Massive Machine-Type Communication (mMTC) is a critical Internet of Things (IoT) scenario in 5G and beyond 5G (B5G) wireless networks, characterized by a vast number of devices, smaller data packets, sporadic transmission, and diverse latency requirements. Massive multiple-input-multiple-output (MIMO) technology allows multiple user equipments (UEs) to transmit their data simultaneously over the same resource block, making it a promising technology to support mMTC. However, when massive UEs attempt to access the massive MIMO network simultaneously, the network will experience severe overload. To address this challenge, we propose a Deep Deterministic Policy Gradient (DDPG)-based delay-aware Access Class Barring (ACB) dynamic access control scheme for mMTC in massive MIMO networks. In this scheme, we model the access blocking probability as a function of latency sensitivity for each active UE with a shared parameter, ensuring that the closer the current delay is to a UE’s delay budget, the higher the access priority of that UE. We then propose a DDPG-based algorithm to optimize the access blocking probability and the access blocking time in ACB. Simulation studies demonstrate that, compared with the baseline methods, the proposed scheme significantly increases the number of successful access UEs while maintaining access delays within the budget constraints.

In IoT-based water quality monitoring, traditional three-dimensional (3D) fluorescence spectroscopy is difficult to deploy directly on terminal sensors due to its large size, high cost, and complex structure, despite its rich information being highly advantageous for water pollution detection. In this work, a novel approach is proposed through designing a compact sensor integrated with a super-resolution reconstruction algorithm to meet the requirement of entire 3D fluorescence spectra in cloud deployment of monitoring network at minimal cost. In the design of the terminal sensor, multiple LEDs with different wavelengths serve as excitation light source and multiple filters are used to switch emission wavelengths, enabling the acquisition of a sparse 3D fluorescence spectrum. In the reconstruction algorithm, a point spread function (PSF) is employed as a constraint module to simulate the degradation process of spectra from high to low resolution by modeling the spectral characteristics of the optical components responsible for degradation, including LEDs and optical filters, which can enhance the authenticity of the reconstructed spectra. Furthermore, a joint training strategy is introduced for better robustness by jointly optimizing the parameters of PSF and deep learning network. Experiment results demonstrate that the proposed method achieves the highest reconstruction quality (PSNR: 47.57 dB, SSIM: 0.9903) and pollutant identification accuracy (98.1%) compared with seven other reconstruction methods. The proposed approach can obtain high-resolution and entire 3D fluorescence spectra for water pollution detection with low manufacturing cost and minimal data, thereby meeting the practical application requirements in the field of IoT-based water quality monitoring.

Electrical Capacitance Tomography (ECT), as a rapidly developing process tomography technique, has been widely applied in multiphase flow monitoring, industrial process control, and medical imaging. However, due to the “soft-field” effect, the ill-posed nature of the inverse problem, and limitations in data acquisition. Existing reconstruction methods generally suffer from large reconstruction errors, low accuracy, blurred edges, and pronounced artifacts. To address these issues, this paper proposed a reconstruction method based on fractional-order particle filtering and image super-resolution. Firstly, a fractional-order formulation is introduced into the system equation to better characterize the dynamic behavior of the ECT system, by exploiting the short-term memory property of fractional-order systems, thereby accelerating convergence in the search space. Subsequently, the particle filter is employed to effectively alleviate the “soft-field” effect of the ECT system. By directly approximating the posterior probability distribution of the system state through Monte Carlo sampling, more informative particles can be selected, which enhances reconstruction accuracy and stability. Finally, an image super-resolution model based on Lorentzian function fitting is adopted, where the point spread function is estimated and combined with the Lucy–Richardson algorithm to achieve high-resolution imaging and mitigate edge blurring.In the experimental study, a three-dimensional simulation model was established using COMSOL, and a 12-electrode hardware platform was constructed for validation under various typical flow patterns. The results demonstrate that the proposed method achieves superior image quality while maintaining low computational cost (less than 0.3 s), providing an effective solution for ECT imaging in complex industrial environments.

In the oil and gas related fields, three-phase distribution flow in pipelines is one of the important aspects that need to be continuously monitored in the Industrial Pipeline Internet of Things (IoT). The dual-modality system of Electrical Capacitance Tomography (ECT) and Electromagnetic Tomography (EMT) can reconstruct the three-phase distribution within a pipeline’s cross-section. However, due to mutual interference among three-phase flow media and noise effects, the reconstruction quality of ECT and EMT images is degraded. In this paper, an ECT-EMT image fusion method based on cross-sensitive field optimization and super-resolution preprocessing is proposed. Firstly, unimodal ECT image reconstruction generates virtual image in solid-phase fluid regions, leveraging this information, a cross-sensitive field optimization method combined with the YOLOv8 network is proposed to improve the EMT reconstructed image quality. Compared with traditional fixed-sensitivity-field EMT reconstruction methods, the correlation coefficient increased by an average of 3.8%. Then, a Gaussian second-order derivative super-resolution method is introduced, which uses a Gaussian second-order derivative function to fit fuzzy kernel parameters and deconvolves the degradation matrices of ECT and EMT images, solving the edge-blurring problem in their fusion. Compared to traditional deblurring methods such as Wiener filtering and single Gaussian kernel modelling, PSNR improved by an average of 1.4 dB, while SSIM improved by an average of 0.03. Finally, an image fusion method based on Latent Low-Rank Representation is proposed by extracting global and local feature information from super-resolution preprocessed ECT and EMT grayscale matrices, respectively. Compared to mainstream pixel-level fusion methods such as wavelet transforms, it resolves the issue of performance degradation in traditional fusion methods when observational data is insufficient. All validations were conducted using simulation data from COMSOL, with sensitivity field optimisation adapted to the complex flow patterns of three-phase flow scenarios. The super-resolution module addressed reconstructed images exhibiting low signal-to-noise ratios and blurred edges, whilst the LatLRR fusion method maintained stable fusion performance even under small-sample conditions.

Non-orthogonal multiple access (NOMA) enables multiple terminal devices to simultaneously share wireless resources, providing efficient computing offloading services for wireless devices in networks that integrate unmanned aerial vehicles (UAVs) with mobile edge computing (MEC). However, the broadcast characteristics of UAV line-of-sight (LoS) communication introduce serious security issues for NOMA-based UAV-MEC systems, especially when facing an aerial warden. To address this issue, we propose a covert communication scheme for NOMA-based UAV-MEC systems against an aerial warden, where the aerial warden monitors the task offloading behavior of terminal devices. In the proposed scheme, the average computing capacity is maximized by jointly optimizing the UAV trajectory and system resources while ensuring the covert performance requirements. Firstly, considering the terminal devices have a fixed number of computing tasks, a block coordinate descent (BCD)-based algorithm is proposed, which decomposes the non-convex original problem into several subproblems and solves them iteratively. Secondly, considering the case of dynamic tasks arrival at terminal devices, we propose a double-deep Q-learning (DDQN)-based algorithm, where the optimal strategy for trajectory planning and resource allocation is obtained. Simulation results demonstrate that the proposed scheme using two algorithms outperform their respective baselines.

Unmanned aerial vehicle (UAV)-assisted federated edge learning (FEL) has emerged as a promising paradigm for privacy-preserving data processing in resource-constrained environments. However, the reliance on open wireless communication inherently exposes the system to eavesdroppers, who can eavesdrop and exploit shared model updates to reconstruct sensitive data, posing serious threats to the privacy and security. To address this challenge, we propose a privacy-aware UAV-assisted FEL framework that integrates adaptive local differential privacy (DP) into the model upload process, where user-specific noise is injected into local updates to prevent eavesdroppers from reconstructing sensitive data. To further enhance security and privacy performance, an indicator named value of privacy and security (VoPS) is designed to characterize the combined connection between training cost and privacy leakage. Furthermore, limited system resources including bandwidth allocation, user CPU frequency, DP noise scale, and UAV CPU frequency are collaboratively optimized under considering leakage threshold and heterogeneous computing constraints. Then, a deep deterministic policy gradient (DDPG)-based resource collaboration and secure aggregation scheme is proposed to solve the problem, in which the continuous optimization strategy is intelligently generated through the interaction between the agent and the dynamic privacy-aware UAV-assisted FEL system. Simulation results validate the effectiveness of the proposed scheme in enhancing the security and privacy performance of the system.

Unmanned aerial vehicles (UAVs) have been applied to various tasks in the low-altitude economy (LAE) with the advantages of high mobility, low costs, and flexible deployment. However, due to the broadcast nature of wireless channels and the increasing number of UAVs, the security of UAV control information and the spectrum resource utilization face significant challenges and threats. Therefore, in this paper, we investigate the secrecy performance of UAV short-packet control information transmission networks based on rate-splitting multiple access (RSMA) with the presence of multiple eavesdroppers. Moreover, we consider and analyze the impacts of both imperfect channel state information (CSI) and successive interference cancellation (SIC) in a more realistic scenario. Considering both large-scale fading and Nakagami-msmall-scale fading, the closed-form expression of the average effective secrecy sum rate is derived utilizing stochastic geometry and the Gauss-Chebyshev quadrature. Considering that the private stream can be concealed within the high-power common stream, an optimization problem is formulated to maximize the common rate by jointly optimizing the blocklength and power allocation coefficients to enhance security. The block coordinate descent (BCD) algorithm is adopted to solve this problem. Finally, simulation results demonstrate the accuracy of the analysis and the effectiveness of the proposed scheme.

Unmanned aerial vehicle (UAV)-assisted ground robot networks (UGRNets) are playing an increasingly critical role in a wide range of time-sensitive and mission-critical applications, such as environmental monitoring, infrastructure inspection, and emergency response. UGRNets require not only low-latency communication but also high spatial awareness to ensure effective coordination and decision-making. This paper proposes a unified spatiotemporal framework that evaluates and enhances the quality of updated information in UGRNets from both temporal and spatial dimensions. On the temporal side, we develop a martingale-theory-based prediction method for the delay violation probability bound (DVPB), coupled with a novel joint decay rate model to accurately characterize latency violations in heterogeneous multi-hop communication UGRNets. On the spatial side, we introduce the use of Wasserstein distance to quantify and improve the spatial completeness of robotic coverage. By integrating these metrics, we formulate a spatiotemporal optimization problem that jointly minimizes DVPB and maximizes spatial completeness, enabling robotic agents to adapt their information collection strategies accordingly. Numerical results demonstrate that the proposed framework significantly improves information timeliness and spatial completeness in heterogeneous and dynamic UGRNets scenarios, thereby providing practical insights for real-world deployment.

