Affine frequency division multiplexing (AFDM) has emerged as a robust multi-carrier modulation candidate for high-mobility communications. This paper investigates an AFDM based integrated sensing and communications (ISAC) framework for unmanned aerial vehicle (UAV) links within space-air-ground integrated networks (SAGINs). A key contribution of this work is the novel design of the cyclic prefix and postfix (CPP) for AFDM, which is specifically tailored to accommodate wireless power transfer (WPT) requirements, thereby supporting simultaneous information and energy transmission. Specifically, the base station exploits the reflected echoes of AFDM signals to estimate sensing parameters, including the position, velocity, and angle of mobile users. To optimize the communication link, we propose an intelligent adaptive modulation and coding (AMC) decision-making process. A specialized dataset is established, integrating physically interpretable metrics—such as distance, velocity, and angle—with historical AFDM channel state information characterized by its unique chirp-domain representation. Subsequently, a hybrid deep learning architecture, designated as CNN-LSTM, is developed to establish a unified evaluation framework. This framework leverages the feature extraction capabilities of convolutional neural networks (CNNs) to process the spatial-temporal correlations of the AFDM channel, while utilizing Long Short-Term Memory (LSTM) networks to capture the long-term temporal dependencies of UAV trajectories. Simulation results demonstrate that the proposed modeling approach achieves superior separability and robustness, aligning closely with the ideal adaptive envelope while exhibiting enhanced cross-trajectory generalization capabilities compared to conventional methodologies.

The proliferation of unmanned aerial vehicles (UAVs) in low-altitude airspace necessitates sophisticated resource management supporting both cooperative communications and unauthorized intrusion detection. This paper investigates joint optimization of cell association and power allocation in integrated sensing and communication (ISAC)-enabled low-altitude networks. We propose a novel dual-function framework where ground base stations simultaneously provide communication services to authorized UAVs and localize non-cooperative UAVs for collision avoidance. We establish a channel model capturing the relationship between communication rate and sensing accuracy, formulating an optimization problem that maximizes the weighted sum of system average sum rate and localization quality of service (QoS). The problem jointly optimizes cell association, communication power allocation, and sensing power allocation under UAV localization QoS and cooperative sum rate constraints. To solve the resulting mixed-integer non-convex problem, we propose a joint optimization algorithm based on optimal transport theory (J2OT) that directly handles discrete variables without relaxation, avoiding accuracy losses of conventional approximation methods. J2OT decomposes the problem using optimal transport-based cell association optimization (OTC) and power allocation optimization (OTP). Simulation results demonstrate J2OT’s superiority, achieving 1.5 bits/s/Hz improvement in system objective and 7.5% reduction in localization Cramér-Rao bound compared to Weighted Voronoi and Iterative Water-filling baseline methods.

Emerging 6G wireless systems suffer severe performance degradation in challenging environments like high-speed trains traversing dense urban corridors and Unmanned Aerial Vehicles (UAVs) links over mountainous terrain. These scenarios exhibit non-Gaussian, non-stationary channels with heavy-tailed fading and abrupt signal fluctuations. To address these challenges, this paper proposes a novel wireless channel model based on symmetric α-stable Lévy processes, thereby enabling continuous-time state-space characterization of both long-term and short-term fading. Building on this model, a generalized optimal control framework is developed via a fractional Hamilton-Jacobi-Bellman (HJB) equation that incorporates the Riesz fractional operator to capture non-local spatial effects and memory-dependent dynamics. The existence and uniqueness of viscosity solutions to the fractional HJB equation are rigorously established, thus ensuring the theoretical validity of the proposed control formulation. Numerical simulations conducted in a multi-cell, multi-user downlink setting demonstrate the effectiveness of the fractional HJB-based strategy in optimizing transmission power under heavy-tailed co-channel and multi-user interference.

In this paper, we consider transmit beamforming and reflection patterns design in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems, where the dual-function base station (DFBS) lacks channel state information (CSI). To address the high overhead of cascaded channel estimation, we propose an improved artificial fish swarm algorithm (AFSA) combined with a feedback-based joint active and passive beam training scheme. In this approach, we consider the interference caused by multipath user echo signals on target detection and propose a beamforming design method that balances both communication and sensing performance. Numerical simulations show that the proposed AFSA outperforms other optimization algorithms, particularly in its robustness against echo interference under different communication signal-to-noise ratio (SNR) constraints.

