One of the key missions of sixth-generation (6G) mobile networks is to deploy large-scale artificial intelligence (AI) models at the network edge to provide remote-inference services for edge devices. The resultant platform, known as edge inference, will support a wide range of Internet-of-Things applications, such as autonomous driving, industrial automation, and augmented reality. Given the mission-critical and time-sensitive nature of these tasks, it is essential to design edge inference systems that are both reliable and capable of meeting stringent end-to-end (E2E) latency constraints. Existing studies, which primarily focus on communication reliability as characterized by channel outage probability, may fail to guarantee E2E performance, specifically in terms of E2E inference accuracy and latency. To address this limitation, we propose a theoretical framework that introduces and mathematically characterizes the inference outage (InfOut) probability, which quantifies the likelihood that the E2E inference accuracy falls below a target threshold. Under an E2E latency constraint, this framework establishes a fundamental tradeoff between communication overhead (i.e., uploading more sensor observations) and inference reliability as quantified by the InfOut probability. To find a tractable way to optimize this tradeoff, we derive accurate surrogate functions for InfOut probability by applying a Gaussian approximation to the distribution of the received discriminant gain. Experimental results demonstrate the superiority of the proposed design over conventional communication-centric approaches in terms of E2E inference reliability.

Federated learning(FL) has emerged as a popular approach for collaborative machine learning insixth-generation(6G) networks, primarily due to its privacy-preserving capabilities. The deployment of FL algorithms is expected to empower a wide range ofInternet-of-Things(IoT) applications, e.g., autonomous driving, augmented reality, and healthcare. The mission-critical and time-sensitive nature of these applications necessitates the design of low-latency FL frameworks that guarantee high learning performance. In practice, achieving low-latency FL faces two challenges: the overhead of computing and transmitting high-dimensional model updates, and the heterogeneity incommunication-and-computation(C2) capabilities across devices. To address these challenges, we propose a novel-aware framework for optimal batch-size control that minimizesend-to-end(E2E) learning latency while ensuring convergence. The framework is designed to balance a fundamental C2tradeoff as revealed through convergence analysis. Specifically, increasing batch sizes improves the accuracy of gradient estimation in FL and thus reduces the number of communication rounds required for convergence, but results in higher per-round latency, and vice versa. The associated problem of latency minimization is intractable; however, we solve it by designing an accurate and tractable surrogate for convergence speed, with parameters fitted to real data. This approach yields two batch-size control strategies tailored to scenarios with slow and fast fading, while also accommodating device heterogeneity. Extensive experiments using real datasets demonstrate that the proposed strategies outperform conventional batch-size adaptation schemes that do not consider the C2tradeoff or device heterogeneity.

Harnessing multi-level electron transitions, Rydberg Atomic REceivers (RAREs) can detect wireless signals across a wide range of frequency bands, from Megahertz to Terahertz. This capability enables multi-band wireless communications and sensing (CommunSense). Existing research on multi-band RAREs primarily focuses on experimental demonstrations, lacking a tractable model to mathematically characterize their mechanisms. This issue leaves the multi-band RARE as a black box and poses challenges in its practical applications. To fill in this gap, this paper investigates the underlying mechanism of multi-band RAREs and explores their optimal performance. For the first time, an analytical transfer function with a closed-form expression for multi-band RAREs is derived by solving the quantum response of Rydberg atoms. It shows that a multi-band RARE simultaneously serves as amulti-band atomic mixerfor down-converting multi-band signals and amulti-band atomic amplifierthat reflects its sensitivity to each band. Further analysis of the atomic amplifier unveils that the intrinsic gain at each frequency band can be decoupled into aglobal gainterm and aRabi attentionterm. The former determines the overall sensitivity of a RARE to all frequency bands of wireless signals. The latter influences the allocation of the overall sensitivity to each frequency band, representing a unique attention mechanism of multi-band RAREs. The optimal design of the global gain is provided to maximize the overall sensitivity of multi-band RAREs. Subsequently, the optimal Rabi attentions are also derived to maximize the practical multi-band CommunSense performance. An experiment platform is built to validate the effectiveness of the derived transfer function, and numerical results confirm the superiority of multi-band RAREs.

