The proliferation of generative AI services, from cloud-based synthesis to on-device content creation, is increasingly bottlenecked by the need for efficient communication over dynamic wireless channels. However, deploying these models at the network edge is impeded by two fundamental challenges, namely the weak coupling between the model and physical channel conditions and the prohibitive computational latency of diffusion-based inference. Existing approaches often treat diffusion as a static module trained at a fixed noise level and indexed by abstract timesteps without physical interpretation, which yields suboptimal performance under time-varying channels. To address this, we propose an signal-to-noise ratio guided latent diffusion model (SNR-LDM). The diffusion process is reparameterized with a physically interpretable mapping between denoising timesteps and channel SNR, aligning the denoising trajectory with the channel state and enabling SNR-adaptive inference. To enhance semantic fidelity, we introduce multimodal guidance that fuses textual and visual prompts to steer content generation. We further develop two inference schemes, namely a dynamic multi-step procedure for highest reconstruction quality and a single-step analytic inversion for ultra-low latency, which makes deployment on resource-constrained edge devices feasible. Experiments show that the proposed model achieves about 4 dB higher PSNR than adaptive baselines and yields up to 8.2% lower LPIPS than diffusion-based benchmarks. The framework links large-scale generative modeling with the communication-constrained network edge and enables robust and efficient deployment of generative AI services.

The rapid growth of multimodal data in 6G-enabled applications demands a paradigm shift from traditional bit-rate-oriented communication to semantic transmission. While diffusion models offer a promising solution for high-fidelity generation, their application is hindered by a fundamental disconnect from the physical channel and high computational overhead. Existing methods typically treat the denoising process as independent of the channel conditions, using abstract integer timesteps that lack physical meaning, and require a full, lengthy sampling chain for reconstruction. To address these challenges, we propose signal-to-noise ratio-guided latent diffusion model (SNR-LDM), which re-parameterizes the diffusion process to map denoising time steps directly to physical SNR. This enables adaptive inference from noise levels precisely matched to the estimated channel state, significantly reducing redundant computations while further guiding the reconstruction process through multimodal prompts to ensure semantic consistency. Experimental results demonstrate that, across a wide range of channel conditions, our proposed SNR-LDM achieves a PSNR gain of approximately 4 dB over ADJSCC while simultaneously reducing the LPIPS from 0.75 to 0.25.

The reciprocity of wireless channels is a prerequisite for physical layer secret key generation (SKG). However, inherent factors, such as noise, asynchronous observations, and hardware impairments, disrupt the ideal reciprocity in the channel state information (CSI) observed by the two legitimate parties. To address this issue, we propose a mutual information-driven autoencoder (MIAE) architecture to extract reciprocal channel features from the non-ideal channel observations of legitimate parties. MIAE is constructed with an AutoEncoder Network (AENet) and a mutual information neural estimator (MINE). Specifically, AENet employs a structure with dual encoders and a shared decoder. The two encoders, integrated with convolutional block attention modules (CBAMs), are designed to focus on reciprocal features within CSI observations and to compensate for temporal variations caused by asynchronous observations. The shared encoder and the correspondingly designed loss function further concentrate the autoencoder’s reciprocity enhancement capability into the encoders. MINE is integrated into the proposed MIAE to estimate the mutual information between the channel features of the two parties. This estimation is then used to formulate a mutual information loss, which guides the encoders to learn channel features that closely match the optimal distribution, thereby boosting the key generation rate. Furthermore, a complete SKG scheme is designed based on the proposed channel feature extractor, MIAE. Simulation results show that our proposed MIAE can extract channel features with strong reciprocity and thus achieve excellent SKG performance based on the extracted features. The generalization performance of the proposed MIAE architecture for communication scenarios of different scales has also been examined.

Secret-key generation (SKG) at physical layer is considered as a promising solution for lightweight key distribution. On the analysis of the secret-key capacity over multipath fading channels, existing studies generally neglect the channel observation method, which has a direct impact on the secret-key capacity. Moreover, there is a lack of concise and interpretable expressions for the secret-key capacity based on the channel state information. Motivated by the above, we analyze the performance of SKG based on MMSE estimation of channel impulse response over multipath fading channels. The general expression of secret-key capacity is derived. We find that the secret-key capacity depends on the estimation signal-to-noise ratios (SNRs) of channel-tap gains. Then, the condensed-parameter expressions of exact secret-key capacity and the expressions of asymptotic secret-key capacity at high SNR are derived in two specific PDP cases. We show that the bandwidth and the transmission SNR determine the upper bound of secret-key capacity over channels with different degrees of freedom for the flat PDP case. In addition, we find that the asymptotic secret-key capacities under these two PDPs follow a uniform form, which is a linear function of the channel estimation SNR (in dB). Finally, we simulate the channel probing process under different transmission and channel parameters and exploit the simulated channel estimates to estimate the secret-key capacity through numerical methods. The simulation results demonstrate the theoretical analysis and conclusions.

