Traffic scheduling plays a critical role in Time-Sensitive Networking (TSN) for ensuring high reliability and deterministic latency. In this paper, we propose a novel window-based scheduling approach for the Time-Aware Shaper (TAS). By allowing packets to wait in egress queues before forwarding, our approach relaxes the strict timing constraints imposed by existing packet-based schedulers. We employ a generalized Network Calculus (NC) framework built on an End-to-End (E2E) network model, to analyze the upper-bound latency, which is then used to assess the schedulability of Time-Critical (TC) traffic. Inspired by the Proportional–Integral–Derivative (PID) closed-loop control architecture, we introduce an Incremental PID-based Search (IPS) algorithm to optimize schedulability, where the P, I, and D terms are leveraged to scale update steps, maintain search momentum, and dampen the oscillations, respectively. To accommodate various traffic classes, throughput constraints for non-TC traffic are incorporated as bounds on window lengths. Simulation experiments were performed on a multi-node network topology carrying large traffic volumes. Under optimal PID settings, the proposed IPS algorithm was evaluated against the well-validated Simulated Annealing (SA) method under a unified scheduling framework with identical decision variables and constraints to ensure a fair comparison. Results show that IPS consistently achieves higher schedulability and requires fewer iterations for flow counts ranging from 100 to 600. Furthermore, a real-time simulation platform based on OMNeT++ was developed, and the effectiveness of the proposed wait-allowed scheduling model was validated through optimized GCL configurations.

Integrated Sensing and Communication (ISAC) systems represent a transformative paradigm for next-generation wireless networks by enabling dual-functional efficiency through simultaneous information transmission and environmental sensing. This paper investigates the critical challenge of robust beamforming design for monostatic ISAC systems operating in the near-field (NF) regime, where conventional far-field channel assumptions become fundamentally invalid. We develop a novel robust beamforming framework that optimizes minimum mean squared error (MMSE) estimation for sensing performance while guaranteeing stringent communication quality-of-service requirements. A distinctive feature of our approach lies in the proposed spherical wavefront-based channel model that incorporates both distance and angular response, providing superior accuracy compared to conventional planar wavefront approximations in NF scenarios. To resolve the inherent non-convex optimization problem with coupled sensing-communication constraints, we devise an efficient semidefinite relaxation (SDR)-based algorithm with guaranteed convergence properties. Comprehensive simulations demonstrate significant improvements in both sensing and communication performance, even under imperfect channel state information.

The digitization and intellectualization have been envisioned as the fundamental basis for future Industrial Internet of things, which integrates sensor technology, industrial control technology, communication technology, and artificial intelligence (AI). Specifically, the collaboration among these above techniques is crucial for the successful implementation of intelligent applications. This article develops an end-to-end visual control framework to accomplish multi-crane collaborative sorting in wireless time sensitive networking (TSN) networks. The design primarily incorporates field devices, data transmission, artificial intelligence (AI), and industrial control. An advanced binocular stereo visual recognition model based on deep learning is investigated to accurately obtain the world coordinates and types. A cooperative control scheduling model that combines a scheduling strategy with an anti-collision strategy is presented to effectively control multiple cranes for sorting tasks. The device data and commands are transmitted through industrial 5G-TSN integrated network for ultra-reliable, low-latency, and deterministic transmission. The proposed visual sorting system is further validated through the establishment of an experimental prototype, demonstrating its exceptional real-time performance while enabling flexible intelligent manufacturing.

5G delivers ultra-reliable low-latency communications and mobile edge computing (MEC) for industrial applications, enabling distributed computing resource collaboration and promoting traditional automation systems toward networked control paradigms. However, industrial networked automation systems face significant challenges in optimally allocating limited computing and communication resources to meet strict QoS requirements for massive control tasks while maintaining system stability and efficiency. Thus, we propose a computing-communication-control collaboration mechanism to enhance coordination between MEC and local controllers, enabling complex control tasks to be processed despite limited local computing resources. To jointly optimize hybrid control tasks migration with communication constraints and computing resources allocation with incomplete information, we design a dual double deep Q-network embedded with the Stackelberg game model. Simulation results demonstrate that the proposed mechanism achieves better performance compared with other benchmarks, and extremely decreases the convergence time compared with the classical Stackelberg game solution.

Green network aims to promote the sustainable development of communication systems, and base station (BS) and cells sleeping has been proven effective in reducing the power consumption of these systems. However, the current Reinforcement Learning (RL) based methods for multi-cells collaborative sleeping face significant challenges in real-world applications due to the complex users-to-cells connection relationships, and have been rarely researched in city-scale deployments. In this article, a robust RL-based multi-cells sleeping model called Graph Deep Deterministic Policy Gradient (GDDPG) is developed for handling highly complex communication scenarios. Besides, we first propose a framework for deploying multi-cells sleeping models at the city scale. Then two algorithms are put forward for determining the essential cells needed to maintain basic radio coverage and for effectively grouping these cells, which are two crucial works in the framework. Additionally, to address the temporal variation of traffic patterns, transfer learning is employed to fine-tune the pre-trained RL model periodically. Finally, we validate the feasibility of city-scale deployment algorithms and demonstrate the effectiveness of GDDPG by leveraging a computational platform and real-collected cells data from a telecom operator in China. Experimental results show that GDDPG effectively manages the sleeping states of up to 72 cells in a real-world environment. The experimental scenario is much more complex than those in other studies.

