Industrial time series often display complex, non-stationary behaviors with trends, periodicity, and abrupt fluctuations. Generating high-quality synthetic data in such domains is essential for simulation, forecasting, and anomaly detection in Industrial Internet of Things (IIoT) applications. However, distributional heterogeneity, sparse failure patterns, and long-term dependencies make this task highly challenging. We introduce HNRF-TS, a rectified flow framework with hybrid noise initialization, designed for scalable and robust time series generation. The hybrid prior combines isotropic Gaussian noise with structured codes from a lightweight generative adversarial network (GAN), yielding semantically aligned and diverse latent representations. To improve sampling efficiency, we propose a bimodal adaptive strategy that allocates denser ordinary differential equation (ODE) steps at the beginning and end of the trajectory while using coarser steps in smoother middle regions. This preserves critical temporal features while lowering computational cost. We further enhance fidelity with modules dedicated to modeling trends and seasonality, which capture global drifts and periodic signals inherent in industrial data. Across multiple IIoT datasets, HNRF-TS outperforms state-of-the-art baselines, including GAN-based and diffusion-based methods. It achieves up to 75.8% reduction in Context-FID and over 60% improvement in correlation metrics on long-horizon tasks. Moreover, high-quality samples can be generated with as few as 20 sampling steps, offering significant efficiency gains without sacrificing accuracy.

This paper considers an integrated sensing and communication (ISAC) system, where two dual-functional base stations (BSs) serve their users and detect multiple targets. To improve detection accuracy while meeting communication quality of service, this paper proposes a two-phase cooperative target detection algorithm that relies on Capon-based adaptive beamforming and maximum likelihood estimation (MLE)-based hypothesis testing. Specifically, based on Capon’s detection results, the two BSs first scan targets with an omnidirectional beam and then track targets with a directional beam. Subsequently, multiple hypotheses regarding the locations of targets are established based on the detection results of the Capon method, and the MLE is employed for hypothesis testing to filter out ghost targets. Finally, simulation results show that the proposed algorithm achieves more precise angles-of-arrival estimation of multiple targets than conventional single-BS sensing, and enables high-precision localization by eliminating ghost targets.

In wireless-powered communication networks (WPCNs) augmented by reconfigurable intelligent surface (RIS), achieving high throughput while managing signaling overhead remains a critical challenge. Conventional approaches rely on instantaneous channel state information (I-CSI) for dynamic RIS beamforming, which leads to prohibitive channel estimation and feedback overhead in large-scale deployments. To address this issue, this paper proposes a novel two-timescale protocol that integrates statistical CSI for long-term RIS beamforming optimization and short-term I-CSI for optimizing resource allocation. In particular, the proposed method designs multiple RIS beamforming patterns using statistical information, while dynamically adjusting time and power allocation within each coherence interval based on effective I-CSI. An alternating optimization (AO) based algorithm is then developed to iteratively refine RIS phase shifts for both downlink energy transfer and uplink information transfer using gradient projection, and derive optimal resource allocation in closed-form expressions via Karush-Kuhn-Tucker (KKT) conditions. Simulation results validate the framework’s efficacy, demonstrating that using only 33% of the total RIS beamforming patterns can achieve 94% of the sum-rate performance of full I-CSI approaches, which provides useful guidelines for reducing the feedback overhead in the considered RIS aided WPCNs.

Multi-unmanned aerial vehicle (UAV) assisted mobile edge computing enables aerial platforms to collaboratively provide computing services for delay-sensitive applications. In such systems, information freshness must be preserved while multiple UAVs simultaneously perform trajectory control, task offloading, and resource scheduling under practical energy, computation, and quality-of-service constraints. The Age of Information (AoI) metric inherently couples these decisions over long time horizons, making the design of effective coordination policies difficult for conventional optimization techniques and reinforcement learning methods. In this work, we develop a constraint-aware multi-agent decision transformer framework, referred to as Prompt-CMADT, to address AoI optimization in multi-UAV MEC networks. By casting multi-UAV coordination as a sequential decision modeling problem, the proposed framework captures long-term temporal dependencies among heterogeneous agents while explicitly accounting for system-level constraints. Moreover, a constraint-aware prompt mechanism is designed to steer policy generation toward feasible solutions, and an opponent action prediction module is introduced to alleviate inter-UAV resource contention. Numerical results demonstrate that Prompt-CMADT consistently reduces the average AoI and overall energy consumption, while improving resource utilization, when compared with representative baseline schemes.

