This paper investigates latency-constrained resource synergization for mission-oriented non-terrestrial networks (NTNs) in post-disaster emergency scenarios. When terrestrial infrastructures are damaged, unmanned aerial vehicles (UAVs) equipped with edge information hubs (EIHs) are deployed to provide temporary coverage and synergize communication and computing resources for rapid situation awareness. We formulate a joint resource configuration and location optimization problem to minimize overall resource costs while guaranteeing stringent latency requirements. Through analytical derivations, we obtain closed-form optimal solutions that reveal the fundamental tradeoff between communication and computing resources, and develop a successive convex approximation method for EIH location optimization. Simulation results demonstrate that the proposed scheme achieves approximately 20% cost reduction compared with benchmark approaches, validating its optimality and effectiveness for mission-critical emergency response applications in the sixth-generation (6G) era.

In industrial automation or emergency rescue, sensors and robots work together with the help of an edge information hub (EIH) containing both communication and computing modules. Typically, the EIH collects the sensing data via the sensor-to-EIH link, processes data and then makes decisions on board before sending commands to the robot via the EIH-to-robot link. This forms a sensing-communication-computing-control (SC3) closed loop. In practice, the inherent openness of wireless links within the closed loop leads to susceptibility to eavesdropping. To this end, this paper refines the conventional physical layer security (PLS) approach with a systematic thinking to safeguard the SC3closed loop. The closed-loop negentropy (CNE), a new metric for the performance of the whole SC3closed loop, is maximized under the closed-loop security constraint. The transmit time, power, bandwidth of both wireless links, and the computing capability, are jointly designed. The optimization problem is non-convex. We leverage the Karush-Kuhn-Tucker (KKT) conditions and the monotonic optimization (MO) theory to derive its globally optimal solution. Simulation results show the performance gain of the proposed systematic approach, and reveal the advantage of exploiting the closed-loop structure-level PLS over the link-level or sum-link-level designs.

Future sixth generation (6G) communications are expected to support robotic control tasks in applications such as industrial automation and emergency response, where sensors, computing units, and robots are interconnected via nervous system-like networks to form sensing-communication-computingcontrol (SC 3 ) closed loops.However, the limited battery capacities of devices within these SC 3 loops constrain operational duration and degrade control efficiency, particularly in remote or postdisaster scenarios.To address this challenge, wireless power transfer (WPT) can be leveraged to provide continuous energy supply for SC 3 closed loops.In this paper, we investigate a wireless-powered SC 3 system, where a satellite transfers energy via radio frequency (RF) signals to support the communication and computing processes of multiple SC 3 closed loops.By accounting for the intricate coupling among computing, communication, and energy transfer, we propose a holistic design framework to enhance overall control performance.Specifically, we adopt the linear quadratic regulator (LQR) cost as the performance metric and formulate a sum LQR cost minimization problem.The uplink/downlink transmit power, bandwidth allocation, computing capability, communication/computing time allocation, and WPT power allocation are jointly optimized.We recast the problem into a more tractable form and develop an iterative algorithm to solve it.For the special case of a single loop, we further analyze the properties of optimal solutions in energylimited scenarios to provide insights for practical parameter configuration.Simulation results demonstrate the performance gains of the proposed scheme.

In covert communications, the position of the warden has a variety of situations, which leads to different scenes that require different covert communication schemes to ensure the security. In response to this situation, in this paper, a beamforming optimization method of covert communications for multi-position warden is proposed. Firstly, we formulate a general optimization problem and optimize it to maximize the covert communication rate of the user based on Dinkelbach’s transform. Subsequently, according to the optimized general optimization problem, we propose three schemes for three scenes corresponding to different fixed warden positions, using appropriate technologies for assistance in each scheme. Specifically, the intelligent reflecting surface (IRS) is used in Scene 1 and the integrated communication and jamming (ICAJ) is used in Scenes 2 and 3, and these technologies can assist the covert communication. Moreover, we propose an alternate optimization (AO) algorithm to solve the optimization problem of Scene 1 for its optimal covert communication performance. Additionally, we also propose an AO algorithm to solve the optimization problems of Scenes 2 and 3 to optimize the active beamforming. Simulation results demonstrate the effectiveness of all three proposed schemes, that outperform their respective benchmark schemes.