The demands for industrial ubiquitous communications promote the development of real-time and high-reliability wireless communication techniques. Accurate time synchronization is a critical foundation for deterministic communications. However, many wireless time synchronization methods achieve poor accuracy, while others take the high hardware costs and can not be used in practice. How to design high precision wireless time synchronization method with reasonable hardware costs is still a big challenge. Therefore, without affecting Wi-Fi protocol stack, a new medium access control (MAC) layer-based approach is proposed in this article to implement precision time synchronization with an open-source Wi-Fi design. The software protocol stack only needs to send handshake messages carrying identifiers, and timestamps are inserted and extracted from handshake messages as they pass through the MAC synchronization architecture designed in field programmable gate array. In the single-hop synchronization experiment, the synchronization accuracy is tested with and without network load. Comparing with other methods in several literatures, the results of the proposed solution unequivocally demonstrate the effectiveness and excellent wireless time synchronization precision, with 99% absolute time synchronization errors under 50% and 100% loads within 200 ns and 1$\mu$s, respectively.

IEEE 802.1 Time-Sensitive Networking (TSN) is an emerging and promising communication solution offering benefits for Industrial Internet. TSN can provide deterministic latency and ultra-reliability guarantee for automation control information in multi-traffics scenario. However, the routing and scheduling methods, which have important effects on system performance, are not covered by TSN standards. Therefore, in this paper, ajoint routing and scheduling model based on K-shortest-path (KSP) and swarm intelligence is proposed. The model effectually reduces end- to-end latency caused by link congestion and improves scheduling feasibility for multiple traffics in TSN domain. By contrast experiments under different circumstances, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are analyzed and compared. The simulation results indicate that the proposed model based on improved ACO achieves better performance on scheduling success rate and end-to-end latency guarantee, while PSO has higher scheduling efficiency in terms of optimization results and iterative convergence rate.

Abstract The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation.

In the 6G integrated air-ground network, the process of accomplishing complex tasks through the integrated multimodal communication faces challenges induced by unmanned aerial vehicles (UAVs), such as limited communication, storage and computing capabilities, and the existence of heterogeneous UAV multimodal information and carriers. Inspired by the process of semantic communication, we view successful execution of advanced UAV tasks as semantic recognition and pragmatic execution. Tiny machine learning (TinyML) provides the UAV advanced algorithms and models that can be run on the low-power and resource-constrained platforms. In this article, from the perspective of semantic communication and leveraging the applicability of TinyML for UAVs, we map the heterogeneous multimodal communication and UAV task execution processes aiming to better utilize the capabilities of machine learning and semantic communication to enhance the pragmatic task execution of UAVs. Multimodal virtual semantic communication can provide task-related auxiliary information, enabling the complementary integration of multiple independent modalities in the task domain. The proposed scheme and model achieve a deep integration of communication, sensation, and computation ultimately enhancing the practical task execution capability of UAVs.

Location-based service (LBS) is the core of internet of things (IoTs), which serves tracking, navigation and monitoring. The ubiquitous magnetic signals are temporally stable and spatially distinguishable, and can achieve high-precision and ubiquitous positioning results without additional infrastructure, which is favored by researchers and has become a major research hotspot. Although there has been extensive research in the field of indoor magnetic positioning, there is still room for optimization in terms of positioning accuracy and robustness. Aiming at the problem that the magnetometer is offset and susceptible to environmental interference, we propose an online magnetometer calibration algorithm without user perception. Aiming at the inconsistency of magnetic data spatial scale problem caused by differences in device sampling frequency and user walking speed, we leverage different scales to segment the magnetic data, extract the magnetic sequence features of the corresponding scales through Transformer, utilize the attention mechanism to score the weights of the different scale features, and finally fuse the multiple scale features for positioning. We conduct extensive and well-designed experiments on public datasets and self-collected datasets. The experimental results indicate that the proposed method effectively solves the magnetic spatial scale problem and improves indoor magnetic positioning accuracy.

Abstract Next‐generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks‐based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region‐based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region‐based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU‐DET and GC10‐DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted‐surface, rolled‐in scale and scratches on NEU‐DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively.

The use of cell-free massive multiple-input–multiple-output (MIMO) is regarded as a novel technique in the Industrial Internet of Things (IIoT) networks, and many studies have been reported on its cross-layer optimization, including random access and power allocation. Nevertheless, the cooperation of deep reinforcement learning (DRL) and cell-free massive lacks of deep study. In this article, a primal–dual deep deterministic policy gradient (DDPG) algorithm is designed to obtain cross-layer radio resource management, including power allocation in the physical layer and random access in the medium access layer. Different from the current studies, the random access and power allocation is formulated in cell-free massive MIMO IIoT networks, utilized by the stochastic ergodic optimization. In contrast to the stochastic policy gradient algorithm, a primal–dual DDPG algorithm is designed for the cross-layer optimization. Moreover, a multiagent primal–dual DDPG algorithm is proposed to different scenarios in the cell-free massive MIMO IIoT networks. Simulations are presented to verify the effectiveness of the primal–dual DDPG algorithm for random access and power allocation in the cell-free massive MIMO IIoT networks.

