Boya Di

To meet stringent requirements of high throughput in the 6G era, semantic communication (SC), a revolutionary framework that transmits semantic information rather than the original data, is expected to be a promising solution. However, existing SC frameworks struggle to accommodate data with new semantic features effectively and ensure minimal data processing latency. This is because achieving strong adaptability necessitates large-scale foundation models, such as large language models with robust semantic understanding, whereas low latency demands small models with simple structures. To address this issue, we propose a feature-foundation model evolution framework toward large model-empowered low-latency SC. In this framework, small-scale feature models and large-scale foundation models are deployed at edge nodes and cloud servers, respectively. The feature models perform real-time semantic encoding and decoding, and they evolve with the aid of foundation models when data with new semantic features occur, thus assuring low latency and high generalization ability simultaneously. We detail the architecture of the proposed collaborative framework and the related evolution protocol. Moreover, a case study on image transmission is presented to verify the effectiveness of the proposed framework. Furthermore, we summarize and discuss several future research directions based on the collaborative framework.

Cloud-edge collaborative manufacturing systems have drawn considerable attention by offloading data processing tasks to edge servers to support latency-sensitive tasks and high manufacturing efficiency. In these systems, data can be accessed by external devices and the access connections are exposed on the Internet, leading to a risk of data leakage or interception. In this paper, to address such data security issues, we introduce the zero trust architecture (ZTA) where every user, whether internal or external, is required to be authorized and verified before accessing any resource, thereby ensuring data security. However, existing ZTA implementations primarily leverage security technologies that rely on either biometrics and human behavior or sparse device attributes, rendering them inapplicable in cloud-edge collaborative manufacturing systems. To deal with this challenge, we propose a cloud-edge collaborative ZTA (CE-ZTA), in which ciphertext-policy attribute-based encryption (CP-ABE) is employed to implement ZTA principles. We propose a continuously-verifying CP-ABE scheme, in which an attribute merging algorithm is designed to avoid the exponential growth in computational latency and overhead caused by continuous verification of CE-ZTA. Theoretical analysis and simulation results show that the proposed scheme complies with ZTA principles and is superior to existing CP-ABE schemes in terms of latency, overhead, and robustness.

The fourth industrial revolution drives AI-powered smart manufacturing through cloud-edge computing, enabling intelligent production processes and data-driven automation. To handle security concerns arising from massive IoT deployments, attribute-based access control (ABAC) has become essential for smart factories. It offers flexibility in dynamic environments by utilizing attributes of users, devices, and contextual conditions to decide whether an access request should be permitted or denied. However, the proliferation of IoT devices drastically increases the number of attributes, causing exponential growth in policy complexity and severe decision latency at resource-constrained edge nodes. To address this issue, we propose PRUNE, a cloud–edge collaborative framework for ABAC policy pruning. The framework adaptively determines pruning strategies based on the real-time security state of the factory. Specifically, it employs deep reinforcement learning (DRL) for coarse-grained control in highly dynamic environments, while switching to a Deterministic Policy Optimizer (DPO) for fine-grained adjustment under quasi-static conditions. The pruned lightweight ABAC policy subset is then deployed on edge nodes for real-time access decisions. By continuously monitoring factory conditions and analyzing historical access requests, PRUNE dynamically adjusts pruning strategies. Simulation results on our containerized digital-twin testbed show that PRUNE reduces security response latency by 22% and improves operational efficiency by 30%, while maintaining robust security with anomaly rates consistently below 10%.

In response to the increasing demand for high-accuracy localization with low power consumption, reconfigurable holographic surfaces (RHSs) have been proposed as a potential large-scale phased-MIMO antenna solution. In this paper, we consider an RHS-enabled mmWave localization system where multiple targets are distributed in the hybrid near-field (NF) and far-field (FF) regions of two co-located RHSs. Unlike conventional methods relying on either multiple time slots or power-consuming phase shifters, we propose a single-shot approach for such an RHS-enabled power-efficient phased-MIMO architecture to localize hybrid-field1 targets within a single frequency-modulated continuous wave (FMCW) chirp measurement. Specifically, we construct an optimized measurement matrix for compressed sensing by designing RHS beamformers, encoding multi-target angular information into a single wide-coverage waveform transmission. The received signals are then processed to estimate the ranges and angles of hybrid-field targets, guided by an efficient NF-FF boundary developed to identify NF and FF regions. Simulation results show that the proposed RHS-enabled localization scheme with a reduced number of RF chains outperforms the traditional MIMO-based single-shot scheme in terms of estimation accuracy at low SNR, while approaching the MIMO case at high SNR. Moreover, it avoids beam scanning and lowers power consumption compared with conventional phased-MIMO systems.