Effective provision of integrated sensing, communication, and computation (ISCC) services in future networks will inevitably increase their operational complexity. The distinct requirements of diverse tasks for tailored ISCC devices further exacerbate the challenge of adaptively allocating constrained resources among concurrent tasks. To address these difficulties, a task-specific joint resource orchestration scheme is proposed in this paper to enhance the effectiveness of ISCC operation and heterogeneous tasks completion. Specifically, the completion of concurrent heterogeneous tasks by different devices relies on the task-specific sharing of limited resource among sensing, real-time data computing and delay-tolerant data processing. Consequently, a value of multi-task completion (VoC) indicator is designed to connect and balance among the diverse demands from concurrent tasks, including computing rate, time delay, and sensing performance. The VoC is then maximized by collaborative optimization of multi-dimensional resources, including transmit beamformer, local and offloading CPU-cycle frequency, data factor assignment and computation capacity. To solve this challenging optimization problem with the lack of close-form solution and coupling of multi-variables, we first transform it into an equivalent form that is tractable to handle. Next, the problem is decomposed into several subproblems, which can be approximately solved by iterative updates. Simulation results demonstrate the performance enhancement of the proposed scheme is superior to the benchmarks.

The rapid advancement of AI-generated content (AIGC) has enhanced the Artificial Intelligence of Things (AIoT) by offering a novel approach to content generation and creation. However, the heterogeneity of AIGC services and the large scale of AIGC models present significant challenges for providing these services. In this paper, we propose an edge-cloud collaborative framework to facilitate the provisioning of heterogeneous AIGC services. In this framework, we focus on three kinds of representative AIGC services, including lightweight AIGC services, computation-intensive AIGC services, and preprocessing-based AIGC services. We jointly optimize resource allocation and AIGC model deployment at an edge server to minimize the service delay for AIoT devices. The delay minimization problem involves mixed-integer nonlinear programming, which is inherently complex. To address this issue, we propose a dual-layer optimization algorithm that decouples the problem into an inner-layer resource allocation subproblem and an outer-layer model deployment subproblem. These subproblems are then addressed using the Karush-Kuhn-Tucker conditions and a cross-entropy-based technique. Finally, simulation results demonstrate the effectiveness of our proposed joint optimization scheme, which achieves an average performance improvement of approximately 23.2%.

Reliable and ultra-low-latency communication is essential for multiagent communication involving autonomous ground robots and unmanned aerial vehicles (UAVs). These mobile platforms form dynamic, multi-hop heterogeneous networks where timely delivery of critical information, such as health status or hazard detection, is vital. While average delay is commonly used, it fails to reflect the risk of rare but critical delay violations, which delay violation probability bound (DVPB) quantifies and helps predict for better planning and control. In this paper, we propose a martingale-based framework to predict the DVPB in the UAV-assisted ground robot communication networks. We specifically introduce a joint decay rate derivation method and define a stability condition to derive closed-form expressions for end-to-end DVPB in multi-hop heterogeneous networks. Simulation results demonstrate that the proposed method significantly outperforms conventional moment generating function in stochastic network calculus (MGF SNC) approaches under various network loads, hop counts, and data types. The proposed martingale-based DVPB offers accurate and reliable delay guarantees for real-world emergency communication networks.

In this work, we investigate the frequency synchronization and system performance in massive multiple-input multiple-output (MIMO) systems with one-bit analog-to digital converters (ADCs). First, we tackle the challenges arising from severe multi-user interference (MUI) and the non-linearity inherent in one-bit ADCs in orthogonal frequency division multiplexing (OFDM) based on Bussgang decomposition and receive beamforming. To assess the accuracy of the CFO estimation, we analyze its theoretical mean square error (MSE) and investigate how quantization noise influences synchronization precision. Additionally, we introduce a multi-user inference (MUI)-plus-noise whitening technique to mitigate the correlation of the equivalent noise. Finally, we derive an approximate expression for the uplink achievable rate using maximal-ratio combining (MRC) detection scheme. Extensive numerical simulations confirm the effectiveness of the proposed approach.

Abstract Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

This article presents a novel under-sampling method for ECG signals, aimed at reducing the sampling rate and power consumption in IoT-based ECG wearable devices. The key contribution addresses the common issue of model mismatch in existing methods, which negatively impacts signal reconstruction accuracy. Initially, the ECG signal is modeled as a linear combination of several Gaussian second-order derivative functions, which can be efficiently represented with only a few parameters, thus mitigating the problem of large model matching errors. To further enhance reconstruction accuracy, an improved two-channel finite rate of innovation sampling framework is introduced, effectively addressing the nonideal effects caused by the low-pass filter during sampling. Additionally, a modified annihilating filter reconstruction algorithm is proposed, allowing high-precision signal reconstruction using a small number of sampling points to estimate parameters. The validity of the proposed method is confirmed through simulations with real ECG signals from the MIT-BIH arrhythmia database, and a hardware platform is developed to verify its feasibility in a practical system. Experimental results demonstrate that, compared to the existing methods, the proposed approach significantly reduces reconstruction error (achieving a PRD as low as 2.29% and an SRR of 11.77 dB), and exhibits better robustness in noisy environments.

In oil and gas related industries, multiphase flow in pipelines is one of the important elements that the Industrial Pipeline Internet of Things (IoT) should continuously monitor. However, existing reconstruction methods often are limited by low resolution and blurred edges. In this article, an electrical capacitance tomography (ECT) imaging system based on Lorentz deblurring and particle filtering is proposed to suppress ECT image blurring. First, a deblurring model based on Lorentz function fitting is proposed, capable of effectively improving the blurred edges, through point spread function (PSF) estimation and Lucy-Richardson algorithm. Then, in the image reconstruction process, it is reformulated as an iterative search for effective particles and their associated weights in the state space, combined with Lorentz deblurring model for the optimal solution. Finally, the virtual-instrument-based ECT hardware system based on the principle of modularity, creates the synergistic architecture between the ECT hardware and imaging software, which enables real-time visualization of imaging. Simulation experiments demonstrate that the image reconstruction algorithm outperforms existing methods in terms of relative error and correlation coefficient, effectively suppressing image blur. Moreover, the ECT imaging system proposed can enhance the measurement capacitance accuracy.

In the Internet of Everything (IoE) era, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) have emerged as crucial enabling technologies to support delay-sensitive and computation-intensive IoE services. Despite the potentials of RISs and MEC, achieving efficient service provisioning in IoE scenarios still faces significant challenges due to interdependencies among different types of resources. To address this issue, we propose a digital twin (DT)-empowered IoE framework that leverages real-time monitoring to virtually replicate network conditions, thereby assisting in decision-making in a physical IoE scenario. Specifically, the IoE scenario comprises a MEC server empowered by prestoring some service programs for task execution and a RIS that assists computation offloading. Taking into account deviations between DT and physical networks, we aim to minimize devices’ total task completion delay by jointly optimizing the service caching at the MEC server, the computation offloading of devices, the computing resource allocation at the MEC server, and the beamforming of the RIS. To handle the problem involving discrete and continuous factors, we develop a hybrid deep reinforcement learning (HDRL) algorithm that integrates the double deep Q-network (DDQN) and deep deterministic policy gradient (DDPG) approaches. In our HDRL algorithm, DDQN plays a crucial role in determining discrete variables representing service caching and computation offloading decisions, while DDPG focuses on optimizing resource allocation and RIS beamforming. We conduct simulations to evaluate the performance of the proposed scheme and compare it with several baselines. Simulation results demonstrate the superiority of our scheme in minimizing the task completion delay.

This paper investigates an active intelligent reflecting surface (IRS) assisted integrated sensing and communications (ISAC) system in a cloud radio access network (C-RAN). In particular, we focus on the IRS assisted target sensing for the blind area, wherein multiple IRSs are deployed to establish controllable reflective links between the targets and the sensing remote radio heads (RRHs). A novel IRS-assisted location-aware direction-of-arrival (DOA) estimation scheme is proposed, where the BBU (baseband unit) pool simultaneously recovers the DOA from each target to each IRS using the RRH received signals. In general, the channel knowledge between the IRSs and RRHs is hard to acquire, hence we utilize the location information of IRSs and RRHs instead. Specifically, we transform the DOA estimation problem into a mixed one-dimensional and two-dimensional atomic norm minimization (ANM) problem, by which the DOAs can be efficiently extracted. Moreover, a theoretical Cramér-Rao lower bound (CRLB) on the DOA estimation error is also derived to evaluate the performance of the proposed scheme. Finally, simulation results illustrate the effectiveness for DOA estimation by the proposed scheme.

Unmanned aerial vehicle (UAV) assisted communication is becoming a promising technology for future networks. Leveraging this benefit, the ambient backscatter communication can utilize the UAV’s emitted signal as the radio frequency carrier to transmit its own information. However, this transmission behavior is easily to be detected by the UAV due to the high possibility of line-of-sight (LoS) air-ground channel. Thus, in this paper, we propose a covert ambient backscatter communication scheme by exploiting the UAV relay as the radio frequency source. Specifically, the UAV relays the information for two legitimate ground nodes, and monitors the potential ambient backscatter communication. Our goal is to maximize the covert ambient backscatter communication rate under the worst case that the UAV performs with the optimal detection threshold, transmit power and hovering location. First, the UAV’s optimal detection threshold is analyzed, and the corresponding closed-form expression of error detection probability is derived. Then, we propose an iterative algorithm to achieve the minimum error detection probability by optimizing the transmit power and hovering location of UAV. To fight against the detection of UAV, we formulate a convex optimization problem to maximize the worst-case covert ambient backscatter communication rate by adjusting the reflection coefficient. Simulation results show that the proposed scheme can effectively improve the covert ambient backscatter communication rate.

