UAV-Enabled Secure ISAC Against Dual Eavesdropping Threats: Joint Beamforming and Trajectory Design

In this work, we study an unmanned aerial vehicle (UAV)-enabled secure integrated sensing and communication (ISAC) system, where a UAV serves as an aerial base station (BS) to simultaneously perform communication with a user and detect a target on the ground, while a dual-functional eavesdropper attempts to intercept the signals for both sensing and communication. Facing the dual eavesdropping threats, we aim to enhance the average achievable secrecy rate for the communication user by jointly designing the UAV trajectory together with the transmit information and sensing beamforming, while satisfying the requirements on sensing performance and sensing security, as well as the UAV power and flight constraints. To address the non-convex nature of the optimization problem, we employ the alternating optimization (AO) strategy, jointly with the successive convex approximation (SCA) and semidefinite relaxation (SDR) methods. Numerical results validate the proposed approach, demonstrating its ability to achieve a high secrecy rate while meeting the required sensing and security constraints.

Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks

Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate. However, the technical bottlenecks of traditional methods pose significant challenges to cross-layer optimization. In this paper, joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning to maximize the weighted sum rate. Specifically, we convert the underlying problem into a joint multi-task optimization problem and then propose a centralized multi-task self-supervised learning algorithm to solve the problem so as to avoid costly manual labeling. Therein, two novel and general loss functions, i.e., negative fraction linear loss and exponential linear loss whose advantages in robustness and target domain have been proved and discussed, are designed to enable self-supervised learning. Moreover, we further design a MEC-enabled distributed multi-task self-supervised learning (DMTSSL) algorithm, with low complexity and high scalability to address the challenge of dimensional disaster. Finally, we develop the distance-aware transfer learning algorithm based on the DMTSSL algorithm to handle the dynamic scenario with negligible computation cost. Simulation results under $3$rd generation partnership project 38.901 urban-macrocell scenario demonstrate the superiority of the proposed algorithms over the baseline algorithms.

Uncertainty-Aware Hybrid Inference with On-Device Small and Remote Large Language Models

This paper studies a hybrid language model (HLM) architecture that integrates a small language model (SLM) operating on a mobile device with a large language model (LLM) hosted at the base station (BS) of a wireless network. The HLM token generation process follows the speculative inference principle: the SLM's vocabulary distribution is uploaded to the LLM, which either accepts or rejects it, with rejected tokens being resampled by the LLM. While this approach ensures alignment between the vocabulary distributions of the SLM and LLM, it suffers from low token throughput due to uplink transmission and the computation costs of running both language models. To address this, we propose a novel HLM structure coined Uncertainty-aware HLM (U-HLM), wherein the SLM locally measures its output uncertainty, and skips both uplink transmissions and LLM operations for tokens that are likely to be accepted. This opportunistic skipping is enabled by our empirical finding of a linear correlation between the SLM's uncertainty and the LLM's rejection probability. We analytically derive the uncertainty threshold and evaluate its expected risk of rejection. Simulations show that U-HLM reduces uplink transmissions and LLM computation by 45.93%, while achieving up to 97.54% of the LLM's inference accuracy and 2.54$\times$ faster token throughput than HLM without skipping.

Capacity Analysis on OAM-Based Wireless Communications: An Electromagnetic Information Theory Perspective

Orbital angular momentum (OAM) technology enhances the spectrum and energy efficiency of wireless communications by enabling multiplexing over different OAM modes. However, classical information theory, which relies on scalar models and far-field approximations, cannot fully capture the unique characteristics of OAM-based systems, such as their complex electromagnetic field distributions and near-field behaviors. To address these limitations, this paper analyzes OAM-based wireless communications from an electromagnetic information theory (EIT) perspective, integrating electromagnetic theory with classical information theory. EIT accounts for the physical properties of electromagnetic waves, offering advantages such as improved signal manipulation and better performance in real-world conditions. Given these benefits, EIT is more suitable for analyzing OAM-based wireless communication systems. Presenting a typical OAM model utilizing uniform circular arrays (UCAs), this paper derives the channel capacity based on the induced electric fields by using Green's function. Numerical and simulation results validate the channel capacity enhancement via exploration under EIT framework. Additionally, this paper evaluates the impact of various parameters on the channel capacity. These findings provide new insights for understanding and optimizing OAM-based wireless communications systems.