Deep Neural Network (DNN)-based physical layer techniques are attracting considerable interest due to their potential to enhance communication systems. However, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques and their cost in terms of computational complexity. We further investigate and also experimentally validate how information is flown in a DNN-based communication system under the information theoretic concepts.

Recently, deep neural network (DNN)-based physical layer communication techniques have attracted considerable interest. Although their potential to enhance communication systems and superb performance have been validated by simulation experiments, little attention has been paid to the theoretical analysis. Specifically, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques, and also drive their cost in terms of computational complexity. To achieve this goal, we first analyze the encoding performance of a DNN-based transmitter and compare it to a traditional one. And then, we theoretically analyze the performance of DNN-based estimator and compare it with traditional estimators. Third, we investigate and validate how information is flown in a DNN-based communication system under the information theoretic concepts. Our analysis develops a concise way to open the “black box” of DNNs in physical layer communication, which can be applied to support the design of DNN-based intelligent communication techniques and help to provide explainable performance assessment.

With innovative applications of unmanned aerial vehicle (UAV) ad hoc networks in various areas, their demands on broad bandwidth, large capacity and low latency become prominent. The combination of millimeter wave, directional antenna and time division multiple access techniques, which enables concurrent transmissions, is promising to deal with it. In this paper, we study the resource allocation problem in UAV ad hoc networks. Specifically, the slot assignment, antenna boresight and transmit power are jointly optimized to promote the network capacity. First, we formulate the optimization problem as the maximization of the fairness-weighted network capacity, subject to the constraint on priority guarantee. Then, because the formulated problem is a mixed integer non-linear programming problem (MINLP), which is NP-hard, two algorithms called dual-based iterative search algorithm (DISA) and sequential exhausted allocation algorithm (SEAA) are respectively proposed to efficiently solve it with acceptable complexity. DISA slacks the MINLP into a continuous-variable optimization problem and solves it with the Lagrangian dual method in an iterative manner. As a heuristic method, SEAA schedules links sequentially, i.e., from high-priority to low-priority ones. Numerical results demonstrate that both DISA and SEAA can efficiently allocate resources for UAVs, while guaranteeing the fairness and priority of links.

Devices in the Internet of Things (IoT) are usually limited in computing resources and energy capacity, which means that encryption schemes with higher complexity are not suitable for them to ensure secure communication. As a promising solution to this problem, physical layer key generation suggests that shared secret keys can be generated from noisy wireless channel measurements to enhance the security of wireless communications. In this article, we propose a key generation scheme with extremely low implementation complexity, which allows physical layer key generation to be implemented on IoT nodes. First, we preprocess the channel measurements with simple moving average filtering before quantization to improve channel reciprocity. Next, a bidirectional difference quantization scheme is proposed to realize reliable quantization of channel measurements, which is ingenious in that the quantization process does not depend on quantization thresholds, and thus, the mismatched key bits caused by measurements close to quantization thresholds can be effectively avoided. Then, we propose an improved Cascade protocol to achieve lightweight and efficient information reconciliation. The simulation results show that our scheme can well balance the reliability and efficiency of key generation, and has excellent performance in terms of implementation complexity and key randomness.

The heterogeneous cloud radio access network (H‐CRAN) is considered a promising solution to expand the coverage and capacity required by fifth‐generation (5G) networks. UAV, also known as wireless aerial platforms, can be employed to improve both the network coverage and capacity. In this paper, we integrate small drone cells into a H‐CRAN. However, new complications and challenges, including 3D drone deployment, user association, admission control, and power allocation, emerge. In order to address these issues, we formulate the problem by maximizing the network throughput through jointly optimizing UAV 3D positions, user association, admission control, and power allocation in H‐CRAN networks. However, the formulated problem is a mixed integer nonlinear problem (MINLP), which is NP‐hard. In this regard, we propose an algorithm that combines the genetic convex optimization algorithm (GCOA) and particle swarm optimization (PSO) approach to obtain an accurate solution. Simulation results validate the feasibility of our proposed algorithm, and it outperforms the traditional genetic and K‐means algorithms.