This letter proposes an efficient tensor decomposition scheme, termed tensor flower (TF), for rapid matrix format factorization of large-scale spectrum environment data. TF leverages the divide-and-conquer methodology to break down higher-order tensors into lower-order components to complete matrixized tensor decomposition. This is achieved by representing the tensor as an ordered collection of factor matrices resembling an inflorescence structure. Then, a streamlined algorithm based on alternating least-squares (ALS) is devised to validate the feasibility, while a hierarchical algorithm with adaptive ranks (HAR) is developed to achieve faster TF decomposition. Simulation results demonstrate that TF, as a general-purpose tensor decomposition scheme, can efficiently process large-scale spectrum environment data.

High-mobility and large-bandwidth applications at high frequencies make the acquisition of doubly-selective channels costly and complex, as the fast fading channel causes a surge in pilot overheads and inter-carrier interference. Accurate estimation of the complex channel gain (CG) and the carrier frequency offset (CFO) is required to support subsequent high-accuracy channel prediction that alleviates the heavy pilot burden. The emerging technology of the wireless channel map (WCM) can approximately reproduce the actual propagation environment digitally with customized channel parameters, offering an opportunity to accurately estimate the complex CG and CFO. In this paper, a WCM-based prior distribution construction method and a joint complex CG and CFO estimation algorithm are proposed. Specifically, a parameterized linear estimation problem for the complex CG is generated based on a nonuniform delay-domain off-grid channel representation, and with more realistic prior distributions constructed by the knowledge from the WCM-provided angular-delay power spectrum density, the joint estimation problem is solved under the Bayesian inference framework. Simulation results demonstrate the superiority of the proposed algorithm, with a better performance in terms of estimation accuracy and bit error rates (BER) than existing baselines. It is also verified that the proposed WCM-based algorithm is highly adaptable to different WCM precision and robust to different user speeds.

In emergency scenarios with incapacitated (compromised/ absent) fixed communication infrastructure, ensuring reliable on-demand and dynamic network coverage becomes critical for rapid and effective disaster management. This paper proposes a novel framework for deploying network-in-a-box (NIB) solution using a hybrid model that integrates ground-based NIBs for accessible areas and airborne NIBs for restricted areas. We adopt priority-based non-orthogonal multiple access (PB-NOMA) for the multi-user multi-antenna downlink/uplink (DL/UL) communications between NIBs and single-antenna ground users. We further employ simultaneous wireless information and power transfer (SWIPT) for the priority user (PUs) to replenish their device batteries for continued operation while transferring wireless information to the general users (GUs). Our proposed approach addresses the key challenges such as terrain-aware rapid deployment, dynamic resource allocation, complete/partial power outages, and seamless connectivity with the aim to maximize the generalized global energy efficiency (GGEE). Our simulation results demonstrate the effectiveness of the adopted system to provide robust on-demand communications while dynamically adapting to user traffic demands, environmental constraints, and emergency scenarios with up to 60%, 33%, 100%, and 35% improvement in average GGEE, spectral efficiency, energy efficiency, and throughput, respectively.