Low peak-to-average power ratio (PAPR) transmission is an important and favorable requirement prevalent in radar and communication systems, especially in transmission links integrated with high power amplifiers. Meanwhile, motivated by the advantages of reconfigurable intelligent surface (RIS) in mitigating multi-user interference (MUI) to enhance the communication rate, this paper investigates the design problem of joint waveform and passive beamforming with PAPR constraint for integrated sensing and communication (ISAC) systems, where RIS is deployed for downlink communication. We first construct a trade-off optimization problem for the MUI and beampattern similarity under PAPR constraint. Then, in order to solve this multivariate problem, an iterative optimization algorithm based on alternating direction method of multipliers (ADMM) and manifold optimization is proposed. Finally, the simulation results show that the designed waveforms can well satisfy the PAPR requirement of the ISAC systems and achieve a trade-off between radar and communication performance. Under high signal-to-noise ratio (SNR) conditions, compared to systems without RIS, RIS-aided ISAC systems have a performance improvement of about 50 % in communication rate and at least 1 dB in beampatterning error.

Orthogonal time frequency space (OTFS) modulation is widely acknowledged as a prospective waveform for future wireless communication networks. To provide insights for the practical system design, this paper analyzes the outage probability of OTFS modulation with finite blocklength. To begin with, we present the system model and formulate the analysis of outage probability for OTFS with finite blocklength as an equivalent problem of calculating the outage probability with finite blocklength over parallel additive white Gaussian noise (AWGN) channels. Subsequently, we apply the equivalent noise approach to derive a lower bound on the outage probability of OTFS with finite blocklength under both average power allocation and water-filling power allocation strategies, respectively. Finally, the lower bounds of the outage probability are determined using the Monte-Carlo method for the two power allocation strategies. The impact of the number of resolvable paths and coding rates on the outage probability is analyzed, and the simulation results are compared with the theoretical lower bounds.

Due to their flexibility and dynamic coverage capabilities, Unmanned Aerial Vehicles (UAVs) have emerged as vital platforms for emergency communication in disaster-stricken areas. However, the complex channel conditions in high-speed mobile scenarios significantly impact the reliability and efficiency of traditional communication systems. This paper presents an intelligent emergency communication framework that integrates Orthogonal Time Frequency Space (OTFS) modulation, semantic communication, and a diffusion-based denoising module to address these challenges. OTFS ensures robust communication under dynamic channel conditions due to its superior anti-fading characteristics and adaptability to rapidly changing environments. Semantic communication further enhances transmission efficiency by focusing on key information extraction and reducing data redundancy. Moreover, a diffusion-based channel denoising module is proposed to leverage the gradual noise reduction process and statistical noise modeling, optimizing the accuracy of semantic information recovery. Experimental results demonstrate that the proposed solution significantly improves link stability and transmission performance in high-mobility UAV scenarios, achieving at least a 3dB SNR gain over existing methods.

Existing deep learning-based semantic communication (DeepSC) systems are typically trained for specific single-channel condition, which restricts the overall adaptability and resilience to interference. To address this limitation, we propose an innovative semantic adaptive feature extraction (SAFE) network that dynamically generates and fuses multiple sub-semantics, each characterized by unique features that can be tailored to different channel conditions. This paper also introduces three advanced learning algorithms to refine and enhance the generated sub-semantics, optimizing the semantic successive refinement performance of the SAFE network. Furthermore, we integrate a novel interference-aware semantic transmission method based on non-orthogonal multiple access (NOMA) into this framework. This approach enables users to adaptively select appropriate subsets for efficient transmission and image reconstruction, tailored to the prevailing channel interference conditions. Through extensive simulation experiments, we demonstrate the framework’s capability to generate and transmit semantics under diverse channel interference scenarios adaptively, and verify the effectiveness through both objective and subjective quality evaluations.

This paper focuses on an intelligent reflecting surface (IRS)-assisted lossy communication system with correlated Rayleigh fading. We analyze the correlated channel model and derive the outage probability of the system. Then, we design a deep reinforce learning (DRL) method to optimize the phase shift of IRS, in order to maximize the received signal power. Moreover, this paper presents results of the simulations conducted to evaluate the performance of the DRL-based method. The simulation results indicate that the outage probability of the considered system increases significantly with more correlated channel coefficients. Moreover, the performance gap between DRL and theoretical limit increases with higher transmit power and/or larger distortion requirement.

In this paper, we address the problem of image semantic communication in a multi-user deployment scenario and propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system (FSSC). Firstly, we demonstrate that the adoption of a Swin Transformer for joint source-channel coding (JSCC) effectively extracts semantic information in the communication system. Next, the FL framework is introduced to collaboratively learn a global model by aggregating local model parameters, rather than directly sharing clients’ data. This approach enhances user privacy protection and reduces the workload on the server or mobile edge. Simulation evaluations indicate that our method outperforms the typical JSCC algorithm and traditional separate-based communication algorithms. Particularly after integrating local semantics, the global aggregation model has further increased the Peak Signal-to-Noise Ratio (PSNR) by more than 2dB, thoroughly proving the effectiveness of our algorithm.