This paper investigates the air-to-air channel model for Unmanned Aerial Vehicle (UAV) communication links. Although the 3-D ellipsoid model is widely used for characterization of wireless channels, present studies only include the angular distribution of scatterers while the power distribution is absent. Besides, all scatterers are assumed homogeneous in existing work, which is inaccurate for low-altitude UAVs. For above-mentioned problems, our work has two main contributions. First, a precise description of statistical characteristics of receiving power in both delay and direction of arrival (DoA) is provided based on the current 3-D ellipsoid model. Second, we extend the original model to a composite model including two independent ellipsoid models. Apart from surrounding scatterers, obtrusive objects like skyscrapers are specially considered in our model as far clusters. These far clusters could cause distinctive rays with excessive delay even from a distance and influence the statistical characteristics of the channel evidently. Numerical results show that the occurrence of far clusters increases the spreads of delay and DoA.

Exploiting unmanned aerial vehicles (UAVs) as flying base stations (BSs) to assist the terrestrial cellular networks is promising in 5G and beyond. Despite the inherent potentials, one challenging problem is how to optimally deploy multiple UAVs to achieve on-demand coverage for ground user equipment (UE). In this article, we model the deployment problem as minimizing the number of UAVs and maximizing the load balance among them, which is subject to two main constraints, i.e., UAVs should form a robust backbone network and they should keep connected with the fixed BSs. To solve this optimization problem with low complexity, we decompose the problem into two subproblems and propose a hybrid algorithm to solve them stepwise. First, a centralized greedy search algorithm is used to heuristically obtain the minimum number of UAVs and their suboptimal positions in a discontinuous space. Then, a distributed motion algorithm is adopted which enables each UAV to autonomously control its motion toward the optimal position in a continuous space. The proposed algorithm is applicable to various scenarios where UAVs are deployed alone or with fixed BSs regardless of the UE distribution. Extensive simulations validate the proposed algorithm.

Dynamic malicious jamming of spectrum throughout the entire communication band is a major issue confronted by tactical communications. The conventional spectrum access scheme based on the central control node fails to satisfy the demand of tactical communications; it has a number of drawbacks including low battlefield survival rates, high computational complexity, large interactive communication overhead and slow reaction to dynamic jamming. In this paper, we propose a distributed multi-agent spectrum access strategy without the interactive communication overhead to evade dynamic jamming. Furthermore, A simplified Q reinforcement learning algorithm is applied to alleviate collisions of spectrum among nodes. Under the proposed strategy, all nodes can predict and evade dynamic jamming as well as avoid collisions of spectrum usage with other nodes. Simulation results verify the collision-avoidance learning algorithm and indicate that the proposed strategy outperforms the random spectrum access strategy.

Cognitive radio (CR) has emerged as an advanced and promising technology to exploit the wireless spectrum opportunistically. In cognitive radio networks (CRNs), any pairwise communicating nodes are required to rendezvous on a commonly available channel prior to exchange information. In the earlier research, the most popular method is selecting a Common Control Channel (CCC) in CRNs to establish the rendezvous. However, employing a CCC has many problems such as the control channel saturation, vulnerability to jamming attacks, and inapplicability to dynamic network scenarios. Therefore, the blind rendezvous, which requires neither CCC nor the information of the target user's available channels, has recently attracted a lot of research interests. As a contribution to this research area, in this paper we propose a Sender-Jump Receiver-Wait (SJ-RW) blind rendezvous algorithm, which has fully satisfied the following requirements: 1) guaranteeing rendezvous; 2) realizing full rendezvous diversity, i.e., any pair of users can rendezvous on all commonly available channels; 3) requiring no time-synchronization; 4) supporting both symmetric and asymmetric models; 5) supporting multi-user/multi-hop scenarios and 6) consuming short Time-to-Rendezvous (TTR). Theoretical analysis, computer simulations and experiment with testbed have validated the proposed SJ-RW algorithm.

The blind rendezvous, which requires neither Common Control Channel (CCC) nor the information of the target user's available channels, has recently attracted a lot of research interests. As a contribution to this research area, in this paper we propose a Sender-Jump Receiver-Wait blind rendezvous algorithm, which has fully satisfied the following requirements: 1) guaranteeing rendezvous; 2) realizing full rendezvous diversity, i.e., any pair of users can rendezvous on all commonly available channels; 3) requiring no time-synchronization; 4) supporting both symmetric and asymmetric models; 5) supporting multi-user/multi-hop scenarios and 6) consuming short Time-to-Rendezvous (TTR). Theoretical analysis and computer simulations have validated our algorithm.