This paper investigates secure transmission in the uplink of a cell-free massive multiple-input multiple-output (MIMO) system employing three distinct multiple access strategies: rate-splitting multiple access (RSMA), non-orthogonal multiple access (NOMA), and space-division multiple access (SDMA). RSMA, functioning as a unifying paradigm, merges the merits of both NOMA and SDMA, and holds substantial promise for enhancing system secrecy. We derive closed-form expressions for secrecy spectral efficiency (SE) under Rician fading channels and imperfect channel knowledge assumptions. The secrecy SE is subsequently evaluated across a range of system configurations, encompassing varying access point (AP) and user numbers, AP and eavesdropper antenna dimensions, line-of-sight probabilities, successive interference cancellation conditions, and multiple access protocols. Harnessing these expressions, we establish an optimization framework for the users’ power control coefficients and APs’ receiving weights to maximize the sum secrecy SE while ensuring quality-of-service secrecy requirements for users. Additionally, an alternative optimization algorithm is proposed to ascertain a high-quality solution. Comprehensive simulations substantiate our theoretical propositions and evaluate the efficacy of the proposed sum secrecy SE maximization algorithm.

Satellite networks hold great promise for future 6G communications because of their benefits such as wide coverage and large capacity. Since the end-to-end (e2e) queuing delay is regarded as one key factor affecting the quality of service (QoS) in satellite networks, accurate delay prediction is a critically important topic. However, the delay prediction is complicated due to the irregular and time-varying features of inter-satellite links (ISLs) and satellite-ground links (SGLs), such as discontinuity and alternation. In this paper, we propose to establish the multi-node satellite-to-ground communication procedure as a multi-hop tandemly queuing model and present a heterogeneous heterogeneous multi-hop martingale model for queuing delay analysis. Due to the unique time-varying characteristics, we propose to model the SGL and ISL services as the stationary Markov processes using the Markov chain Monte Carlo approach. To match the intermittency and burstiness of traffic, the data arrival and service processes are handled using the Markov process. We propose to use a scaling factor for reflecting the heterogeneity of data processing capability, and then present a novel approach to ensure the stability condition requirement of the multi-hop system. Using the multi-hop heterogeneous martingale approach, the tight upper bounds of the delay and backlog in heterogeneous links are derived, and then precise delay prediction can be obtained. Finally, numerous simulations are conducted to demonstrate the effectiveness and accuracy of the proposed martingale method in analyzing the system delay and backlog when compared to the existing stochastic network calculus method.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution to support wireless devices' computation-intensive services in the absence of terrestrial infrastructures. Nevertheless, the heterogeneous nature of MEC services and the security vulnerability of wireless channels present significant challenges to achieving efficient and secure computation offloading. In this paper, we investigate a multi-UAV-assisted MEC network in which wireless devices need to process diverse computation tasks. The devices can perform local computing or offload their computation tasks to UAV servers that have pre-cached relevant service programs in the presence of eavesdroppers. To facilitate secure service provisioning, we propose a cooperative jamming-based scheme in which a UAV jammer transmits jamming signals to interfere with eavesdroppers during devices' computation offloading processes. Taking into account UAV servers' constrained caching spaces and secure offloading requirements, we minimize the total task completion delay of devices by jointly optimizing multi-UAV deployment, task offloading decisions, service placement, UAV jammer's transmit power, and devices' transmit power. To tackle the formulated mixed-integer nonlinear programming problem, we design an optimization-embedding multi-agent twin delayed deep deterministic policy gradient (OE-MATD3) algorithm. Specifically, the MATD3 approach is leveraged to deal with optimization variables concerning UAVs, while a closed-form solution for devices' transmit power is derived and guides MATD3-based decision-making. Simulation results demonstrate that the proposed scheme outperforms baselines in terms of devices' task completion delay.

Integrated sensing and communication (ISAC), is emerging as a promising technology for future mobile networks. This paper studies the robust secure transmission for intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV)-ISAC networks without eavesdropping channel state information. Particularly, the UAV, as a dual-functional ISAC base station, serves$K$communication users and senses$J$targets with an IRS. Furthermore, an eavesdropper aims at eavesdropping the private information from the UAV to$K$users. Without eavesdropping channel state information, a secure transmission scheme is proposed to maximize the average achievable rate via jointly designing the transmit power allocation, the scheduling of users and targets, the phase shifts at IRS, and the trajectory and velocity of the UAV. Owing to the non-convexity, an iterative algorithm based on the alternating optimization, the successive convex approximation and the manifold optimization is proposed to obtain a sub-optimal solution. Simulation results verify the effectiveness of the proposed scheme.

Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC, in which the channel information can be used as semantic information. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, a practical case of coupled phase shifts at STARRIS is investigated. We first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed scheme.

Reconfigurable intelligent surface (RIS) has been widely deployed to assist the covert transmission thanks to its ability of channel reconfiguration. Compared with the conventional RIS, simultaneous transmitting and reflecting RIS (STAR-RIS) can transmit and reflect the incident signal simultaneously. This paper investigates the STAR-RIS assisted covert multicasting with the hardware impairment. Specifically, Alice covertly transmits the common information to two users assisted by one STAR-RIS against two wardens. The covert rate is maximized via jointly optimizing the transmit beamforming, and the reflection and transmission phase shifts, satisfying the transmit power constraint, the covertness constraint and the protocol of STAR-RIS. Owing to the non-convexity, we propose an iterative algorithm based on the alternating optimization, successive convex approximation and penalty-based semi-definite relaxation to obtain a sub-optimal solution. Simulation results verify the effectiveness of STAR-RIS.

As the number of users connected to communication networks such as cellular networks and Internet of Things (IoT) networks increases, massive multiple-input multiple-output (MIMO) technique has been widely adopted to improve the spectral and energy efficiency. However, the multi-user frequency synchronization problem must be solved before channel estimation and data detection. Concurrent estimation of multiple carrier frequency offsets (CFO) at base station could be very challenging due to the coexisting and intertwined effects of multiple CFOs and uplink channels in the received signal. In this paper, we consider the frequency synchronization and channel estimation for multi-user uplink massive MIMO systems. To solve the complex multi-CFO estimation problem, we first derive the efficient joint multi-user frequency synchronization algorithm based on the maximum likelihood (ML) criterion, whose high computational complexity is reduced by the proposed Gauss-Newton method. Furthermore, we develop a multi-stage iteration update filtering (MIUF) based multi-user CFO and channel estimation method. The least squares (LS) algorithm is adopted to estimate the channels, based on which the filtering matrix is carefully designed to perform multi-user interference (MUI) suppression. Moreover, considering the effect of CFO error on the channel estimation, an iterative procedure is designed to improve MUI suppression and estimation accuracy. We also analyze the CFO estimation performance and obtain the theoretical expression of mean squared error (MSE). Finally, the effect of CFO error on channel estimation is derived. Numerical results are provided to corroborate the effectiveness of the proposed methods and their superiority over the existing ones.

Passive sensing using communication signal waveforms is considered to be a promising technology for target monitoring in Internet-of-Everything. Conventional passive sensing schemes require accurate estimation of the time difference of arrival (TDOA) and frequency difference of arrival (FDOA), which is leading to high complexity but low accuracy. In this paper, a robust passive sensing algorithm using multiple illumination of opportunities is proposed to improve the detection performance while avoiding separate estimation of TDOA and FDOA. The proposed method first combines the linear constrained minimum variance adaptive filter with the wide nulling algorithm to achieve target direction finding while separating the direct wave and suppressing multipath interference. Then, the Linear Canonical Transformation-based Cross Ambiguity Function (LCTCAF) is employed to estimate the distance and radial velocity of the target. Relying on the relationship between distance to time and velocity to Doppler, a Distance-Velocity transformation-based Cross Ambiguity Function (DVCAF) is introduced to characterize the distance and radial velocity of the target. Finally, a spectral peak search scheme is exploited in DVCAF to estimate the time delay and Doppler shift so as to identify the target parameters directly. Its’ Cramer-Rao Low Bound is derived. Simulation results validate that the performance of the proposed algorithm outperforms the conventional estimators based on the cross ambiguity function.

With the evolutional development of maritime networks, the explosive growth of maritime data has put forward elevated demands for the computing capabilities of maritime devices (MDs). Unmanned aerial vehicle (UAV) is able to alleviate the computing pressure of MDs by forwarding the computing tasks to the edge server on the coast. However, UAV relaying introduces a significant security challenge due to the vulnerability of line-of-sight (LoS) communication channels, which can be exploited for eavesdropping on computing tasks. In this paper, an efficient secure communication scheme is proposed for UAV-relay-assisted maritime mobile edge computing (MEC) with a flying eavesdropper. The secure computing capacity of MDs is maximized by jointly optimizing the transmit power, time slot allocation factor, computation optimization and UAV trajectory. Due to multi-variable coupling, the formulated optimization problem (OP) is non-convex. We first transform OP by introducing auxiliary variables. Then, the transformed OP is decomposed and solved in an iterative manner by applying block coordinate descent (BCD) and successive convex approximation (SCA). Numerical results show that the secure computing capability of the UAV-relay-assisted maritime MEC system of proposed secure communication scheme can be effectively improved compared with benchmarks.