A distinctive function of sixth-generation (6G) networks is the integration of distributed sensing and edge artificial intelligence (AI) to enable intelligent perception of the physical world. This resultant platform, termed integrated sensing and edge AI (ISEA), is envisioned to enable a broad spectrum of Internet-of-Things (IoT) applications, including remote surgery, autonomous driving, and holographic telepresence. Recently, the communication bottleneck confronting the implementation of an ISEA system is overcome by the development of over-the-air computing (AirComp) techniques, which facilitate simultaneous access through over-the-air data feature fusion. Despite its advantages, AirComp with uncoded transmission remains vulnerable to interference. To tackle this challenge, we propose AirBreath sensing, a spectrum-efficient framework that cascades feature compression and spread spectrum to mitigate interference without bandwidth expansion. This work reveals a fundamental tradeoff between these two operations under a fixed bandwidth constraint: increasing the compression ratio may reduce sensing accuracy but allows for more aggressive interference suppression via spread spectrum, and vice versa. This tradeoff is regulated by a key variable called breathing depth, defined as the feature subspace dimension that matches the processing gain in spread spectrum. To optimally control the breathing depth, we mathematically characterize and optimize this aforementioned tradeoff by designing a tractable surrogate for sensing accuracy, measured by classification discriminant gain (DG). Experimental results on real datasets demonstrate that AirBreath sensing effectively mitigates interference in ISEA systems, and the proposed control algorithm achieves near-optimal performance as benchmarked with a brute-force search.

There is a broad consensus that artificial intelligence (AI) will be a defining component of the sixth-generation (6G) networks. As a specific instance, AI-empowered sensing will gather and process environmental perception data at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many applications, such as autonomous driving and industrial manufacturing, are latency-sensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, the 5G-style ultra-reliable and low-latency communication (URLLC) techniques designed with communication reliability and agnostic to the data may fall short in achieving the optimal E2E performance of perceptive wireless systems. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of the E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal tradeoff. We validate the accuracy of the proposed method through experimental results, and show that the proposed ultra-Lola inference framework outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.

Rydberg Atomic REceivers (RAREs) have shown compelling advantages in precise measurement of radio-frequency signals, empowering quantum wireless sensing. Existing RARE-based sensing systems primarily rely on the heterodyne-sensing technique, which introduces an extra reference source to serve as the atomic mixer. However, this approach entails a bulky transceiver architecture, requiring additional reference sources and transmitter-receiver signal decoupling. To address this problem, we propose a novel concept called selfheterodyne sensing. It utilizes the self-interference generated by the transmitted sensing signal as the reference signal, thus greatly simplifying the transceiver architecture. We derive the transmission model of self-heterodyne sensing and reveal that a self-heterodyne RARE functions as an atomic autocorrelator, where the received signal represents the autocorrelation of the transmitted signal at different delays. This characteristic translates the range of a sensing target into the frequency of the received signal. Inspired by this finding, a two-stage algorithm is devised to estimate the target range via frequency estimation and Newton refinement. Numerical results validate the superiority of the proposed quantum self-heterodyne sensing method.

One mission of sixth-generation (6G) networks is to deploy large-scale artificial intelligence (AI) models at the network edge to enable intelligent services for edge devices. The resultant platform, known as edge inference, will support a wide range of Internet-of-Things applications, such as autonomous driving, industrial automation, and augmented reality. Given the mission-critical and time-sensitive nature of these tasks, existing studies, which primarily focus on channel outage probability, may fail to ensure E2E inference accuracy and latency. To address this limitation, we propose a theoretical framework that introduces the inference outage (InfOut) probability, quantifying the likelihood that E2E inference accuracy falls below a target threshold. Under latency constraints, this framework reveals a fundamental tradeoff between communication overhead and inference reliability. To optimize this tradeoff, we derive tractable surrogate functions for InfOut probability based on a Gaussian approximation of the receive discriminant gain. Experiments demonstrate the superiority of the proposed design over conventional communication-centric approaches.

Artificial intelligence (AI) is expected to be a defining component in sixth-generation (6G) wireless networks. One specific use is AI-empowered sensing, where sensor data will be processed at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many sensing applications, such as autonomous driving and industrial manufacturing, are latencysensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, data-agnostic ultrareliable and low-latency communication (URLLC) techniques designed for 5G fall short in achieving the optimal E2E sensing performance. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal balance. The experimental results show that the proposed approach outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.

Thesixth-generation(6G) mobile networks will feature the widespread deployment ofartificial intelligence(AI) algorithms at the network edge, which provides a platform for supporting robotic edge intelligence systems. In such a system, a large-scaleknowledge graph(KG) is operated at an edge server as a “remote brain” to guide remote robots on environmental exploration or task execution. In this paper, we present a new air-interface framework targeting the said systems, called knowledge-based roboticsemantic communications(SemCom), which consists of a protocol and relevant transmission techniques. First, the proposed robotic SemCom protocol defines a sequence of system operations for executing a given robotic task. They include identification of all task-relevantknowledge paths(KPs) on the KG, semantic matching between KG and object classifier, and uploading of robot’s observations for objects recognition and feasible KP identification. Next, to supportultra-low-latency (observation) feature transmission(ULL-FT), we propose a novel transmission approach that exploits classifier’s robustness, which is measured byclassification margin, to compensate for a highbit error probability(BEP) resulting from ultra-low-latency transmission (e.g., short packet and/or no coding). By utilizing the tractableGaussian mixture(GM) model, we mathematically derive the relation between BEP and classification margin under constraints on classification accuracy and transmission latency. The result sheds light on system requirements to support ULL-FT. Furthermore, for the case where the classification margin is insufficient for coping with channel distortion, we enhance the ULL-FT approach by studying retransmission and multi-view classification for enlarging the margin and further quantifying corresponding requirements. Finally, experiments using deep neural networks as classifier models and real datasets are conducted to demonstrate the effectiveness of ULL-FT in communication latency reduction while providing a guarantee on accurate feasible KP identification.

