Unmanned aerial vehicles (UAVs) have emerged as pivotal components in next-generation communication systems due to their broad coverage and flexible deployment capabilities, enabling efficient connectivity with multiple ground users. Although the integration of nonorthogonal multiple access (NOMA) and multiple-input multiple-output (MIMO) technologies in UAV communications has attracted growing research interest, existing studies remain insufficient for jointly optimizing beamforming and power allocation, particularly in terms of fully capturing the complex coupling among decision variables. This study investigates the joint optimization problem of beamforming, power allocation, and UAV position optimization in UAV systems, where the UAV communicates with multiple ground users using NOMA and MIMO technologies. The core objective is to maximize the achievable transmission rate of the system while complying with a total power budget constraint. Owing to the inherent nonconvexity of the formulated problem and the intricate coupling among decision variables, the original problem is decomposed into three subproblems: beamforming, power control, and UAV placement optimization. To address these subproblems efficiently, we propose an angle-sector-based iterative optimization framework by invoking the alternating optimization technique under the NOMA-MIMO system. This strategy not only enhances overall spectral efficiency but also ensures reliable connectivity for users at greater distances while maintaining high communication quality for those in proximity. The simulation results demonstrate that the adopted user grouping strategy, which incorporates a group matching mechanism, yields notable improvements in resource utilization. Compared with other solution strategies, the proposed alternating optimization algorithm exhibits superior performance in terms of achievable rate enhancement, thereby validating its effectiveness and practical value in complex UAV-enabled NOMA-MIMO systems.

This paper proposes a novel adaptive dual-path framework for covert semantic communication (SemCom), which integrates covert information transmission with task-oriented semantic coding. Unlike conventional covert communication methods that embed hidden messages through power-domain signal superposition, our framework embeds covert data within task-specific features via semantic-level intrinsic encoding. This new architecture introduces dual encoding paths with adaptive block selection: an Explicit path for public task execution and a Stego path that jointly encodes both public and covert information through contrastive representation alignment. A Gumbel-Softmax enabled adaptive path selection mechanism dynamically activates network blocks based on task requirements. We formulate a multi-objective optimization framework that simultaneously ensures accurate semantic understanding and reliable covert transmission. We rigorously evaluate our framework’s security against a powerful, independently trained attacker. Experimental results on the Cityscapes dataset demonstrate a state-of-the-art level of covertness: our method suppresses the attacker’s detection accuracy to a near-random guessing level of 56.12%. This robust security is achieved while simultaneously maintaining superior performance on the primary semantic tasks compared to the baselines.

This paper presents a novel approach for wireless federated learning (WFL) that, for the first time, enables the aggregation of local models with mild to moderate errors under practical communication settings, which has to date been prevented by floating-point standards, e.g., IEEE binary32, and encryption. Specifically, we propose a new conversion from floating-point local models to fixed-point models on a layer basis, eliminating the need to transmit error-intolerant sign and exponent bits of floating-point numbers while accommodating variations in model layer widths and magnitudes. We also quantify how bit errors in the ciphertext affect the plaintext when symmetric encryption is employed for local model uploading, e.g., under Rayleigh, Rician, and Nakagami-m fading channels. Notably, these bit errors are leveraged to enhance the privacy of local models. We interpret the local model transmission process as a (λ, ϵ)-Rényi Differential Privacy (DP) mechanism, where bit errors induced by noisy channels, controlled via transmit powers, and exacerbated by decryption act as DP perturbations. Experiments show the superiority of the new WFL to the status quo with higher training accuracy and lower communication overhead.

Inherent communication noises have the potential to preserve privacy for wireless federated learning (WFL) but have been overlooked in digital communication systems predominantly using floating-point number standards,e.g., IEEE 754, for data storage and transmission. This is due to the potentially catastrophic consequences of bit errors in floating-point numbers,e.g., on the sign or exponent bits. This paper presents a novel channel-native bit-flipping differential privacy (DP) mechanism tailored for WFL, where transmit bits are randomly flipped and communication noises are leveraged, to collectively preserve the privacy of WFL in digital communication systems. The key idea is to interpret the bit perturbation at the transmitter and bit errors caused by communication noises as a bit-flipping DP process. This is achieved by designing a new floating-point-to-fixed-point conversion method that only transmits the bits in the fraction part of model parameters, hence eliminating the need for transmitting the sign and exponent bits and preventing the catastrophic consequence of bit errors. We analyze a new metric to measure the bit-level distance of the model parameters and prove that the proposed mechanism satisfies (λ, ϵ)-Rényi DP and does not violate the WFL convergence. Experiments validate privacy and convergence analysis of the proposed mechanism and demonstrate its superiority to the state-of-the-art Gaussian mechanisms that are channel-agnostic and add Gaussian noise for privacy protection.