Rate-splitting multiple access (RSMA) has emerged as a potent paradigm shift in wireless communications, demonstrating resilience to channel state information (CSI) inaccuracies and significant rate enhancements. This work investigates RSMA’s application within the context of ultra-reliable and low-latency communication (URLLC) for the forthcoming Internet-of-Everything networks. Specifically, we integrate RSMA with a cell-free massive multiple-input multiple-output (MIMO) architecture to support URLLC demands. Considering the imperfect CSI, attributable to pilot contamination and thermal noise, we derive rigorous lower-bound expressions for the downlink achievable rates. These expressions are applicable to short-packet communication scenarios and RSMA strategy over spatially correlated Rician fading channels. Utilizing these analytical expressions, we perform an exhaustive rate performance evaluation, varying system parameters such as the numbers of pilots, access points (APs), devices, and antennas per AP, alongside different multiple access techniques. Furthermore, we address the power control coefficient design for both common and private streams, framing it as an optimization problem aimed at maximizing the weighted sum-rate and enhancing URLLC service quality. To tackle this non-convex challenge, we introduce a geometric programming-based path-following algorithm, which iteratively converges to the solution. The theoretical underpinnings and the efficacy of the proposed power optimization algorithm are corroborated through extensive simulation results.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) can provide flexible computing service for terminal-devices (TDs). However, malicious active aerial eavesdroppers can perform air-to-ground eavesdropping and air-to-air attacking, which makes TDs’ tasks offloading computation more vulnerable, posing significantly secure threats to UAV-enabled MEC. To overcome this challenge, we aim to design collaborative communication and computation schemes for the secure UAV-enabled MEC system, where an active aerial eavesdropper is capable of wiretapping the tasks information offloaded from TDs and transmitting attack signals to the legitimate network. The total weighted energy consumption of the system is minimized via optimizing time allocation, transmit power, local and offloading computation bits, as well as UAV trajectory. First, considering the given number of computational tasks of TDs, a block coordinate descent (BCD)-based scheme is proposed to decompose the original multi-variables-coupling and close-form-lacking problem into several tractable subproblems that can be addressed by iterations. Next, considering that there are dynamic and random tasks arriving to TDs’ original tasks, a deep reinforcement learning (DRL)-based scheme is proposed to maintain the stability of tasks, where the solution of computation, communication and trajectory optimization is intelligently obtained by adopting double-deep Q-learning (DDQN). Simulation results demonstrate that the proposed schemes outperform the respective benchmarks for secure UAV-enabled MEC against active aerial eavesdropping.

Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. Moreover, ISAC outperforms traditional separate radar and communication systems in terms of both power consumption and spectral efficiency. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, both cases of independent and coupled phase shifts at STAR-RIS are investigated. For independent phase shifts, we develop an alternating direction method of multipliers (ADMM)-based algorithm to decouple the original problem into several tractable subproblems that facilitates the derivation of a closed-form solution to each subproblem. In the scenario with the coupled phase shifts, we first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed schemes, demonstrating STAR-RIS’s superiority over conventional RIS. Moreover, the adopted protocol of STAR-RIS can maintain an excellent balance between performance and complexity.

Reconfigurable intelligent surface (RIS) has been widely deployed to assist the covert transmission thanks to its ability of channel reconfiguration. Compared with the conventional RIS, simultaneous transmitting and reflecting RIS (STAR-RIS) can transmit and reflect the incident signal simultaneously, which provides an opportunity for the full-space covert transmission. This paper investigates the robust covert multicasting aided by the STAR-RIS with the hardware impairment. Specifically, Alice covertly transmits the common information to two single-antenna users assisted by the STAR-RIS against two non-colluding multi-antenna wardens. Furthermore, both energy splitting (ES) and mode switching (MS) protocols of the STAR-RIS are considered. With perfect wiretap channel state information (CSI), the covert rate is maximized via jointly optimizing the transmit beamforming, the reflection and transmission coefficient matrices, satisfying the transmit power constraint, the covertness constraint and the protocol of the STAR-RIS. Moreover, we also investigate the covert rate maximization problem under the case of imperfect wiretap CSI. Due to the non-convexity of the problem, we propose iterative algorithms based on the alternating optimization, successive convex approximation and penalty-based semi-definite relaxation to obtain a near-optimal solution to each problem. Simulation results verify the effectiveness of the STAR-RIS, and show that the ES is superior to the MS.

Integrated sensing and communication (ISAC), which can make full use of the wireless platform and the spectrum for concurrent sensing and communication purposes, is emerging as a promising technology for future mobile networks. This paper studies the secure transmission for intelligent reflecting surface (IRS) aided unmanned aerial vehicle (UAV)-ISAC networks. Particularly, the UAV, as a dual-functional ISAC base station, servesKcommunication users and sensesJtargets with the help of an IRS. Furthermore, a potential eavesdropper, whose channel state information is not available, aims at eavesdropping the private information from the UAV toKusers. A secure transmission scheme is proposed to maximize the average achievable rate via jointly designing the transmit power allocation, the scheduling of users and targets, the phase shifts at IRS, as well as the trajectory and velocity of the UAV. Owing to the non-convexity, an iterative algorithm based on the alternating optimization (AO), the successive convex approximation (SCA) and the manifold optimization (MO) is proposed to obtain a near-optimal solution. Moreover, we also investigate the energy efficiency maximization problem. We develop another iterative algorithm based on the AO, the SCA, the MO and the Dinkelbach’s algorithm to obtain a near-optimal solution to this non-convex fractional programming problem. The effectiveness of the proposed schemes is verified via simulation results.

To meet the growing demand for joint data and energy transmission, research on wireless information and power transfer is being promoted. The resonant beam enabled information and power transfer (RBIPT), which supports long-distance, high-power, and wide-bandwidth information and power transfer, has sparked widespread interest. The point-to-multipoint RBIPT system shows great promise for enabling simultaneous RBIPT for multiple receivers. However, the enabling system architecture has not been well studied in the literature, which is holding back the system implementation. To solve this problem, we propose a time division multiplexing RBIPT (TDM-RBIPT) system for multiple access, and constract a novel metric to evaluate the information and power transfer performance. We explore the TDM-RBIPT mechanism and design the architectures of the transmitter and the receiver. For the information transfer performance evaluation, we take system latency and throughput into consideration. We propose to estimate the system delay with the martingale theory by modeling the dynamic data processing procedures as Markovian processes with the markov chain monte carlo (MCMC) method. To evaluate the power transmission performance, we consider the transmitter’s power costs and the receivers’ power benefits. Numerical results reveal the effectiveness of the proposed TDM-RBIPT system and validate the accuracy of the proposed metric.

In the realm of industrial Internet of Things, the imperative for ultra-reliable and low-latency communication (URLLC) is underscored by the demand for up to 99.999% reliability and sub-microsecond latency. In this paper, we delve into a downlink cell-free massive multiple-input multiple-output (MIMO) system designed to facilitate URLLC, operating over spatially correlated Rician fading channels with inherent phase shifts. Utilizing short-packet transmission and accounting for imperfect channel state information, we derive stringent closed-form expressions for the lower-bound achievable rates, considering both phase-aware and phase-unaware minimum mean squared error estimations. Employing these expressions, we execute an in-depth performance analysis across diverse system configurations, including the availability of phase shifts and the counts of access points (APs), connected devices, antennas per AP, and pilot sequences. Additionally, we propose a path-following power control algorithm that employs geometric programming to enhance the downlink sum-rate. This algorithm is meticulously designed to meet the stringent latency and reliability requirements of URLLC for all connected devices. The theoretical underpinnings and the efficacy of the proposed power control algorithm are substantiated through extensive simulations.

The vigorous development of maritime networks and the explosive growth of maritime sampling data put forward more and more high demands on the computing and communication capability of maritime equipment. Unmanned aerial vehicle (UAV), as the mobile relay device guarantees the capability by transferring part of the computing tasks of maritime equipment to carrying mobile edge computing (MEC) servers on land. However, the transmitting data of the UAV's communication channel can be easily intercepted due to the line of sight (LoS) feature, which brings the secure data transmission issue. To solve this issue, we propose a secure data transmission scheme in the UAV-relay assisted maritime MEC system. Specifically, a malicious UAV attempts to intercept the transmission data while another UAV helps forward the offloading computational data to the maritime surface users. A ground jammer transmits jamming signals with the object to block data intercepting. To maximize the users' minimum secure calculation capacity, we jointly optimize the transmit power of users and the relay UAV, the time slot allocation factor, and the UAV flight trajectory with block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Numerical findings demonstrate that the proposed scheme can effectively improve the secure calculation capability of the system compared with four benchmark schemes.

The integration of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) has created new opportunities for efficient data processing and calculating services within the Internet of Things. However, the presence of the active eavesdropper brings serious vulnerabilities to the security calculation of terminal users (TUs), which can eavesdrop on TUs’ confidential content and compromise the quality of offloading calculation. In this paper, we propose an efficient energy consumption minimization scheme for the considered secure UAV-enabled MEC network including an active UAV eavesdropper. While ensuring security calculation for all TUs’ data, the network’s weighted energy consumption is achieved through trajectory and resource optimization, including time, local calculation and offloading calculation allocation. Due to the coupling of multi-variables and the non-convexity of the constraints, the problem is highly challenging to solve directly. To address this, an auxiliary variable is introduced to transform the problem into a more tractable form. The optimizing solution is then obtained through iterative updates, allowing for the convergence towards an optimizing solution. Simulation results show that the proposed scheme exhibits superior performance of reducing the network’s energy consumption compared to the benchmark scheme.

Combining non-orthogonal multiple access (NO-MA) and unmanned aerial vehicles (UAVs) can achieve better performance for wireless networks. In this paper, we propose an effective scheme for NOMA-UAV network with multiple clusters. Due to the limited resource, the user clustering and optimal routing are first developed by the K-means algorithm and genetic algorithm, respectively. Then, the sum throughput is maximized by jointly optimizing the transmission power, hovering locations and transmission duration of UAV. To solve this non-convex problem with coupled variables, we decompose it into three subproblems. Among them, the non-convex sub-problems can be transformed into convex ones by successive convex approximation. Then, we propose an iterative algorithm to solve these three subproblems alternately. Finally, simulation results are presented to show the effectiveness of the proposed scheme.

Unmanned aerial vehicle-aided (UAV-aided) mobile edge computing (MEC) network can greatly reduce the data growth pressure of Internet of Things (IoT) and expand the wireless communication coverage. However, there is a risk of eavesdropping on the offloading information of terminal users (TUs) because of UAV light-of-sight (LoS) transmission. In this paper, we propose a Dinkelbach-guided deep reinforcement learning (DRL) scheme for secure communication in the UAV-aided MEC network. Specifically, the security calculating efficiency of the network is maximized by optimizing offloading decision and resource allocation under the condition of the data queue stability and minimum calculating requirement. The problem is intractable due to the fractional structure and binary constraint. Firstly, we deal with the fractional structure by taking advantage of Dinkelbach optimization. Then, offloading decision is generated based on DRL and the resource is allocated by successive convex approximation (SCA). Simulation results show that the proposed Dinkelbach-guided DRL scheme efficiently improves the security calculating efficiency of the network.