For ultra-dense vehicular networks, dynamic resource optimization among a large number of heterogeneous agents is rather challenging. This paper proposes a learning-based resource allocation scheme in a vehicle-assisted mobile edge computing (MEC) network, where the uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs) are equipped with the MEC servers to provide the computational offloading services to the ground users with time-varying computing demands. Each self-interested vehicle jointly optimizes its trajectory planning and data offloading policies to maximize the expectation of its locally cumulative energy efficiency under the collision and energy constraints. We model the non-cooperative interactions among the massive co-channel vehicles as a multi-population mean-field game (MPMFG), where each vehicle constructs two types of mean-field terms to model the UAVs’ and UGVs’ population distributions, respectively. We propose a multi-population mean-field parameterized deep Q network (MPMF-PDQN) algorithm to solve the equilibrium among the vehicular servers in a discrete-continuous hybrid action space. The simulation results demonstrate that the proposed algorithm significantly enhances the average energy efficiency of the vehicles compared with the baseline algorithms.

This paper investigates an intelligent reflecting surface (IRS) aided wireless federated learning (FL) system, where an access point (AP) coordinates multiple edge devices to train a machine leaning model without sharing their own raw data. During the training process, we exploit the joint channel recon figuration via IRS and resource allocation design to reduce the latency of a FL task. Particularly, we propose three transmission protocols for assisting the local model uploading from multiple devices to an AP, namely IRS aided time division multiple access (I-TDMA), IRS aided frequency division multiple access (I-FDMA), and IRS aided non-orthogonal multiple access (I NOMA), to investigate the impact of IRS on the multiple access for FL. Under the three protocols, we minimize the per-round latency subject to a given training loss by jointly optimizing the device scheduling, IRS phase-shifts, and communication computation resource allocation. For the associated problem under I-TDMA, an efficient algorithm is proposed to solve it optimally by exploiting its intrinsic structure, whereas the high quality solutions of the problems under I-FDMA and I-NOMA are obtained by invoking a successive convex approximation (SCA) based approach. Then, we further develop a theoretical framework for the performance comparison of the proposed three transmission protocols. Sufficient conditions for ensuring that I-TDMA outperforms I-NOMA and those of its opposite are unveiled, which is fundamentally different from that NOMA always outperforms TDMA in the system without IRS. Simulation results validate our theoretical findings and also demonstrate the usefulness of IRS for enhancing the fundamental tradeoff between the learning latency and learning accuracy.

Unmanned aerial vehicles (UAVs) are widely deployed as aerial base stations to provide flexible communication coverage for ground users (GUs), yet the air-ground communications remain highly vulnerable to the jamming attacks. Unlike conventional fixed-policy jammers, the intelligent jammers dynamically adapt their jamming strategies based on the observed UAV communication policies, creating significant anti-jamming challenges particularly under asymmetric information. In this paper, we formulate the strategic interactions between a UAV-mounted server and a jammer as a partially observable pursuit-evasion game, where the UAV aims to maximize the GUs' uplink rates through dynamic evasion while the jammer strategically pursues to maximize the jamming effect. The information asymmetry is explicitly modeled by considering both the jammer's hidden location from the UAV and the jammer's inability to observe the UAV's remaining energy state. To optimize the UAV's anti-jamming policy under these challenges, we propose a novel opponent-modeling based reinforcement learning algorithm, named neural fictitious self-play with dueling double deep recurrent Q network (NFSP-D3RN). This algorithm optimizes the UAV's anti-jamming policy through reinforcement learning, while maintaining robustness against non-stationarity induced by the jammer's adaptive behavior through implicit opponent modeling. Extensive simulations demonstrate that our proposed algorithm achieves superior anti-jamming performance compared with the benchmarks under unknown jammer locations, with results approaching the upper bound of perfect location knowledge.