Channel models with a good balance of pervasiveness, accuracy, and efficiency are important for the design and optimization of the sixth generation (6G) wireless communication systems. In this paper, a pervasive beam domain channel model (BDCM) capable of modeling all frequency bands and scenarios in 6G is proposed. Unlike traditional geometry-based stochastic models (GBSMs) that describe channels between antenna pairs in the space domain, the pervasive BDCM reformulates the channel in terms of beam pairs to describe special channel characteristics in the beam domain, such as sparsity and Doppler insensibility. The proposed BDCM incorporates essential spatial wideband and spherical wavefront effects for ultra-massive multiple-input multiple-output (MIMO) by considering the nonlinear phase variations across antenna arrays. The pervasive transform matrices for different antenna configurations are derived to enable flexible conversions between the pervasive GBSM and pervasive BDCM. In addition, key statistical properties of the BDCM are derived and analyzed. The proposed pervasive BDCM in different frequency bands and scenarios are validated by measurement data and compared with the GBSM results. The complexity analysis reveals that the proposed pervasive BDCM significantly reduces the computational complexity compared with the pervasive GBSM under different scatterer densities.

Coordinated controlling a large UAV swarm requires significant spectrum resources due to the need for bandwidth allocation per UAV, posing a challenge in resource-limited environments. Over-the-air (OTA) control has emerged as a spectrum-efficient approach, leveraging electromagnetic superposition to form control signals at a base station (BS). However, existing OTA controllers lack sufficient optimization variables to meet UAV swarm control objectives and fail to integrate control with other BS functions like sensing. This work proposes an integrated sensing and OTA control framework (ISAC-OTA) for UAV swarm. The BS performs OTA signal construction (uplink) and dispatch (downlink) while simultaneously sensing objects. Two uplink post-processing methods are developed: a control-centric approach generating closed-form control signals via a feedback-looped OTA control problem, and a sensing-centric method mitigating transmission-induced interference for accurate object sensing. For the downlink, a non-convex problem is formulated and solved to minimize control signal dispatch (transmission) error while maintaining a minimum sensing signal-to-interference-plus-noise ratio (SINR). Simulation results show that the proposed ISAC-OTA controller achieves control performance comparable to the ideal optimal control algorithm while maintaining high sensing accuracy, despite OTA transmission interference. Moreover, it eliminates the need for per-UAV bandwidth allocation, showcasing a spectrum-efficient method for cooperative control in future wireless systems.

With the rapid growth in electromagnetic device quantities, various forms of communication interference have emerged, significantly impacting the accuracy of signal classification. Existing classification algorithms mainly focus on unintentional interference, such as co-channel interference and noise, with limited research on the problem of malicious interference in Multiple Input Multiple Output (MIMO) signal classification. This study proposes an intelligent MIMO signal classification algorithm based on fractional graph feature fusion. Initially, a high-order cumulant tensor model is constructed and regularized tensor decomposition is applied to reconstruct the MIMO signals. Subsequently, a feature extraction model using a fractional wavelet scattering network is designed to effectively capture the distinguishing features of signal constellations. Finally, a collaborative representation classifier based on the Grassmann manifold is utilized to amplify the differences between modulation categories, thereby improving classification performance. Simulation results indicate that the proposed algorithm effectively suppresses common communication interference and successfully classifies MIMO signals. Compared to existing methods, the proposed approach demonstrates significant performance improvements without requiring prior knowledge, such as noise power or channel coefficients.

For future sixth generation (6G) space-air-ground-sea integrated networks, leveraging troposcatter for beyond line-of-sight (BLoS) propagation offers a promising solution to achieving long-range unmanned aerial vehicle (UAV) communications. In this paper, a three-dimensional (3D) multiple-input multiple-output (MIMO) geometry based stochastic model (GBSM) is proposed for long-range UAV-to-ground (U2G) communications. The proposed channel model incorporates UAV 3D mobility and troposcatter characteristics, enabling a distance-dependent transition from visual-range to BLoS communications. A path loss model that accounts for stochastic obstacle deployments and troposcatter characteristics is developed, and the corresponding link budget is calculated to evaluate the feasibility of BLoS communications. Furthermore, troposcatter clusters are modeled and their space-time evolution is characterized to capture channel non-stationarity. Statistical properties, including space-time-frequency correlation function (STF-CF), delay/Doppler power spectral densities (PSDs), root mean square (RMS) delay/Doppler spreads, and coherence bandwidth/time, are also derived and analyzed. The proposed channel model provides valuable guidance for the design of long-range U2G communications.