While vacuum-based bending actuation offers benefits such as safety and compactness in soft robotics, it is often overlooked due to its limited actuation pressure, which restricts both bending angle and force output. This study presents a crease-free, origami-inspired vacuum bending actuator that advances both state-of-the-art vacuum bending actuators and traditional origami deformation principles by introducing orderly self-folding through optimized stiffness distribution. Achieved through finite element method (FEM), this design provides several advantages: (i) Self-folding allows for high bending angles (up to 138$^{\circ }$) in a compact form. (ii) The crease-free design facilitates 3D printing from a single soft material using a consumer-level fused filament fabrication (FFF) printer, specifically thermoplastic polyurethane (TPU) with a Shore hardness of 60A, potentially higher flexibility and durability. (iii) The compact configuration enables modular design, supporting reconfiguration as demonstrated in adaptable locomotion soft robots. (iv) The large bending angles allow the actuator to wrap around objects, offering extensive contact compared to other designs. This capability, combined with its vacuum-driven mechanism, enables synergy with self-closing suction cups in an octopus-like vacuum gripper, providing large versatility and grasping force for handling a wide range of objects, from small, irregular shapes to larger, flat items.

The sixth-generation (6G) mobile networks will feature the widespread deployment of artificial intelligence (AI) algorithms at the network edge, which provides a platform for edge intelligence. In this paper, we propose a new air-interface framework targeting the edge inference systems, called ultra-low-latency (observation) feature transmission (ULL-FT). It consists of a novel transmission approach that exploits classifier’s robustness, which is measured by classification margin, to compensate for a high bit error probability (BEP) resulting from ultra-low-latency transmission (e.g., short packet and/or no coding). By utilizing the tractable Gaussian mixture (GM) model, we mathematically derive the relation between BEP and classification margin under constraints on classification accuracy and transmission latency. The result sheds light on system requirements to support ULL-FT. Finally, experiments using deep neural networks (DNN) as classifier models and real datasets are conducted to demonstrate the effectiveness of ULL-FT in communication latency reduction while providing a guarantee on classification accuracy.

Federated Learning(FL) is a widely embraced paradigm for distilling artificial intelligence from distributed mobile data. However, the deployment of FL in mobile networks can be compromised by exposure to interference from neighboring cells or jammers. Existing interference mitigation techniques require multi-cell cooperation or at least interference channel state information, which is expensive in practice. On the other hand, power control that treats interference as noise may not be effective due to limited power budgets, and also that this mechanism can trigger countermeasures by interference sources. As a practical approach for protecting FL against interference, we proposeSpectrum Breathing, which cascades stochastic-gradient pruning and spread spectrum to suppress interference without bandwidth expansion. The cost is higher learning latency by exploiting the graceful degradation of learning speed due to pruning. We synchronize the two operations such that their levels are controlled by the same parameter,Breathing Depth. To optimally control the parameter, we develop a martingale-based approach to convergence analysis of Over-the-Air FL with spectrum breathing, termed AirBreathing FL. We show a performance tradeoff between gradient-pruning and interference-induced error as regulated by the breathing depth. Given receive SIR and model size, the optimization of the tradeoff yields two schemes for controlling the breathing depth that can be either fixed or adaptive to channels and the learning process. As shown by experiments, in scenarios where traditional Over-the-Air FL fails to converge in the presence of strong interference, AirBreahing FL with either fixed or adaptive breathing depth can ensure convergence where the adaptive scheme achieves close-to-ideal performance.