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accurate channel state information (CSI) that is generally difficult to obtain due to the large number of RIS channels. The joint design of CSI acquisition and subsection RIS phase-shifts remains a significant challenge in dynamic environments. In this paper, we propose a diffusion-enhanced decision Transformer (DEDT) framework consisting of a diffusion model (DM) designed for efficient CSI acquisition and a decision Transformer (DT) utilized for phase-shift optimizations. Specifically, we first propose a novel DM mechanism, i.e., conditional imputation based on denoising diffusion probabilistic model, for rapidly acquiring real-time full CSI by exploiting the spatial correlations inherent in wireless channels. Then, we optimize beamforming schemes based on the DT architecture, which pre-trains on historical environments to establish a robust policy model. Next, we incorporate a fine-tuning mechanism to ensure rapid beamforming adaptation to new environments, eliminating the retraining process that is imperative in conventional reinforcement learning (RL) methods. Simulation results demonstrate that DEDT can enhance efficiency and adaptability of RIS-aided communications with fluctuating channel conditions compared to state-of-the-art RL methods.

The rapid development of 5G technology leads to increasing energy consumption in base stations (BSs). For the vision of green and sustainable communications, we propose a scheme aimed at reducing BS energy consumption through symbol shutdown. This approach reduces BS energy consumption while ensuring the quality of service (QoS) for user equipments (UEs). Our scheme considers highly dynamic channel conditions and formulates a joint optimization problem, including BS shutdown and power allocation, constrained to the long-term average rate demand of all UEs. To address this problem, we design a Lyapunov method-based algorithm (LMBA) that transforms the mixed integer and dynamic optimization problem into a more tractable form and solves it using the Karush-Kuhn-Tucker conditions. Simulations indicate that our proposed strategy significantly reduces the BS energy consumption and the LMBA outperforms several benchmark algorithms.

The cellular Internet of Things (IoT) enhanced by federated learning (FL) is a potential paradigm to leverage the vast amount of data generated by the IoT devices and offer various intelligent applications. Through its distributed learning manner, the privacy and delay problems of the learning process are well handled. Nevertheless, in the cellular IoT, FL requires multiple rounds of model parameters exchanging between the parameter server and multiple clients over unstable wireless links, which largely constrains the communication efficiency. Regarding this problem, we propose the training data cost ratio to evaluate the communication efficiency, and then by maximizing this metric, client scheduling, transmitting power, and bandwidth are jointly formulated. The formulated problem is decomposed via problem transformation and derivations, and then, the Lagrange method and greedy-based algorithms are developed to solve the subproblems efficiently. Simulation results verify the advantages of our algorithm in communication efficiency improvement. Moreover, it reveals that the proposed metric and joint optimization substantially obtain superior tradeoff between learning performance and resource consumption compared to the client number oriented optimization.

Intelligent fault diagnosis (IFD) plays a crucial role in ensuring the safe operation of industrial machinery and improving production efficiency. However, traditional supervised deep learning methods require a large amount of training data and labels, which are often located in different clients. Additionally, the cost of data labeling is high, making labels difficult to acquire. Meanwhile, differences in data distribution among clients may also hinder the model’s performance. To tackle these challenges, this paper proposes a semi-supervised federated learning framework, SSFL-DCSL, which integrates dual contrastive loss and soft labeling to address data and label scarcity for distributed clients with few labeled samples while safeguarding user privacy. It enables representation learning using unlabeled data on the client side and facilitates joint learning among clients through prototypes, thereby achieving mutual knowledge sharing and preventing local model divergence. Specifically, first, a sample weighting function based on the Laplace distribution is designed to alleviate bias caused by low confidence in pseudo labels during the semi-supervised training process. Second, a dual contrastive loss is introduced to mitigate model divergence caused by different data distributions, comprising local contrastive loss and global contrastive loss. Third, local prototypes are aggregated on the server with weighted averaging and updated with momentum to share knowledge among clients. To evaluate the proposed SSFL-DCSL framework, experiments are conducted on two publicly available datasets and a dataset collected on motors from the factory. In the most challenging task, where only 10% of the data are labeled, the proposed SSFL-DCSL can improve accuracy by 1.15% to 7.85% over state-of-the-art methods.