Multi-task semantic communication can serve multiple learning tasks using a shared encoder model. Existing models have overlooked the intricate relationships between features extracted during an encoding process of tasks. This paper presents a new graph attention inter-block (GAI) module to the encoder/ transmitter of a multi-task semantic communication system, which enriches the features for multiple tasks by embedding the intermediate outputs of encoding in the features, compared to the existing techniques. The key idea is that we interpret the outputs of the intermediate feature extraction blocks of the encoder as the nodes of a graph to capture the correlations of the intermediate features. Another important aspect is that we refine the node representation using a graph attention mechanism to extract the correlations and a multi-layer perceptron network to associate the node representations with different tasks. Consequently, the intermediate features are weighted and embedded into the features transmitted for executing multiple tasks at the receiver. Experiments demonstrate that the proposed model surpasses the most competitive and publicly available models by 11.4% on the CityScapes 2Task dataset and outperforms the established state-of-the-art by 3.97% on the NYU V2 3Task dataset, respectively, when the bandwidth ratio of the communication channel (i.e., compression level for transmission over the channel) is as constrained as$\frac{1}{12}$.

Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). Little attention has been given to wireless PFL (WPFL), where privacy concerns arise. Performance fairness of PL models is another challenge resulting from communication bottlenecks in WPFL. This paper exploits quantization errors to enhance the privacy of WPFL and proposes a novel quantization-assisted Gaussian differential privacy (DP) mechanism. We analyze the convergence upper bounds of individual PL models by considering the impact of the mechanism (i.e., quantization errors and Gaussian DP noises) and imperfect communication channels on the FL of WPFL. By minimizing the maximum of the bounds, we design an optimal transmission scheduling strategy that yields min-max fairness for WPFL with OFDMA interfaces. This is achieved by revealing the nested structure of this problem to decouple it into subproblems solved sequentially for the client selection, channel allocation, and power control, and for the learning rates and PL-FL weighting coefficients. Experiments validate our analysis and demonstrate that our approach substantially outperforms alternative scheduling strategies by 87.08%, 16.21%, and 38.37% in accuracy, the maximum test loss of participating clients, and fairness (Jain's index), respectively

Decentralized federated learning (D-FL) allows clients to aggregate learning models locally, offering flexibility and scalability. Existing D-FL methods use gossip protocols, which are inefficient when not all nodes in the network are D-FL clients. This article puts forth a new D-FL strategy, termed route-and-aggregate (R&A) D-FL, where participating clients exchange models with their peers through established routes (as opposed to flooding) and adaptively normalize their aggregation coefficients to compensate for communication errors. The impact of routing and imperfect links on the convergence of R&A D-FL is analyzed, revealing that convergence is minimized when routes with the minimum end-to-end (E2E) packet error rates (PERs) are employed to deliver models. Our analysis is experimentally validated through three image classification tasks and two next-word prediction tasks, utilizing widely recognized datasets and models. R&A D-FL outperforms the flooding-based D-FL method in terms of training accuracy by 35% in our tested ten-client network, and shows strong synergy between D-FL and networking. In another test with ten D-FL clients, the training accuracy of R&A D-FL with communication errors approaches that of the ideal centralized federated learning (C-FL) without communication errors, as the number of routing nodes (i.e., nodes that do not participate in the training of D-FL) rises to 28.

The development of Internet of Things (IoT) systems has given rise to security concerns stemming from the exposure of wireless channels and the exponential growth of connected devices. The security challenges can be severer in the next-generation IoT systems that can disperse over large areas under a cell-free (CF) network setting. In this article, we propose a novel covert downlink transmission scheme that jointly optimizes beamforming and artificial noise (AN) vectors to obscure critical transmissions at an eavesdropper in CF IoT Networks. We classify access points (APs) as information APs (IAPs) and noise APs (NAPs) based on their proximity to the IoT devices. IAPs transmit information while NAPs generate AN to prevent eavesdropping. We derive a closed-form solution for the detection error probability. By using the Lagrangian dual algorithm, the complex logarithmic problem is transformed into sum-of-ratios form. Then, we use semidefinite relaxation (SDR) to maximize the covert transmit rate. Numerical results show that the proposed scheme outperforms the state of the art, i.e., the suboptimal Rand- AP scheme, by increasing the transmission rate by more than 23% while maintaining covertness and is better than the rest of the benchmark schemes.