Affective services will become a research hotspot in artificial intelligence (AI) in the next decade. In this paper, a novel service paradigm combined with wireless communication in integrated satellite-terrestrial network (ISTN) is proposed. On this basis, an affective services computing offloading and transmission network (ASCTN) with a three-tier computation architecture is proposed, which is able to assist users to obtain affective computing services and regulate emotions. The optimization problem is investigated in the ASCTN, which is a discrete, non-linear, and non-convex problem with the limitation of computation ability of satellite and transmit power. Specifically, with the objective to minimize the cost utility related to latency and energy consumption, a joint affective services tasks computing offloading strategy, sub-channel, and power allocation algorithm based on dueling deep Q-network (Dueling-DQN) is proposed, which is in possession of better stability. The simulation results reveal the effectiveness of the optimization algorithm in terms of the cost utility in the ASCTN system.

To support massive connectivity for sporadically active devices is a challenging task, as the randomness of the channel and the large number of users lead to enormous increase of communication overhead. Different to the existing methods that differentiate users in resources including time, frequency and code, we propose a new joint activity detection and channel estimation framework for massive multiple-input multiple-output (MIMO) systems, where angular domain information of active users is exploited to enhance activity detection and channel estimation. By exploiting the sporadic activity of users and the angular spread of the wireless signals, the activity detection and channel estimation is formulated as a compressive sensing problem with multiple measurement vectors, which has a simultaneously row-sparse and clustered sparse structure. The sizes and positions of the nonzero clusters are arbitrary, which brings new challenges for algorithm derivation. To this end, we develop new algorithms based on sparse Bayesian learning, where novel hyper-priors are proposed to capture the structural signal characteristics, and appropriate approximations are employed to facilitate algorithm derivations. Numerical experiments demonstrate the improved activity detection and channel estimation performance of the proposed approach in comparison to the existing methods.

Supporting non-orthogonal multiple access (NOMA) in non-coherent joint transmission (NCJT) systems is beneficial for improving spectral efficiency (SE), but the interference coordination, user scheduling, and resource allocation problems in this new scenario have not been well studied. In this paper, a NOMA-enabled NCJT system is considered in which the connected users are jointly served by two multi-antenna transmitting-receiving points (TRPs) with non-ideal backhauls. Each user has two independent receiving (RX) chains that can work with orthogonal multiple access (OMA) or NOMA mode. A joint resource allocation and hybrid OMA/NOMA mode selection is proposed to maximize the throughput. The primal non-convex and NP-hard problem is decomposed into the following three subproblems, i.e., power allocation (PA) of a single TRP, hybrid mode selection (HMS) of a single TRP, and cross-TRP interference optimization (CIO). Firstly, a successive convex approximation (SCA) method is proposed to solve the non-convex PA subproblem, which achieves a local maximum solution. Secondly, the combinatorial HMS subproblem is transformed into finding the maximum matching of bipartite graphs. By constructing two weighted bipartite graphs for the OMA/near UEs and far UEs, a suboptimal solution is found. Thirdly, an alternating optimization is proposed to solve the CIO subproblem by iteratively performing PA and HMS of the two TRPs. Finally, simulation results demonstrate the superiority of throughput improvement of the proposed method, and the sum rate of the NOMA-enabled NCJT system can approach the sum rate of individual TRPs without interference.

In the group vertical handover (GVHO) scenario, many mobile terminals (MTs) send handover requests almost at the same time. The traditional vertical handover (VHO) schemes assume that the VHO user is coming one by one, so the current user knows the decision results of previous users, then the optimal result can be obtained. In GVHO scenario, multiple VHO decisions need to be made simultaneously, if the traditional VHO scheme was applied in this scenario, it may lead to system performance degradation or network congestion, because the decision-making MT does not know the results of other concurrent VHO users, so it may selfishly select the best networks just like in common VHO scenario. Therefore, three decision-making models for GVHO are proposed in this paper, and there performance comparisons are analyzed through numerical simulations.

A random access protocol, particularly suitable for OFDMA system, is proposed and analyzed in this paper. The protocol adopts a new dynamic RACH assignment algorithm and a new adaptive access probability scheme. Under the condition of light load, Base Station (BS) will adjust the number of RACHs to improve the channel utilization. Under the condition of heavy load, BS will take effective measure to guarantee QoS requirements of high priority traffics. In addition, the article introduces a statistical model to estimate system load, which is one of the features of the protocol. The simulation results fully indicate that the proposed random access protocol is efficient and reliable at conditions of both high and low load, and not only provides excellent transmission quality guarantee for higher priority traffics, but also supports lower priority traffics transmission efficiently in the integration traffic environment.