In this article, a novel multiuser multiple-input-multiple-output (MIMO) communication system for Internet of Things (IoT) is proposed, where the aerial reconfigurable intelligent surface (ARIS) and nonorthogonal multiple access (NOMA) are used as the sum rate enhancement pathway. The base station (BS) has multiple antennas that transmit superimposed signals to multiple users. The passive ARIS serves as a flexible transmit relay to reduce path loss and improve channel gains. Users are divided into several groups based on their channel status, each sharing a radio frequency (RF) chain. To maximize the sum rate of all users, the placement of ARIS, the passive/active beamforming design and the power allocation among users are jointly optimized. As the joint optimization for user grouping, passive/active beamforming and power distribution is formulated as a mixed-integer nonlinear program (MINLP) which is nonconvex and coupled and hence, obtaining an optimal solution is challenging. In this article, the problem is decoupled into three subproblems and solved alternately efficiently. The numerical results demonstrate that the suggested MIMO-ARIS-NOMA system can achieve higher sum rate performance than traditional schemes.

Nowadays, covert communication is envisioned as a promising and secure method of delivering private information. However, higher bit error rates, limited data rates, and vulnerability to advanced machine learning detection methods significantly challenge the application of covert communication. In this paper, we propose a multiple carrier index keying orthogonal frequency division multiplexing (MCIK-OFDM) based covert communication system aided by a chaotic modulation scheme to improve covert data rate and covertness. First, we propose a covert information embedding method by dynamically selecting the activation or deactivation of a subcarrier to embed covert bits according to a previously negotiated covert key between the transmitter and receiver. Then, the chaotic modulation scheme is developed to mask transmitted signals with generated chaotic signals. Moreover, we propose an adversarial machine learning-based (AML) perturbation algorithm to resist the eavesdropper’s detection of covert signals. Furthermore, the closed-form bit error rate (BER) and the achievable covert rate of the proposed covert communication system are derived. Numerical and simulation results demonstrate that the BER of the proposed MCIK-OFDM-based hybrid chaotic covert communication system is much lower than that of conventional chaotic communication systems. In addition, the proposed AML perturbation algorithm can more effectively protect covert communication from being detected by supervised and unsupervised machine learning methods compared to traditional algorithms.

The emergence of new application scenarios has enabled integrated sensing and communication (ISAC) as one of the potential technologies of 6-th generation mobile communication (6G). To meet both communication efficiency and sensing efficiency, a satisfactory ISAC waveform is essential. In this paper, we primarily investigate the impact of different modulation schemes on sensing performance for UAV-to-ground scenarios. Firstly, we analyze the sensing performance differences of modulation schemes in the sensing algorithm based on orthogonal frequency division multiplexing (OFDM) systems. Secondly, we examine the periodic auto-correlation functions (PACFs) with different modulation schemes and modulation orders. We observe that the waveforms modulated by phase shift keying (PSK) exhibit the lower sidelobes compared to waveforms modulated by quadrature amplitude modulation (QAM). Finally, we simulate the probability of detection (Pd) with different modulation schemes for UAV-to-ground scenarios. Numerical results demonstrate that the modulated waveform with constant modulus exhibit superior sensing performance. For the modulated waveform with non-constant modulus, the higher modulation orders result in the poorer sensing performance. This inspires us to change the sensing performance by designing the power spectrum of the modulated waveform with non-constant modulus. This work is helpful for the waveform design and performance analysis of ISAC.