Cooperation between satellite and terrestrial wireless networks promises great potential in meeting fast-growing demands for ubiquitous communications coverage. To tackle spectrum scarcity, spectrum sharing is studied for a hybrid satellite-terrestrial network where satellite links share the same group of time-slotted subcarriers with terrestrial links opportunistically. In particular, with coarse network-wide time synchronization, a time-scale-adaptable spectrum sharing framework is proposed based on a satellite-terrestrial cooperation time scale that can be flexibly adjusted according to practical requirements. For generality, it is assumed that both full and partial frequency reuse could be adopted among the base stations (BSs) and satellite selection is supported when multiple satellites are available. Relying on only statistical channel state information (CSI), joint link scheduling and power control are explored to maximize the average sum rate of the network while ensuring quality of service (QoS) for users. To solve the complicated mixed integer programming (MIP) problem, a low-complexity spectrum sharing scheme is presented based on link-feature-sketching-aided hierarchical link clustering and Monte-Carlo-and-successive-approximation-aided transmit power optimization. Simulation results demonstrate that by link feature sketching, diversity of the links brought by the spatial distribution of the users could be well utilized. The proposed scheme promises a significant performance gain even under strict inter-link interference constraints.

Cell-free (CF) massive multiple-input multiple-output (M-MIMO) provides an almost uniformly high data rate for all user equipment (UE) through multiple access points (APs), with a non-negligible signal processing burden. Angle domain transmission and channel maps promise to alleviate this burden by reducing channel dimensions in the angle domain and providing$a$priori channel information, respectively. In this paper, we propose a channel map-based angle domain multiple access scheme for uplink CF M-MIMO communications. First, we propose a twostage data reception and pilot assignment scheme constituting receive combining and large-scale fading decoding (LSFD) to reduce overall interference and maximize spectral efficiency (SE). Furthermore, we construct two channel map-based transmission mechanisms by wielding different levels of channel information, where a tailored data reception scheme with a newly derived SE upper bound is also proposed for quantitative evaluation. Simulation results show that the proposed schemes outperform both their space domain alternatives and those without using channel maps in terms of SE.

Terahertz (THz) communications has been recognized as a key enabling technology for the sixth-generation (6G) networks. Research on communication channels in the THz band (0.1–10 THz) remains limited. This paper investigates the propagation characteristics of THz signals in both indoor and outdoor environments using high-fidelity simulations via Wireless Insite. Key metrics, including path loss, root mean squared (RMS) delay spread, and impulse response, are analyzed across multiple frequencies (90 GHz to 140 GHz) under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The results reveal significant differences in signal attenuation, multipath effects, and temporal dispersion between indoor and outdoor scenarios, which directly impact the performance of THz systems. The findings provide valuable insights into the design and optimization in THz bands.

Sensing is regarded as a key function in state-of-the-art communications systems, such as integrated sensing and communications (ISAC). This paper presents new results on the modeling of the sensing channels with or without clutters, in the sub-THz bands (28, 60, 77, and 94 GHz) using the WaveFarer software from Remcom. For the sensing channel, the range-Doppler map, the root mean squared delay spread and the sensing received power are considered. Both indoor and outdoor cases are examined. The indoor and outdoor cases include object only and object-plus-clutter scenarios. The effects of different channel parameters are discussed. Numerical results show that clutter negatively impacts ranging and tracking resolutions. Additionally, higher frequencies improve sensing performance but result in more severe path loss. To design an efficient ISAC system, careful selection of frequency is required.