A novel grant-free random access scheme with a high success rate is proposed to support massive access for machine-to-machine communications in massive multiple-input–multiple-output (MIMO) systems. This scheme allows active user equipments (UEs) to transmit their modulated uplink messages and super pilots consisting of multiple subpilots to a base station (BS). Then, the BS performs channel state information (CSI) estimation and uplink message decoding by utilizing a proposed graph combined clustering independent component analysis (GCICA) decoding algorithm and then employs the estimated CSIs to detect active UEs by using the characteristic of asymptotic favorable propagation of massive MIMO channel. We call this proposed scheme as the GCICA-based random access (GCICA-RA) scheme. We analyze the successful access probability, missed detection probability, and uplink throughput of the GCICA-RA scheme. Numerical results show that the GCICA-RA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability, and provides low CSI estimation error at the same time.

Combining non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAVs) could achieve better performance for wireless networks. However, effective resource allocation for quality of service (QoS) provision among all users still remains as a great challenge for multi-cluster NOMA-UAV networks. In this paper, we propose a NOMA-UAV scheme, where a UAV is deployed as the mobile base station to serve ground users. To meet the QoS requirements of all users with limited resource, the user clustering and optimal routing are first developed by the K-means algorithm and genetic algorithm, respectively. Then, the sum throughput is maximized by jointly optimizing the transmission power, hovering locations and transmission duration of UAV. To solve this non-convex problem with coupled variables, we decompose it into three subproblems. Among them, the power and location optimizations are also non-convex, which can be transformed into convex ones by successive convex approximation. The duration optimization is a linear programming which can be solved directly. Then, we propose an iterative algorithm to solve these three subproblems alternately. Finally, simulation results are presented to show the effectiveness of the proposed scheme.

Non-orthogonal multiple access (NOMA) allows multiple users to share link resource for higher spectrum efficiency. It can be applied to unmanned aerial vehicle (UAV) and mobile edge computing (MEC) networks to provide convenient offloading computing service for ground users (GUs) with large-scale access. However, due to the line-of-sight (LoS) of UAV transmission, the information can be easily eavesdropped in NOMA-based UAV-MEC networks. In this paper, we propose a secure communication scheme for the NOMA-based UAV-MEC system towards a flying eavesdropper. In the proposed scheme, the average security computation capacity of the system is maximized while guaranteeing a minimum security computation requirement for each GU. Due to the uncertainty of the eavesdropper’s position, the coupling of multi-variables and the non-convexity of the problem, we first study the worst security situation through mathematical derivation. Then, the problem is solved by utilizing successive convex approximation (SCA) and block coordinate descent (BCD) methods with respect to channel coefficient, transmit power, central processing unit (CPU) computation frequency, local computation and UAV trajectory. Simulation results show that the proposed scheme is superior to the benchmarks in terms of the system security computation performance.

With the maneuverability and mobility control of unmanned aerial vehicle (UAV), carrying mobile edge computing (MEC) servers on UAVs is able to effectively alleviate the explosive growth of data traffic pressure. However, UAV adopts line-of-sight transmission which has broadcasting characteristics. Malicious eavesdroppers can easily take advantage of the characteristics to eavesdrop information during the UAV edge computing. Therefore, the security of the UAV-MEC systems is a challenging problem. This article proposes a secure communication scheme for the dual-UAV-MEC system. In the proposed scheme, UAV server assists ground users in calculating the offloading tasks. In order to reduce the eavesdropping of offloading information by UAV eavesdropper, jammer sends interference signals on the ground. We aim to maximize the user's minimum secure calculation capacity by optimizing resources and trajectory of the UAV server. We first transform the optimization problem into a tractable form through mathematical methods and use successive convex approximation and block coordinate descent algorithms to solve it in an iterative manner. The final numerical results show that, compared with the benchmark schemes, the method proposed in this article effectively increases the secure calculation capacity of the system.

Unmanned aerial vehicles (UAVs) have been widely utilized to improve the end-to-end performance of wireless communications. In this paper, we perform a cooperative dual-UAV enabled secure data collection scenario and propose two schemes to ensure the security. The worst-case average secrecy rate is first maximized with the propulsion power limitation, where the scheduling, the transmit power, the trajectory and the velocity of UAVs are jointly optimized. To further save the on-board energy and prolong the flight time, we then maximize the secrecy energy efficiency. Based on the Dinkelbach method, we transform the fractional objective function into an integral expression and propose an iterative algorithm to obtain a suboptimal solution. Finally, numerical results are provided to evaluate the effectiveness of the proposed schemes.

In this paper, we propose a SWIPT-based distributed node sleep scheduling method combining with clustering technology. The information transmission layer adopts a distributed topology structure. Active nodes are divided into cluster head nodes and cluster member nodes. The cluster head node acts as a relay of cluster member nodes, which can reduce the single-hop communication distance and greatly reduce the energy consumption of information transmission. The energy-carrying communication process is divided into three time slots: the first time slot, the sensor node collects energy, according to the residual energy of the node, the energy collected in the first time slot, and the requirements of network connectivity, the sleep scheduling optimization algorithm will divide the nodes into sleeping nodes and active nodes, and the active nodes are clustered according to the relative position of the information sending rate of the node; in the second time slot, the cluster head node receives the information sent by the cluster member node, and the sleeping node continues to collect energy; In three time slots, the cluster head node processes the collected data and sends it to the sink node, and the cluster member nodes and sleeping nodes continue to collect energy. Finally, according to the user's requirements for energy utilization and network coverage, an appropriate time slot division factor is selected. Compared with the centralized sleep scheduling method, the energy utilization and coverage of the network are improved.

As one of the key technologies of 5G communication technology, D2D (device to device) communication technology has the advantages of high frequency spectrum efficiency, low energy consumption, low delay, allowing end users to communicate directly through sharing cell resources, at the same time, it will bring Co-Channel interference problems. In order to solve the interference between D2D users and cellular users, and solve the problems of single path loss compensation and poor power control performance. Therefore, a joint power control algorithm based on two factors is proposed. The path loss compensation is calculated according to the distance from user to base station and cellular user, and the path loss compensation factors of D2D users and cellular users are determined. At the same time, the power compensation parameters are introduced to dynamically analyze the user power; The joint power control method is used to reduce the Co- Channel interference of D2D users and cellular users. The experimental simulation shows that: Compared with the traditional power control algorithm, this algorithm improves the signal to interference plus noise ratio (SINR) and throughput of the system.

Abstract A new random access scheme is proposed to solve the intra‐cell pilot collision for M2M communication in crowded asynchronous massive multiple‐input multiple‐output systems. The proposed scheme utilizes the proposed estimation method of signal parameters to estimate the effective timing offsets, and then active user equipments obtain their timing errors from the effective timing offsets for uplink message transmission. The mean squared error of the estimated effective timing offsets of user equipments and the uplink throughput are analysed. Simulation results show that, compared to the exiting random access scheme for the crowded asynchronous massive multiple‐input multiple‐output systems, the proposed scheme can improve the uplink throughput and estimate the effective timing offsets accurately at the same time.

Internet of Things (IoT) is able to provide various physical objects to exchange their information through the 6G wireless communication network. However, with the large increasing number of the IoT devices (IoDs), the deployment of IoDs faces two basic challenges, i.e., spectrum scarcity and energy limitation. Cooperative spectrum sharing and simultaneous wireless information and power transfer (SWIPT) provide effective ways to improve the spectrum and energy efficiency. In this article, two SWIPT cooperative spectrum sharing methods are proposed to improve the energy and spectrum efficiency for 6G-enabled cognitive IoT network, in which IoDs access to the primary spectrum by serving as orthogonal frequency-division multiplexing (OFDM) relay with the energy harvested from the received radio-frequency (RF) signal. Specifically, in phase1, the IoDs transmitter (DT) in the cognitive IoT network performs information decoding and energy harvesting with the received RF signal. In phase2, DT transmits the signals of the primary system and itself to the corresponding receiver by utilizing orthogonal subcarriers with the harvested energy to avoid the interference. Achievable rates of the cognitive IoT system with amplify-and-forward (AF) and decode-and-forward (DF) relaying mode are maximized through joint power and subcarrier optimization, while ensuring the target rate of the primary system. Simulation results are performed to illustrate the improvement of the spectrum and energy efficiency.

Reconfigurable intelligent surface (RIS), a recently introduced technology for future wireless communication systems, enhances the spectral and energy efficiency by intelligently adjusting the propagation conditions between base stations (BSs) and mobile equipments (MEs). An RIS consists of many low-cost passive reflecting elements that are optimized to improve the quality of the received signal. In this paper, we study the problem of power control at the BS and RIS optimization for application to physical-layer broadcasting. Our goal is to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BS and the phase shifts of the passive elements at the RIS. Furthermore, to help validate the proposed optimization methods, we derive lower bounds to quantify the average transmit power at the BS as a function of the number of MEs, the number of RIS elements, and the number of antennas at the BS. The simulation results demonstrate that the average transmit power at the BS is close to the lower bound in an RIS-aided system, and is significantly lower than the average transmit power in conventional schemes without an RIS.

Mobile edge computing (MEC) has been envisioned as a promising approach for enabling the computation-intensive yet latency-sensitive mobile Internet services in future wireless networks. In this paper, we investigate the secrecy based energy-efficient MEC via cooperative Non-orthogonal Multiple Access (NOMA) transmission. We consider that an edge-computing device (ED) offloads its computation-workload to the edge-computing server (ECS) subject to the overhearing-attack of a malicious eavesdropper. To enhance the secrecy of the ED's offloading transmission, a group of conventional wireless devices (WDs) are scheduled to form a NOMA-transmission group with the ED for sending data to the cellular base station (BS) while providing cooperative jamming to the eavesdropper. We formulate a joint optimization of the ED's offloaded workload, transmit-power, NOMA-transmission duration as well as the selection of the WDs, with the objective of minimizing the total energy consumption of the ED and the selected WDs, while subject to the ED's latency-requirement and the selected WDs' required data-volumes to deliver. Despite the nature of mixed binary and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a three-layered algorithm for solving it efficiently. To further address the fairness among different WDs, we investigate a system-wise utility maximization problem that accounts for the fairness in the WDs' delivered data and the total energy consumption of the ED and WDs. By exploiting our previously designed layered-algorithm, we further propose a stochastic learning based algorithm for determining each WD's optimal data-volume delivered. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of the secrecy based computation offloading via NOMA.