With the rapid development of cutting-edge technologies such as software-defined networking, edge computing, and deep learning (DL), the application of the field of Industrial Internet of Things (IIoT) has been deepening, especially in the areas of fault diagnosis, defect detection, and production management, which has shown great potential. Federated learning (FL) is a collaborative model training approach that allows multiple clients to work together while maintaining data privacy. This method is particularly useful for DL methods in industrial surface defect classification, which often require a large amount of training data that can be hard to gather due to its distributed nature across various sources. However, the aggregated model in federated learning may not perform well when there is a discrepancy between the training dataset (source domain) and the testing dataset (target domain), as well as when individual users face data scarcity. To counter these challenges, we propose a novel federated transfer learning framework with multi-scale aggregation (FTL-MSA) for surface defect classification in the IIoT system. A dynamic central loss function, which takes into account both intra-instance and inter-instance contrasting, is proposed to enhance the model's accuracy. Furthermore, we introduce a multi-scale model aggregation technique for FL. This technique considers the distances between the source domain and the target domain at multiple scales, which utilizes the Jensen-Shannon distance for statistical consistency, and the cosine distance for directional consistency, thereby effectively mitigating the impacts of domain differences. Empirical validation on two public steel defect datasets shows that our FTL-MSA framework outperforms state-of-the-art methods, achieving accuracy improvements of 3.12%-12.51%.

Blockchain technology has shown great application potential in many fields such as finance, supply chain, and the Internet of Things. As blockchain technology keeps evolving, the issue of insufficient scalability has progressively turned into a crucial bottleneck impeding its further progress. The application of sharding technology to enhance the scalability of blockchain systems has attracted considerable interest from the research community. However, the majority of sharding solutions suffer from a number of problems, including high ratio of cross-shard transactions, prolonged transaction confirmation latency, and unbalanced load among shards. To solve these problems, we propose a blockchain sharding method based on dual-constraint label propagating. This sharding method mainly depends on two constraints in the label propagation process to determine whether to perform partition transfers on blockchain accounts. Constraint 1 is to consider the correlation among accounts when splitting accounts. This constraint serves to minimize the cross-shard transactions ratio. Constraint 2 is that the load factor of the blockchain sharding system after the tag update cannot exceed the load factor before the update. This can bring the load of each shard to an almost consistent state, thereby reducing transaction confirmation latency. To verify the effectiveness of our proposed method, we compare it with the Metis and Monoxide algorithms in three aspects: cross-shard transaction ratio, transaction throughput, and transaction confirmation latency. Furthermore, we examine the influence of the load distribution across individual shards within the blockchain network on both throughput and transaction confirmation latency. The experimental results show that our proposed method makes the load among shards more balanced, thereby reducing the ratio of cross-shard transactions, decreasing the transaction confirmation latency, and increasing the transaction throughput.

As a potential technique, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) can provide flexible coverage and computing services for real-time applications such as emergency search, traffic control and disaster rescue. In this paper, we investigate a freshness sensitive multi-UAV assisted MEC system where tasks arrive stochastically. The system aims to minimize the age of information (AoI), subject to the constraints on computation offloading, trajectory control and communication resource allocation. Due to the dynamic environment and the coupling of variables, we develop a multi-agent reinforcement learning (MARL) scheme, in which a federated updating method is introduced. Through our scheme, smart mobile devices, UAVs and cloud center can collaborate to learn interactive policies. Simulation results validate that our scheme outperforms local computing, remote computing, and centralized solutions in terms of both the average AoI and convergence.

Incentive mechanism is an effective approach to encourage user participation in the Federated Learning (FL) process and improve training efficiency. However, current research often focuses on scenarios with complete information or weak information asymmetry between the server and users, and few studies consider incentive mechanism design in strong asymmetric information scenarios. Meanwhile, most works assume that users' resource contributions to model performance are independent of each other, which is not consistent with practical situations. To tackle these challenges, we design an incentive contract tailored for scenarios with strong information asymmetry. Our contract leverages the probability distribution of user types to ensure its appropriateness. Furthermore, taking into account the correlation of the users' resource contributions, we propose an iteration algorithm to determine the set of optimal contract items that satisfy the constraints of individual rationality (IR) and incentive compatibility (IC). Our simulation results show that our contract can effectively motivate multiple users to take part in the training process, enabling the server to achieve utility close to those in weak asymmetric information scenarios while maintaining robustness.