As the electromagnetic environment becomes more complex, a significant number of interferences and malfunctions of authorized equipment can result in anomalies in spectrum usage. Utilizing intelligent spectrum technology to sense and identify anomalies in the electromagnetic space is of great significance for the efficient use of the electromagnetic space. In this paper, a method for intelligent sensing and identification of anomalies in spectrum with alpha‐stable noise is proposed. First, we use a delayed feedback network (DFN) to suppress alpha‐stable noise. Then, we use a long short‐term memory (LSTM) autoencoder‐based attention mechanism to sense anomaly. Finally, we use the deep forest model to identify abnormal spectrum. Simulation results demonstrate that the proposed method effectively suppresses alpha‐stable noise, and it outperforms existing methods in abnormal spectrum sensing and identification.

Backscatter communication (BC) is a promising method of providing low-power interconnection for green or Internet of Things applications. To combat the strong interference from the direct link, it often uses a small constellation size for modulation, leading to a low data rate. In this paper, backscatter index modulation (BIM) is proposed to increase the data rate of BC by employing multiple single-antenna tags and activating one tag a time to carry extra data bits for information delivery. Both coherent detection with full channel knowledge and non-coherent detection with partial channel knowledge are studied. In each case, ambient backscatter communication with either Gaussian distributed or constant modulus source signals, as well as dedicated backscatter communication with known source signals, are considered. The optimal detectors are derived using the maximum likelihood principle, and their symbol error rate (SER) performances are analyzed in closed-form expressions. Numerical results are presented to examine the effects of different system parameters on the SER. In particular, BIM performs well in coherent detection with full channel knowledge but could quickly reach error floors in non-coherent detection with partial channel knowledge. Also, the number of tags is more important than the number of samples in some cases. The channel asymmetry in the tag-to-reader links can also play an important role in the SER performance.

Underwater acoustic (UWA) channel models are indispensable for the design of UWA communication systems and technologies. In this paper, a novel non-stationary three-dimensional (3D) twin cluster geometry-based stochastic model (GBSM) is proposed for multiple-input multiple-output (MIMO) UWA communication systems in shallow seas. The distribution of non-line-of-sight (NLoS) delays obeys the Nakagami distribution in this model according to the results generated by Bellhop. In addition, the relationship between delay and power is modeled as a negative exponential distribution with different parameters for each NLoS component. The periodic mobility of clusters and distribution of scatterers, caused by the fluctuations of sea surface, are considered to account for the unique UWA environment. Channel statistical properties, such as the space-time-frequency correlation function (STFCF), Doppler power spectrum density (PSD), Doppler spread, coherence time, and coherence distance, are investigated. In particular, temporal autocorrelation function (TACF) and frequency correlation function (FCF) are compared with measurement data to validate the accuracy of this model. Simulation results show that the fluctuating sea surface can cause significant changes in UWA channel characteristics, making it indispensable in channel modeling.

High-accuracy indoor localization is a key enabler of ubiquitous location-based services (LBSs) in the Internet of Things (IoT), with applications in mobile robots, asset tracking, and beyond. For indoor localization, it has been reported that the methods based on probabilistic models have high localization accuracy and strong generalization in the presence of nonline-of-sight (NLOS) conditions and multipath effects. To further leverage such advantages, this article proposes two fusion localization methods based on Bayesian filters which fuse an inertial measurement unit (IMU) motion model with a probabilistic model constructed by soft information (SI) framework to enhance localization performance. First, we propose a method based on particle filter (PF) to directly fit the posterior probability density distribution (PDF), called PF-SI. This method reduces accuracy loss caused by linearization and achieves high accuracy. Then, to reduce the high computational complexity of the PF-SI method, we utilize an error state Kalman filter (ESKF) to construct linearized error state transition and error observation equations and update the filter with distance residual, as ESKF-SI. This method has slightly lower localization accuracy but significantly improves computational efficiency. Finally, experimental results in a real indoor scenario based on ultrawideband (UWB) signals are presented. The results show that the two proposed fusion methods can achieve a root mean square localization error of less than 0.25 m in a complex NLOS scenario.

