Energy Efficient Trajectory Control and Resource Allocation in Multi-UAV-assisted MEC via Deep Reinforcement Learning

Mobile edge computing (MEC) is a promising technique to improve the computational capacity of smart devices (SDs) in Internet of Things (IoT). However, the performance of MEC is restricted due to its fixed location and limited service scope. Hence, we investigate an unmanned aerial vehicle (UAV)-assisted MEC system, where multiple UAVs are dispatched and each UAV can simultaneously provide computing service for multiple SDs. To improve the performance of system, we formulated a UAV-based trajectory control and resource allocation multi-objective optimization problem (TCRAMOP) to simultaneously maximize the offloading number of UAVs and minimize total offloading delay and total energy consumption of UAVs by optimizing the flight paths of UAVs as well as the computing resource allocated to served SDs. Then, consider that the solution of TCRAMOP requires continuous decision-making and the system is dynamic, we propose an enhanced deep reinforcement learning (DRL) algorithm, namely, distributed proximal policy optimization with imitation learning (DPPOIL). This algorithm incorporates the generative adversarial imitation learning technique to improve the policy performance. Simulation results demonstrate the effectiveness of our proposed DPPOIL and prove that the learned strategy of DPPOIL is better compared with other baseline methods.

LLM Meets the Sky: Heuristic Multi-Agent Reinforcement Learning for Secure Heterogeneous UAV Networks

This work tackles the physical layer security (PLS) problem of maximizing the secrecy rate in heterogeneous UAV networks (HetUAVNs) under propulsion energy constraints. Unlike prior studies that assume uniform UAV capabilities or overlook energy-security trade-offs, we consider a realistic scenario where UAVs with diverse payloads and computation resources collaborate to serve ground terminals in the presence of eavesdroppers. To manage the complex coupling between UAV motion and communication, we propose a hierarchical optimization framework. The inner layer uses a semidefinite relaxation (SDR)-based S2DC algorithm combining penalty functions and difference-of-convex (d.c.) programming to solve the secrecy precoding problem with fixed UAV positions. The outer layer introduces a Large Language Model (LLM)-guided heuristic multi-agent reinforcement learning approach (LLM-HeMARL) for trajectory optimization. LLM-HeMARL efficiently incorporates expert heuristics policy generated by the LLM, enabling UAVs to learn energy-aware, security-driven trajectories without the inference overhead of real-time LLM calls. The simulation results show that our method outperforms existing baselines in secrecy rate and energy efficiency, with consistent robustness across varying UAV swarm sizes and random seeds.

Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art

With the rapid development of Generative Artificial Intelligence (GAI) technology, Generative Diffusion Models (GDMs) have shown significant empowerment potential in the field of wireless networks due to advantages, such as noise resistance, training stability, controllability, and multimodal generation. Although there have been multiple studies focusing on GDMs for wireless networks, there is still a lack of comprehensive reviews on their technological evolution. Motivated by this, we systematically explore the application of GDMs in wireless networks. Firstly, starting from mathematical principles, we analyze technical advantages of GDMs and present six representative models. Furthermore, we propose the multi-layer wireless network architecture including sensing layer, transmission layer, application layer, and security plane. We also introduce the core mechanisms of GDM at each of the layers. Subsequently, we conduct a rigorous review on existing GDM-based schemes, with a focus on analyzing their innovative points, the role of GDMs, strengths, and weaknesses. Ultimately, we extract key challenges and provide potential solutions, with the aim of providing directional guidance for future research in this field.

Scene Graph-Aided Probabilistic Semantic Communication for Image Transmission

Semantic communication emphasizes the transmission of meaning rather than raw symbols. It offers a promising solution to alleviate network congestion and improve transmission efficiency. In this paper, we propose a wireless image communication framework that employs probability graphs as shared semantic knowledge base among distributed users. High-level image semantics are represented via scene graphs, and a two-stage compression algorithm is devised to remove predictable components based on learned conditional and co-occurrence probabilities. At the transmitter, the algorithm filters redundant relations and entity pairs, while at the receiver, semantic recovery leverages the same probability graphs to reconstruct omitted information. For further research, we also put forward a multi-round semantic compression algorithm with its theoretical performance analysis. Simulation results demonstrate that our semantic-aware scheme achieves superior transmission throughput and satiable semantic alignment, validating the efficacy of leveraging high-level semantics for image communication.

Predictive Control over LAWN: Joint Trajectory Design and Resource Allocation

Low-altitude wireless networks (LAWNs) have been envisioned as flexible and transformative platforms for enabling delay-sensitive control applications in Internet of Things (IoT) systems. In this work, we investigate the real-time wireless control over a LAWN system, where an aerial drone is employed to serve multiple mobile automated guided vehicles (AGVs) via finite blocklength (FBL) transmission. Toward this end, we adopt the model predictive control (MPC) to ensure accurate trajectory tracking, while we analyze the communication reliability using the outage probability. Subsequently, we formulate an optimization problem to jointly determine control policy, transmit power allocation, and drone trajectory by accounting for the maximum travel distance and control input constraints. To address the resultant non-convex optimization problem, we first derive the closed-form expression of the outage probability under FBL transmission. Based on this, we reformulate the original problem as a quadratic programming (QP) problem, followed by developing an alternating optimization (AO) framework. Specifically, we employ the projected gradient descent (PGD) method and the successive convex approximation (SCA) technique to achieve computationally efficient sub-optimal solutions. Furthermore, we thoroughly analyze the convergence and computational complexity of the proposed algorithm. Extensive simulations and AirSim-based experiments are conducted to validate the superiority of our proposed approach compared to the baseline schemes in terms of control performance.