Abstract In recent years, the Integrated Satellite Aerial Terrestrial (I‐SAT) network has garnered significant attention as an innovative and integrated communication system. However, it still encounters interference in the face of the complex external environment. In this context, reconfigurable intelligent surface (RIS) provides a key way of solving this problem and effectively improves the performance and stability of the I‐SAT network. This article considers the combination of unmanned aerial vehicle (UAV) and RIS and proposes a novel architecture for sub‐connected active RIS (ARIS) under the energy consumption constraints of UAV and ARIS. The authors first provide a UAV‐ARIS based position prediction strategy for the vehicle. Then, a joint RIS phase shift, amplification and UAV trail optimization algorithm is proposed to pursue a high achievable rate. The interference between each link and the total energy consumption are all taken into consideration. In addition, a deep deterministic policy gradient (DDPG) algorithm is utilized for the optimization problem, and achieves convergence in continuous action space. Finally, the simulation results affirm the precision of the proposed method in significantly enhancing performance compared to other schemes.

This article proposes a new framework of aerial reconfigurable intelligent surface (ARIS) enhancing the nonorthogonal multiple access (NOMA) system. The base station (BS) transmits superimposed signals to multiple users with different channel gains through ARIS which can flexibly change channel conditions and perform intelligent NOMA operations. It ensures that our system can perform well in providing services to multiple users simultaneously. In this system, the placement of the unmanned aerial vehicle (UAV) is jointly optimized along with the AIRS passive beam and the multiuser power allocation in order to maximize the communication sum rate. Since the joint optimization problem is nonconvex and coupled, it is hence disintegrated into three subproblems and it is solved alternately through the successive convex approximation (SCA). Moreover, semi definite programming (SDP) is used to deal with the rank one constraint of RIS reflection matrix and comparisons are made using particle swarm optimization (PSO). The numerical results show that the proposed ARIS-NOMA framework can achieve better sum rate performance than traditional NOMA with fixed RIS and OMA-ARIS.

Reconfigurable intelligent surface (RIS) is envisioned as a potential technology to improve spectrum efficiency with low energy consumption. Moreover, the spectrum reuse technologies are also extensively applied in various networks, such as vehicle-to-vehicle (V2V) in transportation system, machine-to-machine (M2M) in industrial Internet of Things (IIoT) system and so on. In this paper, we investigate the performance of the general RIS-assisted device-to-device (D2D) communication. We consider the limited feedback system where the channel state information (CSI) is imperfectly known. The analytical expression of the ergodic achievable rate (EAR) for beamforming (BF) strategy and interference cancellation (IC) strategy are derived. For exploring engineering design, we investigate the tight bounds of EAR with much simpler form. Then, the EAR gain brought by RIS compared to the traditional D2D system is analyzed. Further, four specific scenarios are discussed in detail, including the weak interference case, the high SNR case, the large number of BS antennas case and the large number of reflective elements case. We derive the EAR approximations of these four cases and explore a series of insights. Numerical Results shows the perfect agreement between the analytical results and simulations.

Reconfigurable intelligent surface (RIS) is a promising paradigm for implementing intelligent reconfigurable wireless propagation environments in the 6G era. However, most of the existing studies focus on utilizing RIS deployed on buildings to provide services to users or constructing a RIS-assisted system framework for static users, which greatly limited application in real-time changing vehicular communication environments. As a result, combining unmanned aerial vehicles (UAVs) with RIS (RIS-UAV) plays a crucial role in various wireless networks due to their high mobility. To maximize the communication rate between base station (BS) and mobile vehicle, we propose a position prediction strategy for vehicles that facilitates real-time adjustment of UAV trajectories and RIS phase shifts, enhancing communication in dynamic environments. Deep reinforcement learning (DRL) algorithm is utilized to solve the above question, which achieves a good effect on convergence in continuous action space. Simulation results demonstrate that compared with benchmark schemes, the algorithm we suggested has significant performance gains, that is to maximize the communication rate under system constraints and guarantee the reliability of the communication.

