Secure Semantic Communication for Image Transmission in the Presence of Eavesdroppers

Semantic communication (SemCom) has emerged as a key technology for the forthcoming sixth-generation (6G) network, attributed to its enhanced communication efficiency and robustness against channel noise. However, the open nature of wireless channels renders them vulnerable to eavesdropping, posing a serious threat to privacy. To address this issue, we propose a novel secure semantic communication (SemCom) approach for image transmission, which integrates steganography technology to conceal private information within non-private images (host images). Specifically, we propose an invertible neural network (INN)-based signal steganography approach, which embeds channel input signals of a private image into those of a host image before transmission. This ensures that the original private image can be reconstructed from the received signals at the legitimate receiver, while the eavesdropper can only decode the information of the host image. Simulation results demonstrate that the proposed approach maintains comparable reconstruction quality of both host and private images at the legitimate receiver, compared to scenarios without any secure mechanisms. Experiments also show that the eavesdropper is only able to reconstruct host images, showcasing the enhanced security provided by our approach.

Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation

Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are shown to be able to learn the structural information about graphs representing the wireless networks to adapt to the time-varying channel state information and dynamics of network topology. This article aims to provide a comprehensive overview of applying GNNs to optimize wireless networks via answering three fundamental questions, i.e., how to input the wireless network data into GNNs, how to improve the performance of GNNs, and how to evaluate GNNs. Particularly, two graph representations are given to transform wireless network parameters into graph-structured data. Then, we focus on the architecture design of the GNN-based models via introducing the basic message passing as well as model improvement methods including multi-head attention mechanism and residual structure. At last, we give task-oriented evaluation metrics for DL-enabled wireless resource allocation. We also highlight certain challenges and potential research directions for the application of GNNs in wireless networks.

AI-Empowered RIS-Assisted Networks: CV-Enabled RIS Selection and DNN-Enabled Transmission

This paper investigates artificial intelligence (AI) empowered schemes for reconfigurable intelligent surface (RIS) assisted networks from the perspective of fast implementation. We formulate a weighted sum-rate maximization problem for a multi-RIS-assisted network. To avoid huge channel estimation overhead due to activate all RISs, we propose a computer vision (CV) enabled RIS selection scheme based on a single shot multi-box detector. To realize real-time resource allocation, a deep neural network (DNN) enabled transmit design is developed to learn the optimal mapping from channel information to transmit beamformers and phase shift matrix. Numerical results illustrate that the CV module is able to select of RIS with the best propagation condition. The well-trained DNN achieves similar sum-rate performance to the existing alternative optimization method but with much smaller inference time.

Generative AI for Advanced UAV Networking

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions.

Interactive Generative AI Agents for Satellite Networks through a Mixture of Experts Transmission

In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by the complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregates them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated.

Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision

Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable. In this paper, we study generative artificial intelligence (AI) agent-enabled next-generation MIMO design. Firstly, we provide an overview of the development, fundamentals, and challenges of the next-generation MIMO. Then, we propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents with the aid of large language model (LLM) and retrieval augmented generation (RAG). Next, we comprehensively discuss the features and advantages of the generative AI agent framework. More importantly, to tackle existing challenges of next-generation MIMO, we discuss generative AI agent-enabled next-generation MIMO design, from the perspective of performance analysis, signal processing, and resource allocation. Furthermore, we present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis in complex configuration scenarios. These examples highlight how the integration of generative AI agents can significantly enhance the analysis and design of next-generation MIMO systems. Finally, we discuss important potential research future directions.

UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning

In this paper, we investigate an unmanned aerial vehicle (UAV)-assistant air-to-ground communication system, where multiple UAVs form a UAV-enabled virtual antenna array (UVAA) to communicate with remote base stations by utilizing collaborative beamforming. To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to simultaneously maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs by optimizing the positions and excitation current weights of all UAVs. This problem is challenging because these two optimization objectives conflict with each other, and they are non-concave to the optimization variables. Moreover, the system is dynamic, and the cooperation among UAVs is complex, making traditional methods take much time to compute the optimization solution for a single task. In addition, as the task changes, the previously obtained solution will become obsolete and invalid. To handle these issues, we leverage the multi-agent deep reinforcement learning (MADRL) to address the UCBMOP. Specifically, we use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB, where three techniques are introduced to enhance the performance. Simulation results demonstrate that the proposed algorithm can learn a better strategy compared with other methods. Moreover, extensive experiments also demonstrate the effectiveness of the proposed techniques.

Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning

In this paper, we propose a distributed collaborative beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. Specifically, DCB treats the terminals that are unable to establish efficient direct connections with the low Earth orbit (LEO) satellites as distributed antennas, forming a virtual antenna array to enhance the terminal-to-satellite uplink achievable rates and durations. However, such systems need multiple trade-off policies that variously balance the terminal-satellite uplink achievable rate, energy consumption of terminals, and satellite switching frequency to satisfy the scenario requirement changes. Thus, we perform a multi-objective optimization analysis and formulate a long-term optimization problem. To address availability in different terminal cluster scales, we reformulate this problem into an action space-reduced and universal multi-objective Markov decision process. Then, we propose an evolutionary multi-objective deep reinforcement learning algorithm to obtain the desirable policies, in which the low-value actions are masked to speed up the training process. As such, the applicability of a one-time trained model can cover more changing terminal-satellite uplink scenarios. Simulation results show that the proposed algorithm outmatches various baselines, and draw some useful insights. Specifically, it is found that DCB enables terminals that cannot reach the uplink achievable threshold to achieve efficient direct uplink transmission, which thus reveals that DCB is an effective solution for enabling direct ground-space communications. Moreover, it reveals that the proposed algorithm achieves multiple policies favoring different objectives and achieving near-optimal uplink achievable rates with low switching frequency.

Two-Way Aerial Secure Communications via Distributed Collaborative Beamforming under Eavesdropper Collusion

Unmanned aerial vehicles (UAVs)-enabled aerial communication provides a flexible, reliable, and cost-effective solution for a range of wireless applications. However, due to the high line-of-sight (LoS) probability, aerial communications between UAVs are vulnerable to eavesdropping attacks, particularly when multiple eavesdroppers collude. In this work, we aim to introduce distributed collaborative beamforming (DCB) into UAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two UAV swarms and construct these swarms as two UAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and the maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of UAVs for constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives as a multi-objective optimization problem. Following this, we propose an enhanced multi-objective swarm intelligence algorithm via the characterized properties of the problem. Simulation results show that our proposed algorithm can obtain a set of informative solutions and outperform other state-of-the-art baseline algorithms. Experimental tests demonstrate that our method can be deployed in limited computing power platforms of UAVs and is beneficial for saving computational resources.

BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling

Battery recycling is a critical process for minimizing environmental harm and resource waste for used batteries. However, it is challenging, largely because sorting batteries is costly and hardly automated to group batteries based on battery types. In this paper, we introduce a machine learning-based approach for battery-type classification and address the daunting problem of data scarcity for the application. We propose BatSort which applies transfer learning to utilize the existing knowledge optimized with large-scale datasets and customizes ResNet to be specialized for classifying battery types. We collected our in-house battery-type dataset of small-scale to guide the knowledge transfer as a case study and evaluate the system performance. We conducted an experimental study and the results show that BatSort can achieve outstanding accuracy of 92.1% on average and up to 96.2% and the performance is stable for battery-type classification. Our solution helps realize fast and automated battery sorting with minimized cost and can be transferred to related industry applications with insufficient data.