Non-Terrestrial Networking for 6G: Evolution, Opportunities, and Future Directions

From 5G onwards, Non-Terrestrial Networks (NTNs) have emerged as a key component of future network architectures. Leveraging Low Earth Orbit (LEO) satellite constellations, NTNs are capable of building a space Internet and present a paradigm shift in delivering mobile services to even the most remote regions on Earth. However, the extensive coverage and rapid movement of LEO satellites pose unique challenges for NTN networking, including user equipment (UE) access and inter-satellite delivery, which directly impact the quality of service (QoS) and data transmission continuity. This paper offers an in-depth review of advanced NTN management technologies in the context of 6G evolution, focusing on radio resource management, mobility management, and dynamic network slicing. Building on this foundation and considering the latest trends in NTN development, we then present some innovative perspectives to emerging challenges in satellite beamforming, handover mechanisms, and inter-satellite transmissions. Lastly, we identify open research issues and propose future directions aimed at advancing satellite Internet deployment and enhancing NTN performance.

Decentralized Semantic Communication and Cooperative Tracking Control for a UAV Swarm over Wireless MIMO Fading Channels

This paper investigates the semantic communication and cooperative tracking control for an UAV swarm comprising a leader UAV and a group of follower UAVs, all interconnected via unreliable wireless multiple-input-multiple-output (MIMO) channels. Initially, we develop a dynamic model for the UAV swarm that accounts for both the internal interactions among the cooperative follower UAVs and the imperfections inherent in the MIMO channels that interlink the leader and follower UAVs. Building on this model, we incorporate the power costs of the UAVs and formulate the communication and cooperative tracking control challenge as a drift-plus-penalty optimization problem. We then derive a closed-form optimal solution that maintains a decentralized semantic architecture, dynamically adjusting to the tracking error costs and local channel conditions within the swarm. Employing Lyapunov drift analysis, we establish closed-form sufficient conditions for the stabilization of the UAV swarm's tracking performance. Numerical results demonstrate the significant enhancements in our proposed scheme over various state-of-the-art methods.

Deep Learning-Based Decoding of Linear Block Codes for Spin-Torque Transfer Magnetic Random Access Memory (STT-MRAM)

Thanks to its superior features of fast read/write speed and low power consumption, spin-torque transfer magnetic random access memory (STT-MRAM) has become a promising non-volatile memory (NVM) technology that is suitable for many applications. However, the reliability of STT-MRAM is seriously affected by the variation of the memory fabrication process and the working temperature, and the later will lead to an unknown offset of the channel. Hence, there is a pressing need to develop more effective error correction coding techniques to tackle these imperfections and improve the reliability of STT-MRAM. In this work, we propose, for the first time, the application of deep-learning (DL) based algorithms and techniques to improve the decoding performance of linear block codes with short codeword lengths for STT-MRAM. We formulate the belief propagation (BP) decoding of linear block code as a neural network (NN), and propose a novel neural normalized-offset reliability-based min-sum (NNORB-MS) decoding algorithm. We successfully apply our proposed decoding algorithm to the STT-MRAM channel through channel symmetrization to overcome the channel asymmetry. We also propose an NN-based soft information generation method (SIGM) to take into account the unknown offset of the channel. Simulation results demonstrate that our proposed NNORB-MS decoding algorithm can achieve significant performance gain over both the hard-decision decoding (HDD) and the regular reliability-based min-sum (RB-MS) decoding algorithm, for cases without and with the unknown channel offset. Moreover, the decoder structure and time complexity of the NNORB-MS algorithm remain similar to those of the regular RB-MS algorithm.