With the development of dual-function radar communication (DFRC) systems, waveform design has received increasing attention. At the same time, subcarrier superposition can lead to the high peak-to-average power ratio (PAPR) problem in orthogonal frequency division multiplexing (OFDM). To solve the problem, in this paper, we propose an alternating direction method of multipliers (ADMM)-based low-PAPR OFDM waveform design algorithm for DFRC systems, which minimizes the signal PAPR with the constraint of the zero integrated sidelobe level (ISL). Moreover, we compare our algorithm with a recently proposed benchmark algorithm. Simulation results demonstrate that our algorithm has better performance compared to the$l$- norm cyclic algorithm.

Physical layer authentication (PLA) is a promising solution to address the security issue raised due to malicious jamming or spoofing. However, accurate and diversified channel state information is required to implement the PLA schemes. In this regard, reconfigurable intelligent surface (RIS) has the potential to quickly reshape the communication environment at a cheap cost, and thus has great potential to enhance the PLA. In this paper, we propose a RIS-assisted channel impulse response (CIR)-based dynamic PLA scheme. Specifically, the receiver exploits the geographic location information of the transmitters embedded in CIR to identify the message. In order to reduce the impact of the components representing environmental changes in CIR on the authentication, the method of regularly updating CIR database is adopted. In addition, with RIS enriched CIR information, we can achieve a high authentication rate by constructing a classification neural network. Experiments are conducted based on the communication system with DeepMIMO datasets, and the simulation results demonstrate that the proposed authentication scheme is effective for the identification of both first-attack and non-first-attack spoofers.

In blockchain systems characterized by computation competition, allocating computation resources is of paramount significance for the economic benefits of nodes. Besides, uncer-tainties of computation resources also affect the node's profits. In this paper, we address the computation resource allocation issue within a proof-of-work (PoW) blockchain system without exact information on the available resources, which impedes the direct investigation of the maximum mining profit. Correspondingly, we establish the chance-constrained threshold for maximum achievable profit through the blockchain in an uncertain environment and maximize this threshold under a given outage probability. Particularly, the uncertain computation resource is modeled only with its first and second statistics, which lack the exact distribution information. In this respect, we propose the distributionally robust approach to tackle the chance-constrained resource allocation strategy, which guarantees the intended profit threshold regardless of the actual distribution. We show that the considered problem admits a conditional value-at-risk (CVaR) approximation reformulation, which can be handled by alternately optimizing the resource allocation strategy and the profit threshold. Simulation results demonstrate that the proposed design is robust against the uncertainty distribution, and effectively guarantees the profits of miners.

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments and large-scale DL models are critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a novel neural network named convolution-linked signal transformer (ClST) and a novel knowledge distillation method named signal knowledge distillation (SKD). The ClST is accomplished through three primary modifications: a hierarchy of transformer containing convolution, a novel attention mechanism named parallel spatial-channel attention (PSCA) mechanism and a novel convolutional transformer block named convolution-transformer projection (CTP) to leverage a convolutional projection. The SKD is a knowledge distillation method to effectively reduce the parameters and complexity of neural networks. We train two lightweight neural networks using the SKD algorithm, KD-CNN and KD-MobileNet, to meet the demand that neural networks can be used on miniaturized devices. The simulation results demonstrate that the ClST outperforms advanced neural networks on all datasets. Moreover, both KD-CNN and KD-MobileNet obtain higher recognition accuracy with less network complexity, which is very beneficial for the deployment of AMR on miniaturized communication devices.

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments is critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a novel neural network named convolution signal transformer (CST). The CST is accomplished through three primary modifications: a hierarchy of transformer containing convolution, a novel signal-specific self-attention mechanism to replace the multi-headed self-attention mechanism in Transformer, and a novel convolutional transformer block named convolution-transformer projection (CTP) to leverage a convolutional projection. The simulation results demonstrate that the CST outperforms advanced neural networks on all datasets, which is very beneficial for the deployment of AMR in complicated channel environments.