This paper investigates the achievable rate and spectral efficiency (SE) of an uplink cell-free massive multiple-input multiple-output Internet-of-Things (mMIMO-IoT) network over Ricean fading channels, where both access points and user equipments (UEs) are equipped with multiple antennas. We derive tight closed-form expressions for the lower-bound achievable rate and SE under maximum ratio combining and imperfect channel state information (CSI). Moreover, we propose a target-signal-to-interference-plus-noise-ratio-tracking opportunistic power control (TOPC) algorithm with gradual soft UE removal to mitigate the effects of unsupported UEs. The proposed TOPC algorithm is fully distributed, as each UE updates its transmit power based on local CSI. Numerical results show that adding more antennas at the UEs can enhance the achievable rate, but may degrade the achievable SE due to the increased pilot overhead. Moreover, the Ricean fading channels offer much higher achievable rate and SE than the Rayleigh fading channels, and our TOPC algorithm exhibits satisfactory performance in various aspects.

Against the background of 6G communication and big data, more and more attention have been paid to the communication reliability and resource efficiency. Reconfigurable intelligent surface (RIS) is envisioned as a potential technology to improve the communication environment by controlling the electromagnetic wave propagation. A majority of related researches use the central limit theorem (CLT) to implement the analytical evaluation, which results in inaccuracy for the case with small number of reflective elements. In this letter, we investigate the outage behavior of RIS-enabled downlink cellular networks where exist several device-to-device (D2D) pairs under the general fading channels, i.e., Nakagami-m fading channels. We propose a comprehensive solution to evaluate the outage probability for all the cases with different numbers of reflective elements. Our solution utilizes the multivariate Fox's H-function and proposes the outage expressions in closed form. Finally, The accuracy of the closed-form outage probability is verified in simulation section for different cases of system configurations.

Bistatic backscattering communication (BackCom) allows passive tags to send signals over long distances, but requires proximity to the carrier emitter (CE). Otherwise, the system would suffer from severe path loss, which need to be compensated by a high transmitting power of the CE. This paper presents an inventive BackCom system that makes use of the Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS), which could improve the deployment flexibility of BackCom devices at both sides of STAR-RIS simultaneously. With the ensurance of the BackCom performance, we focus on minimizing the CE transmitting power through optimizing STAR-RIS phase shift and power splitting coefficient of tags. Numerical results reveal that the CE transmitting power in bistatic BackCom systems can be greatly saved with the assistance of STAR-RIS.

Mobile edge computing (MEC) is a promising paradigm to handle the mismatch between computation-intensive applications and resource-limited devices. Nevertheless, as most Internet of Things (IoT) terminals are battery-limited, the computation gain of MEC may be compromised due to insufficient battery energy for task offloading. Wireless power transfer (WPT) and full duplex (FD) communications are economical charging and transmission methods for battery-limited IoT terminals. However, when integrating wireless power transfer and FD into MEC, the incentive problem should be jointly addressed with task offloading, because the WPT facilities and their powered IoT nodes belong to different service operators. In this paper, we investigate the efficiency of WPT from the perspective of battery saving, and propose an efficient wireless powered task offloading and incentive mechanism in FD MEC-enabled cellular IoT networks. The battery saving efficiency, which addresses both the total cost of WPT and saved energy of battery, is proposed as the performance metric. By adopting this metric as the utility function of the network operator (NO), the task offloading and incentive problem are jointly formulated as a Stackelberg game. We then propose an efficient alternating direction iteration-based algorithm to solve its equilibrium efficiently. Simulation results demonstrate the benefits of our algorithm in battery saving by comparisons with utility oriented benchmarks. Moreover, it reveals the tradeoff between the utility of NO and battery saving, which verifies the positive effects of FD communications and WPT in improving the efficiency of battery saving.

Energy consumption is one crucial aspect in IoT and healthcare applications. Reconfigurable intelligent surface (RIS) is composed of man-made passive reflective elements which can configure the channel environment with lower energy consumption. In this paper, we focus on the RIS-assisted D2D networks. We obtain analytical closed-form expressions for the outage performance. Based on this, we then discuss the performance under high SNR case, as well as weak interference case. The corresponding closed-form simpler approximations are also presented. Due to the existence of interference, 0 order outage diversity is obtained. Numerical results show the agreement between Monte Carlo simulations and analytical results in various network configurations.