Distributed Channel Estimation for 6D Movable Antenna: Unveiling Directional Sparsity

Six-dimensional movable antenna (6DMA) is an innovative technology to improve wireless network capacity by adjusting 3D positions and 3D rotations of antenna surfaces based on channel spatial distribution. However, the existing works on 6DMA have assumed a central processing unit (CPU) to jointly process the signals of all 6DMA surfaces to execute various tasks. This inevitably incurs prohibitively high processing cost for channel estimation. Therefore, we propose a distributed 6DMA processing architecture to reduce processing complexity of CPU by equipping each 6DMA surface with a local processing unit (LPU). In particular, we unveil for the first time a new \textbf{\textit{directional sparsity}} property of 6DMA channels, where each user has significant channel gains only for a (small) subset of 6DMA position-rotation pairs, which can receive direct/reflected signals from users. In addition, we propose a practical three-stage protocol for the 6DMA-equipped base station (BS) to conduct statistical CSI acquisition for all 6DMA candidate positions/rotations, 6DMA position/rotation optimization, and instantaneous channel estimation for user data transmission with optimized 6DMA positions/rotations. Specifically, the directional sparsity is leveraged to develop distributed algorithms for joint sparsity detection and channel power estimation, as well as for directional sparsity-aided instantaneous channel estimation. Using the estimated channel power, we develop a channel power-based optimization algorithm to maximize the ergodic sum rate of the users by optimizing the antenna positions/rotations. Simulation results show that our channel estimation algorithms are more accurate than benchmarks with lower pilot overhead, and our optimization outperforms fluid/movable antennas optimized only in two dimensions (2D), even when the latter have perfect instantaneous CSI.

A New Channel Model for OAM Wireless Communication at 5.8 and 28 GHz

Orbital angular momentum (OAM) in electromagnetic (EM) waves can significantly enhance spectrum efficiency in wireless communications without requiring additional power, time, or frequency resources. Different OAM modes in EM waves create orthogonal channels, thereby improving spectrum efficiency. Additionally, OAM waves can more easily maintain orthogonality in line-of-sight (LOS) transmissions, offering an advantage over multiple-input and multiple-output (MIMO) technology in LOS scenarios. However, challenges such as divergence and crosstalk hinder OAM's efficiency. Additionally, channel modeling for OAM transmissions is still limited. A reliable channel model with balanced accuracy and complexity is essential for further system analysis. In this paper, we present a quasi-deterministic channel model for OAM channels in the 5.8 GHz and 28 GHz bands based on measurement data. Accurate measurement, especially at high frequencies like millimeter bands, requires synchronized RF channels to maintain phase coherence and purity, which is a major challenge for OAM channel measurement. To address this, we developed an 8-channel OAM generation device at 28 GHz to ensure beam integrity. By measuring and modeling OAM channels at 5.8 GHz and 28 GHz with a modified 3D geometric-based stochastic model (GBSM), this study provides insights into OAM channel characteristics, aiding simulation-based analysis and system optimization.

Symbiotic Sensing and Communication: Framework and Beamforming Design

In this paper, we propose a novel symbiotic sensing and communication (SSAC) framework, comprising a base station (BS) and a passive sensing node. In particular, the BS transmits communication waveform to serve vehicle users (VUEs), while the sensing node is employed to execute sensing tasks based on the echoes in a bistatic manner, thereby avoiding the issue of self-interference. Besides the weak target of interest, the sensing node tracks VUEs and shares sensing results with BS to facilitate sensing-assisted beamforming. By considering both fully digital arrays and hybrid analog-digital (HAD) arrays, we investigate the beamforming design in the SSAC system. We first derive the Cramer-Rao lower bound (CRLB) of the two-dimensional angles of arrival estimation as the sensing metric. Next, we formulate an achievable sum rate maximization problem under the CRLB constraint, where the channel state information is reconstructed based on the sensing results. Then, we propose two penalty dual decomposition (PDD)-based alternating algorithms for fully digital and HAD arrays, respectively. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate with effective localization capability for both VUEs and the weak target. In particular, the HAD beamforming design exhibits remarkable performance gain compared to conventional schemes, especially with fewer radio frequency chains.