This article focuses on the simultaneous wireless information and power transfer (SWIPT) systems, which provide both the power supply and the communications for Internet‐of‐Things (IoT) devices in the sixth‐generation (6G) network. Due to the extremely stringent requirements on reliability, speed, and security in the 6G network, aerial access networks (AANs) are deployed to extend the coverage of wireless communications and guarantee robustness. Moreover, sparse code multiple access (SCMA) is implemented on the SWIPT system to further promote the spectrum efficiency. To improve the speed and security of SWIPT systems in 6G AANs, we have developed an optimization algorithm of SCMA to maximize the secrecy sum rate (SSR). Specifically, a power‐splitting (PS) strategy is applied by each user to coordinate its energy harvesting and information decoding. Hence, the SSR maximization problems in the SCMA system are formulated in terms of the PS and resource allocation, under the constraints on the minimum rates and minimum harvested energy of individual users. Then, a successive convex approximation method is introduced to transform the nonconvex problems to the convex ones, which are then solved by an iterative algorithm. In addition, we investigate the SSR performance of the SCMA system supported by our optimization methods, when the impacts from different perspectives are considered. Our studies and simulation results show that the SCMA system supported by our proposed optimization algorithms significantly outperforms the legacy system with uniform power allocation and fixed PS.

This paper focuses on a lossy transmission of binary symmetric source (BSS) with down-link non-orthogonal multiple access (DL-NOMA). The transmitted binary sequences are lossy-compressed descriptions of the same BSS, which are overlapped in a specified signal format, corresponding to the receivers with pre-determined distortion requirements. Despite a higher spectrum efficiency achieved by DL-NOMA, redundancy may still remain if the overlapped descriptions are correlated. In this work, a system combining DL-NOMA and successive refinement, referred to as DN-SR, is proposed. Specifically, instead of re-constructing the source by a single description, the receiver can achieve a low distortion by first recovering the basic description, and then refining it with the help of another refinement description. If the two descriptions are made independent, transmission efficiency of the conventional DL-NOMA can be further improved. As the main contribution, this paper derives the outage probability of DN-SR in closed-form, assuming block Rayleigh fading channels. The advantage of DN-SR is numerically studied in terms of the system outage probability, compared to both the conventional DL-NOMA, and a modified DL-NOMA which exploits the correlation between descriptions, referred to as DN-CE. Finally, the optimal power allocation to the two descriptions is investigated for DN-SR, aiming to minimize the system outage probability.

This paper presents an in-depth performance analysis of lossy communications in a single-relay system, where the recovered information is not necessarily lossless in both the relay and the destination. In this system, the relay continues transmitting the sequence with source-relay (S-R) link errors to the destination even if errors are detected after decoding, i.e., so-called lossy-forward (LF) strategy. The problem can be decomposed into two parts as follows: a point-to-point coding problem in the S-R link and a lossy source coding problem with an LF relay in the source-destination (S-D) and a relay-destination (R-D) links. To begin with, we derive the admissible rate region of the lossy source coding problem with the LF relay for a specified distortion requirement. Then, we focus on the analysis of outage probability over block Rayleigh fading channels. Finally, a practical encoding/decoding scheme is proposed for the evaluation of system performance by computer simulations. Due to the suboptimal channel coding and incomplete utilization of joint typicality, the theoretical performance cannot be achieved in the simulation; however, the tendency of curves in simulations matches that in theoretical calculation.

Wireless sensor networks (WSNs) are the core component in the big data era. Due to the unreliable transmission environment, it is significantly useful to introduce a helper to refine the system performance. To begin with, we formulate the system model of WSNs as a problem of multiterminal source coding. Subsequently, we propose a 3D distributed compress-bin scheme and derive a corresponding inner bound by analyzing the expected rate-distortion. Finally, we investigate the performance improvement of a helper by comparing the derived inner bound with the Berger-Tung inner bound and through simulation. Both the theoretical bounds and simulation results indicate that a helper can obviously improve the system performance.

As the most widely used standards for wireless local area network (WLAN), IEEE 802.11 standards are continuously amended by introducing new techniques so as to meet the increasing demands. In order to verify the performance of amended protocols, network simulation is considered as a significant method. However, as far as we know, current simulation tools are only for either media access control layer (MAC) or physical layer (PHY). The separate simulation of MAC and PHY can hardly evaluate the performance of IEEE 802.11ax in whole system level for authenticity and objectivity. Hence, the next generation WLAN (IEEE 802.11ax) requires integrated system simulation to take impacts of both MAC and PHY techniques into account. Moreover, IEEE 802.11ax introduces some new techniques, such as orthogonal frequency division multiple access (OFDMA), multi-user multiple input multiple output (MU- MIMO) and non-continuous channel bonding. In this paper, we design and further implement the integrated link-system level simulation platform, which makes it possible to evaluate the new technologies for IEEE 802.11ax. Moreover, we propose a MAC protocol combining OFDMA, MU-MIMO, non-continuous channel bonding and link adaptation and further evaluate its performance. Finally, we validate performance gains of IEEE 802.11ax through simulation, and the simulation results show that IEEE 802.11ax has obviously higher throughput, better quality of service (QoS) and higher multi- channel efficiency. To the best of our knowledge, this is the first work to design and implement simulation platform for IEEE 802.11ax with an integrated link- system level framework.