMuxing improves network throughput by multi-folds compared with the state-of-the-art.

5G, as a monumental shift in cellular communication technology, holds tremendous potential for spurring innovations across many vertical industries, with its promised multi-Gbps speed, sub-10 ms low latency, and massive connectivity. On the other hand, as 5G has been deployed for only a few months, it is unclear how well and whether 5G can eventually meet its prospects. In this paper, we demystify operational 5G networks through a first-of-its-kind cross-layer measurement study. Our measurement focuses on four major perspectives: (i) Physical layer signal quality, coverage and hand-off performance; (ii) End-to-end throughput and latency; (iii) Quality of experience of 5G's niche applications (e.g., 4K/5.7K panoramic video telephony); (iv) Energy consumption on smartphones. The results reveal that the 5G link itself can approach Gbps throughput, but legacy TCP leads to surprisingly low capacity utilization (< 32%), latency remains too high to support tactile applications and power consumption escalates to 2 - 3x over 4G. Our analysis suggests that the wireline paths, upper-layer protocols, computing and radio hardware architecture need to co-evolve with 5G to form an ecosystem, in order to fully unleash its potential.