Federated Learning (FL) is a widely embraced paradigm for distilling artificial intelligence from distributed mobile data. However, the deployment of FL in mobile networks is compromised due to the exposure to interference from neighboring cells, besides a communication bottleneck caused by the uploading of high-dimensional model updates. Existing interference mitigation techniques require multi-cell cooperation or at least interference Channel State Information (CSI), which is expensive in practice. To address these challenges, we propose Spectrum Breathing, which cascades stochastic-gradient pruning and spread spectrum to suppress interference without bandwidth expansion. The cost is higher learning latency by exploiting the graceful degradation of learning speed due to pruning. We synchronize the two operations using a common parameter, Breathing Depth, and develop a martingale-based approach to convergence analysis of the Over-the-Air FL with Spectrum Breathing (AirBreathing FL). Given the receive SIR and model size, the optimization of the tradeoff between pruning and interference-induced error yields the scheme for controlling the breathing depth that can be adaptive to channels and learning process. Experiments show that AirBreathing FL with adaptive breathing depth can obtain close-to-ideal performance in scenarios where traditional over-the-air FL fails to converge in the presence of strong interference.

Recent advances in soft robotics in academia have led to the adoption of soft grippers in industrial settings. Due to their soft bending actuators, these grippers can handle delicate objects with great care. However, due to their flexibility, the actuators are prone to out-of-plane deformations upon asymmetric loading. These undesired deformations lead to reduced grasp performance and may cause instability or failure of the grip. While the state-of-the-art contributions describe complex designs to limit those deformations, this work focuses on a complementary path investigating the material distribution. In this paper, a novel bending actuator is developed with improved out-of-plane deformation resistance by optimizing the material distribution in multi-material designs composed of two polymers with different mechanical properties. This is made possible by the strong interfacial strength of Diels-Alder chemical bonds in the used polymers, which have a self-healing capability. A Solid Isotropic Material with Penalization (SIMP) topology optimization is performed to increase the out-of-plane resistance. The actuator is simulated using FEA COMSOL in which the (hyper) elastic materials are simulated by Mooney-Rivlin models, fitted on experimental uniaxial tensile test data. This multi-material actuator and a reference single material actuator were manufactured and modeled. Via experimental characterization and validation in FEA simulations, it is shown that the actuator out-of-plane stiffness, characterized by the in-plane bending angle and out-of-plane bending angle, can be increased by an optimized multi-material composition, without changing the geometrical shape of the actuator.

In satellite networks, information can be transmitted either directly by inter-satellite links or the movement of satellites carrying. Consequently, how to quantify network capacity, considering both the carrying and transmission capability of satellites is crucial to the deployment of satellite networks. In this paper, we define the capacity of satellite networks consisting of both, and propose a strategy to exploit the mobile carrying of satellites under the constraint of service requirements. Then, we reveal the theoretical relationship between satellite carrying and the network capacity. The theoretical analysis and simulated results show that 1) satellite carrying can improve the network capacity when the service delay constraints could be released; 2) The capacity gain from satellite carrying is influenced by network parameters, such as orbital altitude, number of satellites, and storage capacity.

Recently, satellite networks have played an increasingly important role in both military and civilian fields. With the continual growth of the network size, the assessment of link criticality is of great significance to protect or attack satellite networks. With regard to the dynamic topologies and store-carry-forward transmission paradigm in satellite networks, detecting critical links should fully consider the relationship of consecutive snapshots and the key performance of the traffic, which raises great challenges. In this paper, we explore critical link sequence of satellite networks from the perspective of delay. We first formulate the problem based on the time-expanded graph model and discuss its convexity. Then, by exploring the space-time relationship between the criticality of different link at different slots, a heuristic critical link sequence detection algorithm (CLSD) is proposed. The simulation proves that deleting the critical link sequence given by the algorithm can effectively prolong the minimum transmission delay of the network and verifies the importance of network vulnerability assessment from the perspective of delay.

The conventional nonorthogonal multiple access (NOMA) strategy has secrecy challenge in coordinated multipoint (CoMP) networks. Under the secrecy considerations, this paper focuses on the security‐based NOMA system, which aims to improve the physical layer security issues of conventional NOMA in the coordinated multipoint (NOMA‐CoMP) networks. The secrecy performance of S‐NOMA in CoMP, that is, the secrecy sum‐rate and the secrecy outage probability, is analysed. In contrast to the conventional NOMA (C‐NOMA), the results show that the proposed S‐NOMA outperforms C‐NOMA in terms of the secrecy outage probability and security‐based effective sum‐rate.

HAWQ, developed at Pivotal, is a massively parallel processing SQL engine sitting on top of HDFS. As a hybrid of MPP database and Hadoop, it inherits the merits from both parties. It adopts a layered architecture and relies on the distributed file system for data replication and fault tolerance. In addition, it is standard SQL compliant, and unlike other SQL engines on Hadoop, it is fully transactional. This paper presents the novel design of HAWQ, including query processing, the scalable software interconnect based on UDP protocol, transaction management, fault tolerance, read optimized storage, the extensible framework for supporting various popular Hadoop based data stores and formats, and various optimization choices we considered to enhance the query performance. The extensive performance study shows that HAWQ is about 40x faster than Stinger, which is reported 35x-45x faster than the original Hive.