In this paper, we address physical layer security in multiple-input single-output (MISO) wiretap channels in the presence of a passive eavesdropper. Artificial noise (AN) is used to provide masked beamforming for degrading the eavesdropper's channel. Existing works based on Gaussian input assumption may lead to substantial secrecy rate loss when considering practical finite-alphabet input. In order to maximize ergodic secrecy rate, a power allocation scheme between the information-bearing signal and AN is proposed. Despite the fact that there is no closed- form expression of mutual information under the constraint of finite-alphabet input, we exploit the relationship between mutual information and minimum mean square error (MMSE) to transform the original problem into a zero-searching problem, which can be solved via gradient search algorithm. Numerical simulations demonstrate that the proposed scheme offers a very good approximation to the optimal performance.

In this study, the authors propose a novel secret key generation scheme to address the consecutively identical secret key bits problem in stationary environments. First, by randomising the phase of probe signals with stochastic coefficients, they sum the channel estimates in a moving window to get new records with remarkable fluctuations. Then, they propose an adaptive equal probability quantisation approach to ensure the randomness of the secret key. Furthermore, considering the worst scenario, the security of the scheme is evaluated in terms of the adversary's mean square error (MSE) for the single‐antenna system. While for the multi‐antenna system, they propose an artificial noise‐aided strategy to compromise the adversary's MSE performance. The simulation results reveal that the security of the scheme is guaranteed and a better security performance is achieved compared with the prior works. Finally, they validate the feasibility of the proposed scheme in real stationary environments. The testing results show that their scheme achieves remarkable performance in bit mismatch rate and key generation rate, and the generated keys pass the National Institute of Standards and Technology test.

In this paper, we investigate the secrecy performance for a multiple-input multiple-output (MIMO) wiretap channel in the presence of a multiantenna eavesdropper. In particular, the legitimate transmitter uses transmit antenna selection (TAS) to transmit on a single antenna with the largest signal-to-noise ratio (SNR) while both the legitimate receiver and the eavesdropper adopt maximal ratio combining (MRC) for reception. We derive exact closed-form expressions for the probabilities of achieving positive secrecy rate and secrecy outage in the case of imperfect feedback due to feedback delay and/or feedback error. Furthermore, we derive the asymptotic secrecy outage probability at high SNR, which accurately reveals the secrecy diversity loss due to imperfect feedback. Simulation results are provided to verify our analytical results and illustrate the impact of imperfect feedback on the secrecy performance of such a wiretap system.

This paper studies masked beamforming schemes for secure communication in broadcast multiple-input multiple-output (MIMO) systems with a passive multiple-antenna eavesdropper. Assuming no information about the eavesdropper is available at the transmitter, we aim to maximize the transmit power of the artificial noise while meeting mean square error (MSE) constraints at the legitimate receivers and the total power constraint at the transmitter. Based on imperfect channel state information (CSI) of the legitimate receivers at the transmitter, we present a worst-case robust masked beamforming algorithm. By exploiting alternating iterative optimization, the proposed algorithm recasts the non-convex optimization problem as two semidefinite program (SDP) based subproblems, which are solvable with interior-point methods. Simulation results are provided to illustrate the secrecy performance of the proposed algorithm.

This study addresses the problem of carrier frequency offset (CFO) tracking in co‐operative space‐frequency block‐coded orthogonal frequency division multiplexing (OFDM) systems with multiple CFOs. Considering that the inserted pilot tones are decayed by data subcarriers in the presence of multiple CFOs, a novel recursive residual CFO tracking (R‐RCFOTr) algorithm is proposed. This method first removes CFO‐induced inter‐carrier interference from data subcarriers, and then updates the residual CFO (RCFO) estimation of each OFDM block recursively. When used in conjunction with a multiple CFOs estimator, the proposed R‐RCFOTr can effectively mitigate the impacts from the multiple RCFOs with affordable complexity. Finally, simulation results are provided to validate the effectiveness of our proposed R‐RCFOTr algorithm, which has performance close to that of perfect CFO estimation at moderate and high signal‐to‐noise ratio, and significantly outperforms conventional CFO tracking algorithm for large CFOs.

The authors propose a novel and practical approach to estimate resources and perform a distributed admission control in multi-hop ad hoc networks based on multi-rate enabled IEEE 802.11 technology. The main challenge is to determine if there exist sufficient resources [i.e. the available bandwidth (AB)] for a new incoming flow to be admitted rather than quantifying the exact amount of existing resources. In order to determine the AB along a multi-hop path, the authors take into consideration the channel rate at each hop as well as the channel idle ratio of relevant neighbouring nodes. Furthermore, the admission control is performed at the same time as the AB is determined which minimises overhead. The proposed approach can be applied hop-by-hop in a distributed manner by the end-user, thus being suitable for wireless ad hoc networks. Analysis and simulation based on the Network Simulator version 2 (NS2) platform verify the proposed approach.