The traditional ground industrial Internet of Things (IIoT) cannot supply wireless interconnections anywhere due to its small-scale communication coverage. In this article, a multibeam satellite IIoT in Ka-band is proposed to realize wide-area coverage and long-distance transmissions, which uses nonorthogonal multiple access (NOMA) for each beam to improve transmission rate. To guarantee Quality of Service (QoS) for the satellite IIoT, the beam power is optimized to match the theoretical transmission rate with the service rate. The NOMA transmission rate for each beam is maximized by optimizing the power allocation proportion of each node subject to the constraints of the total power for the beam and the minimal transmission rate for each node within the beam. Satellite-ground integrated IIoT is proposed to use the ground cellular network to supplement the satellite coverage in the blocked areas. The power allocation and network selection for the integrated IIoT are proposed to decrease the transmission cost. Simulation results are provided to validate the superiority of employing NOMA in the satellite IIoT and show higher transmission performance for the QoS-guarantee resource allocation.

Smart agriculture is able to optimize the information resources of agriculture, which can improve the quality and productivity of agricultural products. Wireless sensor networks (WSNs) provide smart agriculture with effective solutions for collecting, transmitting, and processing of information. However, the large number of sensor networks consume too much energy that violates the principle of green communication. Simultaneous wireless information and power transfer (SWIPT) technology utilizes radio-frequency signals to transmit information and provide energy to WSNs, which can extend the lifetime of WSNs effectively. In this article, an architecture design of smart agriculture is first proposed by exploiting the SWIPT. Then, an energy efficiency optimization scheme is studied to achieve green communication, in which the subcarriers' pairing and power allocation are jointly optimized. The process of communication is divided into two phases. Specifically, in the first phase, source sensor sends information to relay sensor and destination sensor. Relay sensor utilizes a part of the subcarriers to receive the information, and utilizes the remaining subcarriers to collect energy. Destination sensor uses all the subcarriers to receive the information. In the second phase, relay sensor utilizes the energy collected in the first phase to forward the information to destination sensor. An effective iterative optimization algorithm is proposed to resolve the proposed optimization problem through Lagrangian dual function. Simulation results validate that the performance of the algorithm can improve energy efficiency of the system effectively.

Multiaccess mobile edge computing (MA-MEC) has been envisioned as one of the key approaches for enabling computation-intensive yet delay-sensitive services in future industrial Internet of Things (IoT). In this article, we exploit nonorthogonal multiple access (NOMA) for computation offloading in MA-MEC and propose a joint optimization of the multiaccess multitask computation offloading, NOMA transmission, and computation-resource allocation, with the objective of minimizing the total energy consumption of IoT device to complete its tasks subject to the required latency limit. We first focus on a static channel scenario and propose a distributed algorithm to solve the joint optimization problem by identifying the layered structure of the formulated nonconvex problem. Furthermore, we consider a dynamic channel scenario in which the channel power gains from the IoT device to the edge-computing servers are time varying. To tackle with the difficulty due to the huge number of different channel realizations in the dynamic scenario, we propose an online algorithm, which is based on deep reinforcement learning (DRL), to efficiently learn the near-optimal offloading solutions for the time-varying channel realizations. Numerical results are provided to validate our distributed algorithm for the static channel scenario and the DRL-based online algorithm for the dynamic channel scenario. We also demonstrate the advantage of the NOMA assisted multitask MA-MEC against conventional orthogonal multiple access scheme under both static and dynamic channels.

Unmanned aerial vehicles (UAVs) have been widely utilized to improve the end-to-end performance of wireless communications. However, its line-of-sight makes UAV communication vulnerable to malicious eavesdroppers. In this paper, we propose two cooperative dual-UAV enabled secure data collection schemes to ensure security, with the practical propulsion energy consumption considered. We first maximize the worst-case average secrecy rate with the average propulsion power limitation, where the scheduling, the transmit power, the trajectory and the velocity of the two UAVs are jointly optimized. To solve the non-convex multivariable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. To further save the on-board energy and prolong the flight time, we then maximize the secrecy energy efficiency of UAV data collection, which is a fractional and mixed integer nonlinear programming problem. Based on the Dinkelbach method, we transform the objective function into an integral expression and propose an iterative algorithm to obtain a suboptimal solution to secrecy energy efficiency maximization. Numerical results show that the average secrecy rate is maximized in the first scheme with propulsion limitation, while in the second scheme, the secrecy energy efficiency is maximized with the optimal velocity to save propulsion power and improve secrecy rate simultaneously.

In this paper we study interference reducing and resource allocation in Unmanned aerial vehicle (UAV) wireless powered communication system with two UAVs and two ground nodes (GNs). In existing scenarios interference exists at the receiver because multiple GNs transmit information at the same time. In order to reduce interference at the receiver, a new scenario is proposed in this paper. In the proposed scenario, one GN transmit information while another is receiving energy. Minimum uplink throughput is maximized by optimizing trajectory of UAVs and resource allocation. The optimization problem is decomposed into three subproblems which are approximated to convex optimization problems. Simulation results show that the new scenario achieves larger minimum uplink throughput than original scenario.

Sparse code multiple access (SCMA) is a nonorthogonal multiplexing technology based on multidimensional codebook in 5G mobile communication system, which has good flexibility and adaptability. In the SCMA system, although the original Message Passing Algorithm (MPA) has good bit error ratio (BER) performance and complexity, the algorithm complexity is very high because of the exponential algorithm. The Maximum Logarithm Message Passing Algorithm (Max-log-MPA) uses approximate and maximum calculation, which results in some information loss and poor system BER performance. The Threshold-based Message Passing Algorithm adopts the hard decision mechanism, which reduces the complexity of the algorithm, but has the problem of high BER performance with low threshold. The Serial Message Passing Algorithm (S-MPA) uses the user node information update fusion in the resource node information update, which effectively reduces the algorithm complexity, but the user information BER performance is poor. On the basis of the previous algorithm, this paper proposes a Maximum Logarithm Message Passing Algorithm based on Serial and Threshold (S-T-Max-log-MPA), In this algorithm, the threshold is used to determine the necessary conditions of the stability of the user node, and then the user node information update is integrated into the resource node information update, so that the system can maintain good BER performance, especially when the threshold is low, the BER performance of the system is better. The simulation results show that the BER performance of this algorithm is better than that of Max-log-MPA algorithm with the decrease of threshold.

Mobile edge computing (MEC) has been considered as a promising approach for enabling computation-intensive Internet services in future wireless systems. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted MEC, in which edge-computing users (EUs) adopt NOMA to simultaneously offload part of their computation-workloads to the edge-server (ES). To improve the spectrum-efficiency, we consider a paradigm of underlaying device-to-device (D2D) communications, namely, the EUs reuse a cellular user's (CU's) licensed channel for offloading transmission. We firstly characterize the transmit-powers of EUs and CU in this D2D approach, and then formulate a joint optimization of the EUs' computation- workloads offloading and the ES's computation-resource allocation, with the objective of minimizing the latency in completing the EUs' tasks. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose an efficient algorithm for computing the optimal solution. Numerical results are provided to validate the effectiveness and efficiency of our proposed NOMA assisted MEC via the D2D sharing1.

Non-orthogonal multiple access (NOMA) has been considered as a promising scheme for enabling ultra-high throughput transmission and massive-connectivity in next generation wireless systems. In this paper, we investigate the secrecy-based NOMA transmission for encountering the eavesdropping attack. Exploiting the NOMA-users simultaneous transmission as an artificial jamming, we investigate the joint optimization of NOMA-users' power allocations and the secrecy-provisioning, with the objective of the effective secure throughput of NOMA-users while ensuring the fairness among them. Despite the non-convexity of the formulated joint optimization problem, we explore its hidden feature and design a search algorithm to compute the optimal solution. Numerical results are provided to validate the performance of our proposed algorithm.1

Wireless sensor networks (WSNs) have the disadvantages of short lifetime due to the limited energy of the energy storage batteries of the sensor nodes and scarcity of spectrum resources as the number of sensor nodes increasing. Simultaneous wireless information and power transfer (SWIPT) can make WSNs solve the problem of short lifetime through sensor nodes harvest energy from radio-frequency (RF) signals. Cognitive radio(CR) can make WSNs solve the problem of the scarcity of spectrum resources through sensor nodes sense and access free licensed spectrum. This paper mainly investigates the performance of an underlay cognitive sensor network (CSN). The sensor nodes in the underlay CSN can communicate with each other through the help of energy harvesting (EH) relay sensor node (RSN) by using amplify-and-forward (AF) relaying protocol. To maximize the throughput of CSN, we propose a algorithm through optimizing the transmit power of sensor nodes. Simulation results show the algorithm is correct and has good performance.

Wireless power transfer (WPT) and mobile edge computing (MEC) are two prospective technologies to enhance the computing power and endurance of mobile devices. Integrating unmanned aerial vehicle (UAV) into wireless powered MEC system, the energy collection efficiency can be effectively improved with the short‐distance line‐of‐sight path power transfer. However, WPT is susceptible to the ‘double near‐far’ effect. Therefore, in this study, the authors study power optimisation in UAV‐assisted wireless powered cooperative MEC system, which utilises the user cooperation to make the mobile device which is closer to the UAV acting as a relay for offloading. They aim to minimise the total transmission energy of the UAV through the joint power optimisation while satisfying the delay and size of the computational task. Simulation results demonstrate the performance of the proposed scheme.