This paper analyzes the impact of imperfect communication channels on decentralized federated learning (D-FL) and subsequently determines the optimal number of local aggregations per training round, adapting to the network topology and imperfect channels. We start by deriving the bias of locally aggregated D-FL models under imperfect channels from the ideal global models requiring perfect channels and aggregations. The bias reveals that excessive local aggregations can accumulate communication errors and degrade convergence. Another important aspect is that we analyze a convergence upper bound of D-FL based on the bias. By minimizing the bound, the optimal number of local aggregations is identified to balance a trade-off with accumulation of communication errors in the absence of knowledge of the channels. With this knowledge, the impact of communication errors can be alleviated, allowing the convergence upper bound to decrease throughout aggregations. Experiments validate our convergence analysis and also identify the optimal number of local aggregations on two widely considered image classification tasks. It is seen that D-FL, with an optimal number of local aggregations, can outperform its potential alternatives by over 10% in training accuracy.

Sensing in Integrated Sensing and Communications (ISAC) systems with clock asynchronism between the transmitter and receiver poses significant challenges. Understanding the fundamental limits of sensing performance in such setups, which remain largely unknown, is crucial. This paper investigates the sensing performance bounds in the presence of clock asynchronism. In both single-carrier and multi-carrier models, we derive the Cramér-Rao bounds (CRB) for estimating dynamic channel path parameters including angle of arrival, delay, and complex gain sequence (CGS). Through mathematical analyses and numerical simulations, we conduct a comprehensive study on how these bounds depend on various system parameters and the impact of clock asynchronism. Our findings highlight the degradation of parameter estimation performance due to clock asynchronism and reveal low-accuracy zones for CGS estimation in strong-line-of-sight scenarios. Additionally, we observe asymptotic mitigation in performance degradation with larger bandwidth, providing valuable insights for system design and optimization.

In recent years, the Internet of Things (IoT) has been studied in the context of the Metaverse to provide users with immersive cyber-virtual experiences in mixed-reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.

Wireless federated learning (WFL) undergoes a communication bottleneck in uplink, limiting the number of users that can upload their local models in each global aggregation round. This paper presents a new multi-carrier non-orthogonal multiple-access (MC-NOMA)-empowered WFL system under an adaptive learning setting of Flexible Aggregation. Since a WFL round accommodates both local model training and uploading for each user, the use of Flexible Aggregation allows the users to train different numbers of iterations per round, adapting to their channel conditions and computing resources. The key idea is to use MC-NOMA to concurrently upload the local models of the users, thereby extending the local model training times of the users and increasing participating users. A new metric, namely, Weighted Global Proportion of Trained Mini-batches (WGPTM), is analytically established to measure the convergence of the new system. Another important aspect is that we maximize the WGPTM to harness the convergence of the new system by jointly optimizing the transmit powers and subchannel bandwidths. This nonconvex problem is converted equivalently to a tractable convex problem and solved efficiently using variable substitution and Cauchy’s inequality. As corroborated experimentally using a convolutional neural network and an 18-layer residential network, the proposed MC-NOMA WFL can efficiently reduce communication delay, increase local model training times, and accelerate the convergence by over 40%, compared to its existing alternative.

This paper investigates the energy-efficient resource allocation problem in massive multiple input and multiple output (MIMO) non-orthogonal multiple access (NOMA) networks with practical non-linear energy harvesting (NEH). In the considered system, a multi-antenna base station (BS) transfers power to the Internet-of-Things (IoT) devices via energy beamforming in the downlink, followed by the IoT devices sending their data simultaneously in the uplink by consuming the harvested energy. A time division protocol is designed to adequately allocation the energy harvesting (EH) time and wireless information transmission time. To improve the energy efficiency (EE), we propose a joint power, time and antenna selection allocation scheme under the NEH model. An EE maximization problem is formulated to effectively determine the optimal resource allocation strategies. As the formulated problem is non-trivial, a non-linear fraction programming method is applied to convert the problem into a convex optimization problem, and then solve it by Lagrange dual decomposition approach. Simulation results demonstrate the effectiveness of the proposed solution.