This paper studies a new framework of aerial reconfigurable intelligent surface (ARIS) assisted non-orthogonal multiple access (NOMA) for wireless communication systems. The base station transmits superimposed signals to multiple users with different channel gains through ARIS which can be deployed flexible. The placement of the unmanned aerial vehicle (UAV) and the passive beamforming of the ARIS are jointly optimized to maximize the sum rate. The non-convex problem is decomposed into two subproblems and solved alternately through the successive convex approximation (SCA). The numerical results show that our proposed NOMA-ARIS framework can achieve better sum rate performance than traditional NOMA with fixed RIS and OMA-ARIS.

It is an irreversible trend to build a green and sustainable vehicular network facing with the dramatic increase in urban traffic. Reducing energy consumption has been an important aspect for green transportation. Reconfigurable intelligent surface (RIS) is considered as a promising technology to enhance the communication quality with higher energy efficiency. In this paper, we focus on the RIS-assisted vehicular networks. We obtain the closed-form analytical expressions for outage probability, ergodic achievable rate and average energy efficiency. A series of insights are further explored. Based on these, we discuss the performance under high SNR case, as well as, weak interference case. And then, the approximations in simpler form expressions are provided for each case, respectively. Outage diversity order and high SNR rate slope are also investigated. In addition, we propose a power allocation algorithm to maximize the ergodic achievable sum rate guaranteeing the outage probability and average energy efficiency. Numerical results show that our analytical results agree well with the Monte Carlo simulations in various network configurations. Besides, our proposed power allocation scheme significantly enhances the ergodic achievable sum rate compared with the equal power strategy.

This paper presents an analytical performance investigation of both beamforming (BF) and interference cancellation (IC) strategies for a device-to-device (D2D) communication system underlaying a cellular network with an M-antenna base station (BS). We first derive new closed-form expressions for the ergodic achievable rate for BF and IC precoding strategies with quantized channel state information (CSI), as well as, perfect CSI. Then, novel lower and upper bounds are derived which apply for an arbitrary number of antennas and are shown to be sufficiently tight to the Monte-Carlo results. Based on these results, we examine in detail three important special cases including: high signal-to-noise ratio (SNR), weak interference between cellular link and D2D link, and BS equipped with a large number of antennas. We also derive asymptotic expressions for the ergodic achievable rate for these scenarios. Based on these results, we obtain valuable insights into the impact of the system parameters, such as the number of antennas, SNR and the interference for each link. In particular, we show that an irreducible saturation point exists in the high SNR regime, while the ergodic rate under IC strategy is verified to be always better than that under BF strategy. We also reveal that the ergodic achievable rate under perfect CSI scales as log2M, whilst it reaches a ceiling with quantized CSI.

This study investigates the outage probability of device‐to‐device communication assisted by a relay node utilising a one‐way amplify‐and‐forward relaying strategy. The authors assume that all the terminals are equipped with a single antenna and all the users know perfect channel state information. They first derive the exact closed‐form expression for characterising the outage probability performance of the system. They subsequently discuss several special scenarios and obtain the asymptotic results for each of the considered scenarios. The results can be easily computed with only the channel statistics. Based on the analysis in the high signal‐to‐noise ratio regime, closed‐form power allocation policies are developed to improve the outage probability performance. The author's analytical results are validated via Monte Carlo computer simulations.

This paper studies the outage probability of device-to-device (D2D) communication aided by another D2D user using the two-way amplify-and-forward (AF) relaying protocol. We first discuss the outage behavior under strong and weak interference from the cellular network. Then the exact expressions for the outage probability under the two cases are derived. Based on these results, we give tight approximations in the high signal-to-noise (SNR) regime under the two cases. Numerical results show that the outage behavior for the relay aided D2D link can be greatly enhanced without extra power. Analytical results are validated via comparisons with the Monte-Carlo simulations.