In order to improve the efficiency of information transmission, this paper proposes an energy trading method based on wireless power supply. In the proposed method, the system consists of a source node, a relay node, a destination node and several energy supply points. Since the relay node is selfish, it does not consume its own energy to help the source node forward information. So this paper designed a series of energy-reward pairs. Through the contract theory designed in this paper, we study how to optimize the rewards that the destination node pays to the ESP and the energy provided by the ESP to the relay node to maximize the utility of the social welfare. The simulation results show that the energy trading method proposed in this paper can make the utility of the destination node reach the optimal value and effectively improve the information transmission efficiency.

In this paper, we study energy efficiency optimization in OFDM based two-way DF relaying network with energy harvesting. Instead of time switching (TS) and power splitting (PS) schemes, we adopt OFDM modulation method, where subcarriers are divided into two groups to achieve information decoding (ID) and energy harvesting (EH) separately. The formulated EE optimization problem is non-convex constrained by the minimum information rate and the maximum transmission power. By exploiting fractional programming, an iterative resource allocation algorithm is proposed to solve the problem. Then we adopt dual decomposition and sub-gradient method to obtain the optimal variables, where subcarrier grouping and power allocation are jointly optimized to maximize the system energy efficiency.

Aiming at the problem of low accuracy and efficiency of traditional indoor positioning algorithm, a positioning algorithm is put forward, which combines with Kalman filter algorithm and clustering algorithm. This algorithm firstly divided the original fingerprint database into K clusters with k-means clustering algorithm and Gaussian mixture model algorithm, and then use the Kalman filtering algorithm to process the collected testing signal. The experimental results show that compared with the traditional indoor positioning algorithm, the joint positioning method reduces the signal noise, improves the efficiency and stability of the positioning algorithm, reduces the positioning error by 23%, and this algorithm works well in positioning.

Aiming at the problems of low accuracy, difficulty in implementation and large computational complexity of indoor location algorithm, a fingerprint localization algorithm based on K value change is proposed. On the basis of fingerprint localization algorithm, this method studies the influence of the K-value selection and positioning accuracy, and selects the K value with the lowest error for positioning. Then, the CKNN algorithm is used to reduce computation load, thereby completing the positioning. The experimental results show that the algorithm can effectively suppress the occurrence of larger errors, control the average error of location to about 1.7 m, improve the accuracy of location, reduce the computational complexity of the algorithm, and shorten the time spent on location.

The shortage of spectrum resources has limited the development of Internet of Things (IoT). Fifth generation (5G) network can flexibly support a variety of devices and services, which makes it possible to combine 5G with IoT. In this paper, a novel multichannel IoT is proposed to dynamically share the spectrum with 5G communication, where an IoT node including transmitter and receiver is designed to perform 5G communication and IoT communication simultaneously. The subchannel sets allocated for 5G communication and IoT communication are defined by two complementary spectrum marker vectors, respectively. Two independent spectrum sequences are generated by calculating the inner products of spectrum marker vectors, presudo-random phases and power scaling vectors. Two time-domain fundamental modulation waveforms generated by the inverse fast Fourier transform of the spectrum sequences are used to modulate 5G data and IoT data, respectively. The receiver can detect the data using the same spectrum marker vectors as the transmitter. The BER performances of the system using binary modulation and cyclic code shift keying modulation in the cases of spectrum marker error and multiple access are analyzed, respectively. A subchannel and power optimization unit is formulated as a joint optimization problem, which seeks to maximize the 5G throughput under the constraints of minimal IoT throughput, maximal power, and maximal interference. An alternative optimization problem is proposed to maximize the IoT throughput while guaranteeing the minimal 5G throughput. A joint optimization algorithm based on Lagrange dual decomposition is proposed to achieve the optimal solution. Simulation results indicate that the proposed IoT can improve the 5G throughput significantly while the IoT throughput is guaranteed.

In the fifth generation mobile networks, generalized frequency division multiplexing (GFDM) is expected as the candidate waveform which can flexibly meet the requirements of diverse applications and scenarios for the Internet of Things (IoT) because of its advantages over orthogonal frequency division multiplexing (OFDM). In order to achieve the reliable data transmission in GFDM-based IoT systems, channel estimation (CE) is a prerequisite. However, the 2-D block modulation and the nonorthogonality between subcarriers for GFDM make it almost impossible that the conventional CE methods suitable for OFDM are directly applied to GFDM. To cope with this problem, a soft decision control strategy-based iterative CE (SDC-ICE) method is proposed in this paper. First, the received signal is equalized by the channel frequency response (CFR) from the pilot-based CE. After GFDM demodulation and Turbo decoding, the feedback log-likelihood ratio is utilized to rebuild symbols for data-aided CE by a redesigned Turbo receiver. Subsequently, the feedback information of both current and former iterations is used to improve the reliability of rebuilt symbols. The CFR obtained from SDC-ICE is used for equalization in the next iteration. The performance of SDC-ICE can be improved by increasing the iterations. Finally, the bit error rate (BER) and mean square error (MSE) performances of SDC-ICE and hard decision control strategy-based iterative CE (HDC-ICE) are simulated and evaluated. Simulation results demonstrate that the proposed method has better BER and MSE performance than HDC-ICE within fewer iterations.

As cellular networks are evolving toward the fifth generation/long-term evolution systems, cellular radio access networks are expected to provide high throughput and reliable connectivity for massive number of smart devices (SDs), which leads to the emerging narrowband Internet of Things (NBIoT), a cellular-assisted low-power wide area IoT system. Driven by the potential critical missions, such as transportation safety and video surveillance that require high throughput and lowpower consumption, we investigate the small-cell assisted traffic offloading for NB-IoT systems. Taking into account the offloading through small cells operating on unlicensed bands, we account for the secrecy-outage issue in which some malicious eavesdroppers might intentionally overhead the offloaded data delivered to small cells. We first formulate a joint traffic scheduling and power allocation problem to minimize the total power consumption of SDs, while satisfying both the traffic throughput requirement and secrecy-requirement. Despite the nonconvexity of the problem, we propose an efficient algorithm to compute the optimal offloading solution. With the per-SD's optimal offloading solution, we further investigate a multi-SDs multi access-points (APs) scenario, in which different SDs select different APs for providing offloading service to minimize the overall offloading-cost for all SDs. Specifically, we formulate an optimal SD-AP pairing problem to find the optimal pairing between the SDs and APs. Numerical results have been provided to validate our proposed algorithm and show the performance gain of our proposed traffic offloading for the NB-IoT systems.

Smart city is able to make the city source and infrastructure more efficiently utilized, which improves the quality of life for citizens. In this framework, wireless sensor networks (WSNs) play an important role to collect, process, and analyze the corresponding information. However, the massive deployment of WSNs consumes a significant energy consumption, which has raised the growing demand for green WSNs for smart cities. Exploiting the recent advance in collaborative energy and information transfer to power the WSNs and transmit the data has been considered a promising approach to realize the green WSNs for smart cities. We propose an architecture design of the green WSNs for smart cities, by exploiting the collaborative energy and information transfer protocol, and illustrate the challenging issues in this design. To achieve a green system design, the sensor nodes in WSNs harvest the energy simultaneously with the information decoding (ID) from the received radio frequency signals. Specifically, the energy-constrained sensor nodes partition the received signals into two independent groups to perform energy harvesting (EH) and ID. The sensor nodes then use the harvested energy to amplify and forward the information signals. We study the joint optimization of subcarrier grouping, subcarrier pairing, and power allocation such that the transmission rate performance is maximized with the EH constraint. The joint optimization problem is solved via dual decomposition after transforming it into an equivalent convex optimization problem. Simulation results tested with the real WSNs system data indicate that the performance of our proposed protocol can be significantly improved.

In this paper, we propose a cooperative spectrum sharing protocol based on OFDM two-way relaying with joint optimal subcarrier and power allocation. Specifically, the secondary system helps the primary system achieve their target rates through OFDM two-way relaying, where the secondary system forwards the primary signal by using a fraction of subcarriers and power. In return, the secondary system can gain spectrum access by using the remaining subcarriers and power to transmit its own signal. Joint optimal subcarrier and power allocation is derived aiming to maximize secondary transmission rate with primary transmission rate constraint. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.

In this paper, we proposed a cooperative spectrum sharing strategy based on contract theory with optimal power and bandwidth allocation. Specifically, primary user (PU) and secondary users (SUs) act as the employer and employees like labor consumption market, respectively. SUs provide labor, i.e. the relay power used for forwarding the PU's signal, in exchange for the reward, i.e. the spectrum accessing bandwidth for transmitting their own signals. PU needs to overcome a challenge how to balance the relationship between contributions and incentives for SUs. We designed an optimal contract with joint power and bandwidth optimization. We study how to allocate the power and bandwidth to maximize primary user's utility. Simulation results confirm that the utility of the primary user is significantly enhanced with our proposed cooperative spectrum sharing strategy.

To achieve secure communication between legitimate users, a physical layer encryption scheme based on random rotation of the modulated symbol is proposed. By exploiting the random behavior and the reciprocity property of the wireless channel, the channel state information (CSI) shared between transmitter and legitimate receivers is used as a initial seed to generate chaotic sequence. The transmitter uses the chaotic sequence to rotate the modulated symbol to enhance communication security and to reduce eavesdroppers' ability to demodulated symbols correctly. Due to the fact that the eavesdropper does not posses any information about the legitimate channel because the channel response is unique to the location of the transmitter and receiver as well as the environment, the receiver is able to demodulate the random rotated symbols correctly while the eavesdroppers demodulate them erroneously. Simulation results show that bit-error-rate (BER) of the legal user matches theoretical results perfectly while the eavesdroppers' BER stays around 0.5, which means that the proposed scheme keeps data transmission under security.

The advanced non-orthogonal multiple access (NOMA) has been considered as a promising scheme to satisfy the ultimate goals of future 5G cellular networks for providing ultra-high throughput and ultra-dense connections. By enabling a group of mobile users (MUs) to simultaneously share a same frequency channel and adopting successive interference cancellation to mitigate the co-channel interference, the NOMA can significantly improve the spectrum efficiency compared with the conventional orthogonal multiple access (OMA). However, due to cellular operators' limited and crowded spectrum resources, a critical question is how to properly size the channel bandwidth for the NOMA- enabled transmission to satisfy all MUs' traffic demands. In this paper, we propose a joint optimization scheme of bandwidth and power allocations for the NOMA- enabled downlink transmission, with the objective of minimizing the overall resource consumption cost that accounts for both the spectrum consumption cost and power consumption cost. In spite of the non-convexity nature of the joint optimization problem, we propose an efficient algorithm to compute the optimal bandwidth allocation and power allocation. Numerical results validate the proposed algorithm and the performance advantage of the proposed NOMA-enabled transmission in saving the overall resource consumption cost.