This paper addresses the problem of beamforming (BF) design for orthogonal frequency division multiplexing (OFDM) based relay networks over frequency-selective channels. Both frequency-domain (FD) BF and time-domain (TD) BF are investigated. The later requires less feedback from the destination to perform BF. The BF vectors are designed by maximizing the minimum signal-to-noise-ratio (SNR) over all subcarriers at the destination, first under the total power constraint (TPC) and then under the per-relay power constraint (PPC). We show that both TPC and PPC BF designs lead to a quasi-convex optimization problem, which can be solved by bisection search method efficiently. Simulation results demonstrate that based on max-min SNR criterion, the performance of TD-BF rapidly approaches that of FD-BF when increasing the filter length. Moreover, it is found that for TD-BF, the minimum filter length required to achieve optimum performance under PPC is longer than that under TPC.

This letter addresses the problem of beamforming design for an amplify-and-forward (AF) based two-way relaying network (TWRN) which consists of two terminal nodes and several relay nodes. Considering a two-time-slot relaying scheme, we design the optimal beamforming coefficients to maximize the sum-rate of AF-based TWRN under total relay power constraint (TRPC). Although the optimization problem is neither convex nor concave, we show that the global optimal solution can be obtained by the branch-and-bound algorithm. To address the computational complexity concern, we also propose a low-complexity suboptimal solution which is obtained by optimizing a cost function over one real variable only. Simulation results show that the proposed optimal solution outperforms existing schemes significantly. Moreover, we show that the suboptimal solution only suffers small sum-rate losses compared to the optimal solution.

In this paper we propose a novel connection admission control scheme named Rate Transition Area assisted Guaranteed Handover Scheme (GHS-RTA), which utilizes the geographical information, rate transition areas and overlap areas to intelligently decide when to block a new call. This scheme helps avoid possible inter-cell and intra-cell handover failures for HAP communications systems with adaptive modulation and coding in the physical layer. Simulation results show that the GHS-RTA can improve the average new call blocking probability greatly (by a minimum of 21.5% for the system model with the parameter values chosen) while maintaining zero inter-cell and intra-cell handover call dropping probabilities compared with Extended Time-based Channel Reservation Algorithm, and that the larger the rate transition area and the overlap area, the better the average new call blocking performance.

With the wide application of Web services, its performance has caused the extensive concern and becomes one of the key factors of determining whether it is widely applied further. Based on the idea of differentiating Web services, the paper presents a SLA-based Web services performance guarantee model to improve Web services runtime environment, and expatiates on the key techniques of realizing the model: the request class utility function, the predicting model of response time, the genetic algorithm-based resource allocation and the dynamic weight-based weighted round-robin scheduling algorithm. Experiments prove the model can guarantee the response performance of Web services requests and improve the stability of the system.

We present an "opportunistic" scheduling policy with the objective of improving delay performance for time-sensitive users in wireless networks. Since packet delay depends on both resource allocation and time-varying capacity of wireless channels, we introduce a search radius (SR) into the framework of packet fair queueing (PFQ) and employ maximum relative SNR (Max-rSNR) as scheduling rule with the purpose of providing short-term temporal fairness guarantee and improving user throughput. We make theoretical analysis of the delay performance of each user and find that each user's delay is directly related to SR, moreover the value of SR should be restricted within a limited range in order to provide better delay performance to each user. Based on this, we propose a feasible iterative algorithm to achieve an appropriate SR. We conduct an extensive set of simulations, which characterizes the performance of our scheduling scheme.

Radio resource allocation is one of the key technologies in orthogonal frequency division multiplexing (OFDM) cellular systems, where subcarriers and power are schedulable resources. In this paper, we solve the fair resource allocation problem based on the idea of the Nash bargaining solution (NBS) from cooperative game theory, which not only provides the resource allocation of users that are Pareto optimal from the view of the whole system, but also are consistent with the fairness axioms of game theory. We develop a suboptimal solution of NBS via low-pass time window filter and first-order Taylor expansion, and then propose an efficient and practical dynamic subcarriers allocation algorithm. Simulation results show that the proposed dynamic subcarriers allocation algorithm providing utility fairness and improving system capacity.