Token Communication in the Era of Large Models: An Information Bottleneck-Based Approach

This letter proposes UniToCom, a unified token communication paradigm that treats tokens as the fundamental units for both processing and wireless transmission. Specifically, to enable efficient token representations, we propose a generative information bottleneck (GenIB) principle, which facilitates the learning of tokens that preserve essential information while supporting reliable generation across multiple modalities. By doing this, GenIB-based tokenization is conducive to improving the communication efficiency and reducing computational complexity. Additionally, we develop $\sigma$-GenIB to address the challenges of variance collapse in autoregressive modeling, maintaining representational diversity and stability. Moreover, we employ a causal Transformer-based multimodal large language model (MLLM) at the receiver to unify the processing of both discrete and continuous tokens under the next-token prediction paradigm. Simulation results validate the effectiveness and superiority of the proposed UniToCom compared to baselines under dynamic channel conditions. By integrating token processing with MLLMs, UniToCom enables scalable and generalizable communication in favor of multimodal understanding and generation, providing a potential solution for next-generation intelligent communications.

From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks

In this article, we introduce a novel low-altitude wireless network (LAWN), which is a reconfigurable, three-dimensional (3D) layered architecture. In particular, the LAWN integrates connectivity, sensing, control, and computing across aerial and terrestrial nodes that enable seamless operation in complex, dynamic, and mission-critical environments. In this article, we introduce a novel low-altitude wireless network (LAWN), which is a reconfigurable, three-dimensional (3D) layered architecture. Different from the conventional aerial communication systems, LAWN's distinctive feature is its tight integration of functional planes in which multiple functionalities continually reshape themselves to operate safely and efficiently in the low-altitude sky. With the LAWN, we discuss several enabling technologies, such as integrated sensing and communication (ISAC), semantic communication, and fully-actuated control systems. Finally, we identify potential applications and key cross-layer challenges. This article offers a comprehensive roadmap for future research and development in the low-altitude airspace.

Stacked Intelligent Metasurfaces for Multi-Modal Semantic Communications

Semantic communication (SemCom) powered by generative artificial intelligence enables highly efficient and reliable information transmission. However, it still necessitates the transmission of substantial amounts of data when dealing with complex scene information. In contrast, the stacked intelligent metasurface (SIM), leveraging wave-domain computing, provides a cost-effective solution for directly imaging complex scenes. Building on this concept, we propose an innovative SIM-aided multi-modal SemCom system. Specifically, an SIM is positioned in front of the transmit antenna for transmitting visual semantic information of complex scenes via imaging on the uniform planar array at the receiver. Furthermore, the simple scene description that contains textual semantic information is transmitted via amplitude-phase modulation over electromagnetic waves. To simultaneously transmit multi-modal information, we optimize the amplitude and phase of meta-atoms in the SIM using a customized gradient descent algorithm. The optimization aims to gradually minimize the mean squared error between the normalized energy distribution on the receiver array and the desired pattern corresponding to the visual semantic information. By combining the textual and visual semantic information, a conditional generative adversarial network is used to recover the complex scene accurately. Extensive numerical results verify the effectiveness of the proposed multi-modal SemCom system in reducing bandwidth overhead as well as the capability of the SIM for imaging the complex scene.

Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Application

In this paper, a novel semantic communication framework empowered by generative artificial intelligence (GAI) is proposed, specifically leveraging the capabilities of diffusion models (DMs). A rigorous theoretical foundation is established based on stochastic differential equations (SDEs), which elucidates the denoising properties of DMs in mitigating additive white Gaussian noise (AWGN) in latent semantic representations. Crucially, a closed-form analytical relationship between the signal-to-noise ratio (SNR) and the denoising timestep is derived, enabling the optimal selection of diffusion parameters for any given channel condition. To address the distribution mismatch between the received signal and the DM's training data, a mathematically principled scaling mechanism is introduced, ensuring robust performance across a wide range of SNRs without requiring model fine-tuning. Built upon this theoretical insight, we develop a latent diffusion model (LDM)-based semantic transceiver, wherein a variational autoencoder (VAE) is employed for efficient semantic compression, and a pretrained DM serves as a universal denoiser. Notably, the proposed architecture is fully training-free at inference time, offering high modularity and compatibility with large-scale pretrained LDMs. This design inherently supports zero-shot generalization and mitigates the challenges posed by out-of-distribution inputs. Extensive experimental evaluations demonstrate that the proposed framework significantly outperforms conventional neural-network-based semantic communication baselines, particularly under low SNR conditions and distributional shifts, thereby establishing a promising direction for GAI-driven robust semantic transmission in future 6G systems.

Multi-Waveguide Pinching Antennas for ISAC

Recently, a novel flexible-antenna technology, called pinching antennas, has attracted growing academic interest. By inserting discrete dielectric materials, pinching antennas can be activated at arbitrary points along waveguides, allowing for flexible customization of large-scale path loss. This paper investigates a multi-waveguide pinching-antenna integrated sensing and communications (ISAC) system, where transmit pinching antennas (TPAs) and receive pinching antennas (RPAs) coordinate to simultaneously detect one potential target and serve one downlink user. We formulate a communication rate maximization problem subject to radar signal-to-noise ratio (SNR) requirement, transmit power budget, and the allowable movement region of the TPAs, by jointly optimizing TPA locations and transmit beamforming design. To address the non-convexity of the problem, we propose a novel fine-tuning approximation method to reformulate it into a tractable form, followed by a successive convex approximation (SCA)-based algorithm to obtain the solution efficiently. Extensive simulations validate both the system design and the proposed algorithm. Results show that the proposed method achieves near-optimal performance compared with the computational-intensive exhaustive search-based benchmark, and pinching-antenna ISAC systems exhibit a distinct communication-sensing trade-off compared with conventional systems.