Offloading data traffic from the conventional macro- cell base stations to densely deployed small-cell base stations (SBSs) has been emerging as a promising technique to support the explosion of data traffic with reduced energy consumption and improved quality of service provision. In this paper, we study the joint spectrum allocation and power allocation problem for the data offloading in energy-harvesting downlink small-cell networks. First, we formulate the resource allocation problem under the revenue maximization criterion, which is expressed as the difference between the total utility across users and the total power payment. By proving the convexity of the problem, we can compute the solution efficiently. Numerical results show that the energy-efficiency can be improved while alleviating the burden of macro-cell base station through using the resource allocation scheme proposed for data offloading.

The correlation coefficient of two consecutive Gaussian sequences is modulated by a binary message bit to achieve a secure communication system. The receiver of the proposed random communication system demodulates the received signal by estimating the correlation coefficient of the transmitted two consecutive sequences. Theoretical bit error rate (BER) expressions in frequency‐flat/‐selective fading channels with/without Doppler shift are derived. Simulation results show that the proposed system can achieve reasonably low BERs in an additive white Gaussian noise channel as well as a Rayleigh fading channel. More importantly, the proposed system shows good performance in resisting eavesdropping, since the transmitted sequence appears to be a Gaussian noise which is almost always inevitable in the process of wireless communications.

In cognitive radio (CR), the spectrum sensing of the primary user (PU) may consume some electrical power from the battery capacity of the secondary user (SU), yielding to decrease the transmission power of the SU. In this paper, a multislot simultaneous spectrum sensing and energy harvesting model is proposed, which uses the harvested radio frequency (RF) energy of the PU signal to supply the spectrum sensing. In the proposed model, the sensing duration is divided into multiple sensing slots consisted of one local-sensing subslot and one energy-harvesting subslot. If the presence of the PU is detected in the local-sensing subslot, the SU will harvest RF energy of the PU signal in the energy-harvesting slot, otherwise, the SU will continue spectrum sensing. The global decision is obtained through combining local sensing results from all the sensing slots by adopting "OR Rule". A joint optimization problem of sensing time and time splitter factor is proposed to maximize the throughput of the SU under the constraints of probabilities of false alarm and detection and energy harvesting. The simulation results have shown that the proposed model can improve the maximal throughput of the SU obviously compared to the traditional sensing-throughput tradeoff model.

In this paper, we propose an anti-interference cooperative spectrum sharing strategy based on fairness secondary users selection where the secondary system can gain spectrum access to the primary system. Specifically, secondary user STband STqare selected to transmit the primary and secondary signal by using different bandwidth in the second transmission slot which occupies a part of the whole transmission time. The primary and secondary systems will not interfere with each other as they use orthogonal bandwidth to transmit their signals. We study the secondary users selection to guarantee the fairness among the secondary users, and the joint optimization of time and bandwidth allocation such that the transmission rate of the secondary system is maximized, while guaranteeing the primary system achieve its target rate. Simulation results confirm efficiency of the proposed spectrum sharing strategy, and the significant performance improvement of the cognitive system.

In this paper, a cooperative spectrum sharing protocol with quality-of-service (QoS) support for both of the primary and secondary systems is proposed. Specifically, the secondary system gains primary spectrum access by allocating a fraction of its power to forward the primary signal helping the primary system achieve the target rate, and meanwhile exploits the remaining power to transmit its own signal. We analyze the achievable rates for the primary and secondary systems, and determine the optimal power allocation such that the sum transmission rate of primary and secondary systems is maximized, while the QoS of both primary and secondary systems can be guaranteed. Simulation results demonstrate the efficiency of the proposed spectrum sharing protocol and its benefit to both primary and secondary systems.

In this paper we proposed a cooperative spectrum sharing protocol with two-way decode-and-forward (DF) relaying. Specifically, two primary users A and B communicate with each other with the assistant of the secondary user S. The secondary user uses a fraction of power to forward the primary signals by acting as a DF relay. As a reward, the secondary user can gain spectrum access by using the remaining power to transmit its own signal. We study the optimization of power allocation such that the secondary transmission rate is maximized, while both of the primary users can achieve their target rates. Numerical simulation and comparisons are presented to illustrate the performance of the proposed spectrum sharing protocol, and both primary and secondary users can benefit from the proposed spectrum sharing protocol.

With the rapid development of smart handled devices and mobile internet services, mobile network operators (MNOs) have experienced an explosive growth in traffic demand in cellular access networks. Intelligently offloading traffic through small-cell networks has been widely considered as an efficient approach for MNOs to relieve traffic congestion in cellular access networks and accommodate more mobile users (MUs) with satisfactory quality of service (QoS). However, offloading traffic to small-cell networks might incur co-channel interference among the MUs. Such interference, if without a proper control, will lead to significant power consumptions of the MUs, which undermines the benefit of traffic offloading. In this paper, we are motivated to investigate the joint access-selection and power allocation problem, in which the MUs are appropriately selected to offload their traffic demands to different small-cell networks with proper transmit-powers. Our objective is to maximize a system-reward that takes into account both the MNO's economic reward for serving the MUs and the MUs' transmit-power consumption costs. The formulated problem corresponds to a mixed binary and non-convex optimization problem. We exploit the decomposable structure of the problem and propose an efficient algorithm to solve it. Numerical results are provided to show the performance of the proposed algorithm as well as the benefits of the proposed traffic offloading scheme.

Energy harvesting (EH) is a prominent method to prolong the operation time of energy-constrained wireless networks. Integrating EH into wireless communications to support simultaneous wireless information and power transfer (SWIPT) allows the spectrum to be used for both purposes without compromising the quality of service (QoS). In this paper, we propose a subcarrier allocation based SWIPT scheme in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the received OFDM subcarriers are partitioned into two groups. A part of the received subcarriers are allocated to form one group which are used for information decoding (ID), and the remained subcarriers form another group, which are used for energy harvesting. Thus, no splitter is needed at the receiver. We study the optimal subcarrier allocation such that the harvested energy is maximized with the ID constraint. By using the Lagrangian method, we develop efficient algorithm to solve the optimization problem.

Dynamic spectrum access via active spectrum sharing has been considered as a promising approach to improve the spectrum utilization for future wireless systems. In this paper, based on our recent study on the optimal transmit-power allocation for an active spectrum sharing system comprised of single primary-user (PU) and multiple secondary-users (SUs) [7], we move a further step to investigate a more challenging scenario comprised of multiple PUs and multiple SUs. Specifically, we formulate a joint SU-selection and power allocation problem, in which the PUs properly select different groups of SUs to share channels with and the PUs and SUs then determine the proper transmit- powers. We aim at maximizing a system reward for serving the SUs' traffic while trading off the PUs' additional power consumptions to guarantee their required quality of service (QoS). We exploit the layered structure of the joint optimization problem and propose an efficient algorithm to solve it. Numerical results are provided to validate performances of the proposed algorithm and show advantages of performing the joint SU-selection and power allocation in active spectrum sharing.

In this paper, we investigate the energy-aware data- offloading of mobile user (MU) which schedules its traffic demand to a macro Base Station (BS) and a small-cell access point (AP) simultaneously. For saving the usage of licensed spectrum, we consider that the MU uses unlicensed spectrum to offload data. The open access of unlicensed spectrum, however, results in that the MU's data offloading suffer from uncontrollable interference, which comprises the benefit of data offloading. We propose an outage-probability to quantify such an adverse influence and formulate a joint rate-splitting and power allocation problem to minimize a system-wise cost accounting for both the MU's power consumption and the BS's licensed channel usage. Despite the non-convexity of the joint optimization problem, we transform it into three rate- allocation problems under different cases and derive the respective optimal solutions, which yield the globally optimal solution for the original problem. Numerical results are provided to validate the optimal offloading-solution.

In this study, the parameter of a symmetric α ‐stable (S α S) noise sequence is modulated by the binary message sequence to achieve a secure communication system. The characteristic exponent ‘ α ’ of an S α S noise sequence carries the binary information. In order to recover the binary message sequence at the receiver, the authors propose a logarithmic moments estimator to estimate the characteristic exponent ‘ α ’ of the transmitted noise sequence. The optimal decision threshold and the minimum theoretical bit error ratio are derived. It is shown that the simulation results of the presented logarithmic moments estimator are consistent with the analytical results. Moreover, this estimator shows better performance and lower computational complexity than the conventional SINC estimator based on the fractional low‐order moment method.

Cooperative communication can significantly improve the performance of wireless network through the help of relay nodes. Choosing a good relay is very important in cooperation. However, for user mobility, both inter-user channel and channel between relay and destination will vary quickly. So relay will not be used all the time. It will be changed when it is no longer suitable to be a relay. A relay handoff algorithm based on energy gain and direction information is proposed in this paper. In this algorithm, handoff is performed when the energy gain of the working relay falls below the threshold and the energy gain of the best relay exceeds the energy gain of the working relay by a variable hysteresis level assisted by direction information. The simulation and analytical results show that this algorithm can reduce the average number of handoffs while keeping the energy gain at a good level .

In this paper, we propose an opportunistic spectrum sharing protocol that exploits the situation when the primary system experiences weak channel conditions. Specifically, when the outage rate of the primary systeme falls below the target rate, the secondary system tries to help the primary system achieve its target rate by acting as a decode-and-forward relay for the primary system, and allocating a fraction of its subcarriers to forward the primary signal. As a reward, the secondary system gains spectrum access by using the remaining subcarriers to transmit its own signal. We study the joint optimization of the set of subcarriers used for cooperation and the secondary subcarrier power allocation such that the transmission rate of the secondary system is maximized, while guaranteeing the primary system to achieve its target rate. Simulation results demonstrate that both primary and secondary systems can benefit from the proposed secondary spectrum access scheme.