In response to the escalating communication demand, transceivers are transforming from a data-oriented to an artificial intelligence (AI)-driven semantic-aware paradigm. However, current semantic-aware transceivers fail to simultaneously adapt to unseen data without labels and guarantee low data processing latency because strong generalization ability requires large-scale models with robust semantic understanding, while low latency leads to small model size and simple structure. To this end, we propose a feature-foundation model evolution framework deployed at cloud, edge, and users, where models with different scales can cooperate to address these issues. Specifically, the transmitter at edge sends images to the users by performing real-time semantic feature extraction and data encoding, and the small feature model is evolved with the aid of a large foundation model at cloud when unseen data occurs. To simultaneously update the feature model and avoid loss of previously acquired semantic understanding, we design a feature-foundation model evolution scheme where outputs of both foundation and feature models are leveraged. Additionally, to tackle coupled communication-computation resources for evolution, we formulate the resource scheduling problem and design algorithms to minimize the evolution latency. We conduct rigorously-designed simulation to validate the effectiveness of our framework.

To overcome the challenges of ultra-low latency, ubiquitous coverage, and soaring data rates, this article presents a combined use of Near Field Communication (NFC) and Reconfigurable Holographic Surfaces (RHS) for Non-Terrestrial Networks (NTN). A system architecture has been presented, which shows that the integration of RHS with NTN platforms such as satellites, High Altitude Platform Stations (HAPS), and Uncrewed Aerial Vehicles (UAV) can achieve precise beamforming and intelligent wavefront control in near-field regions, enhancing Energy Efficiency (EE), spectral utilization, and spatial resolution. Moreover, key applications, challenges, and future directions have been identified to fully adopt this integration. In addition, a use case analysis has been presented to improve the EE of the system in a public safety use case scenario, further strengthening the UAV-RHS fusion.

Integrated sensing and communication (ISAC) can perform both communication and sensing tasks using the same frequency band and hardware, making it a key technology for 6G. As a low-cost implementation for large-scale antenna arrays, reconfigurable holographic surfaces (RHSs) can be integrated into ISAC systems to realize the holographic ISAC paradigm, where enlarged radiation apertures achieve significant beamforming gains. In this paper, we investigate the tri-hybrid holographic ISAC framework, where the beamformer comprises digital, analog, and RHS-based electromagnetic (EM) layers. The analog layer employs a small number of phase shifters (PSs) to provide subarray-level phase control for the amplitude-modulated RHSs. Tri-hybrid beamforming provides a pathway for low-cost large-scale holographic ISAC. However, compared to conventional ISAC systems, it is challenging to achieve joint subarray-level phase control via PSs and element-level radiation amplitude control via RHSs for holographic ISAC. To address this, we present a tri-hybrid holographic ISAC scheme that minimizes sensing waveform error while satisfying the minimum user rate requirement. A joint optimization approach for PS phases and RHS amplitude responses is designed to address inter-layer coupling and distinct feasible regions. Theoretical analyses reveal that the optimized amplitude responses cluster near boundary values, i.e., 1-bit amplitude control, to reduce hardware and algorithmic complexity. Simulation results show that the proposed scheme achieves a controllable performance trade-off between communication and sensing tasks. Measured RHS beam gain validates the enhancement of holographic beamforming through subarray-level phase shifting. Moreover, as the number of RHS elements increases, the proposed approach exceeds the performance of conventional hybrid beamforming while significantly reducing the number of PSs.

Radio simultaneously localization and mapping (SLAM) is indispensable for a wide range of wireless applications owing to its ability to provide both location and mapping information. Traditional radio SLAM systems use fixed-aperture antennas with invariant beamwidths, which cannot adapt to the varying requirements for beam gain and coverage in complex environments. In this paper, we propose an aperture-changeable reconfigurable holographic surface (RHS)-assisted SLAM system. By deactivating different numbers of RHS elements, the equivalent antenna aperture is changeable, leading to adaptive beamwidths across detection directions. However, the RHS should follow the leakage power constraint, which stipulates that the total radiated power of RHS elements cannot exceed the input power. Its impact on the elements’ radiated power makes the optimization of RHS equivalent aperture challenging. To address this issue, we formulate a detection probability maximization problem and then adopt a two-step decomposition method to solve it. In the first step, we relax the leakage power constraint to obtain a relaxed solution. In the second step, we refine the relaxed solution by incorporating the leakage power constraint. We analyze the impact of RHS equivalent aperture and leakage power constraint on the SLAM system and evaluate the complexity of the proposed algorithm. Simulation results demonstrate that the proposed SLAM system can effectively reduce agent localization error compared with benchmark schemes.