Efficient implementation of massive multiple-input-multiple-output (MIMO) transceivers is essential for the next-generation wireless networks. To reduce the high computational complexity of the massive MIMO transceiver, in this paper, we propose a new massive MIMO architecture using finite-precision arithmetic. First, we conduct the rounding error analysis and derive the lower bound of the achievable rate for single-input-multiple-output (SIMO) using maximal ratio combining (MRC) and multiple-input-single-output (MISO) systems using maximal ratio transmission (MRT) with finite-precision arithmetic. Then, considering the multi-user scenario, the rounding error analysis of zero-forcing (ZF) detection and precoding is derived by using the normal equations (NE) method. The corresponding lower bounds of the achievable sum rate are also derived and asymptotic analyses are presented. Built upon insights from these analyses and lower bounds, we propose a mixed-precision architecture for massive MIMO systems to offset performance gaps due to finite-precision arithmetic. The corresponding analysis of rounding errors and computational costs is obtained. Simulation results validate the derived bounds and underscore the superiority of the proposed mixed-precision architecture to the conventional structure.

In recent decades, cyber-physical systems (CPSs) have received great attention due to their broad applications. This paper investigates CPS deployment in remote areas, specifically focusing on a digital twin-empowered unmanned robotic system. The system consists of a multifunctional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that connects sensors and actuators—forming reflex-arc-like sensing-communication-computing-control (SC3) loops. A digital twin is integrated into the EIH to emulate the system’s behavior and assist in the decision-making. To alleviate resource limitations in remote areas, we propose a goal-oriented closed-loop optimization scheme. The proposed scheme takes the SC3loop as an integrated structure and jointly optimizes uplink and downlink (UL&DL) communication and computing resources to minimize the total linear quadratic regulator (LQR) cost. To address the non-convex optimization problem, we derive the closed-form solution for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate the superiority of the proposed scheme, which achieves a two-tier task-level balance within and across the SC3loops.

An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial base stations or mobile edge computing (MEC), the EIH would direct the operations of robots via sensing-communication-computing-control ($\textbf {SC}^{3}$) closed-loop orchestration. This paper aims to optimize the closed-loop control performance of multiple$\textbf {SC}^{3}$loops, with constraints on satellite-backhaul rate, computing capability, and on-board energy. Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources. We first derive the optimal splitting ratio of sensor data, and then recast the problem to a more tractable form. An iterative algorithm is finally proposed to provide a sub-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm. We also uncover the influence of$\textbf {SC}^{3}$parameters on closed-loop controls, highlighting more systematic understanding.

Channel state information (CSI) is important to reap the full benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The traditional channel estimation methods using pilot frames (PF) lead to excessive overhead. To reduce the demand for PF, data frames (DF) can be adopted for joint channel estimation and data recovery. However, the computational complexity of the DF-based methods is prohibitively high. To reduce the computational complexity, we propose a joint channel estimation and data recovery (JCD) method assisted by a small number of PF for mmWave massive MIMO systems. The proposed method has two stages. In Stage 1, differing from the traditional PF-based methods used for precise estimation of channel parameters, the proposed PF-assisted method is utilized to narrow down the search range for the angle of arrival (AoA) of principal components (PC) of channels. In Stage 2, JCD is designed for parallel implementation based on the multi-user decoupling strategy. The theoretical analysis demonstrates that the PF-assisted JCD method can achieve equivalent performance to the Bayesian-optimal DF-based method, while greatly reducing the computational complexity. Simulation results are also presented to validate the analytical results.

Coded computation has attracted significant attention because it can eliminate the stragglers’ effect effectively. Most existing works of coded computation are designed for linear tasks, such as matrix multiplication. They cannot handle non-linear tasks directly, leading to high computation, transmission and decoding latency. This is not suitable for latency-sensitive services. In this paper, considering a non-linear task in wireless heterogeneous networks, we propose an efficient merged-rLuby transform (LT) coded computation scheme based on the rateless and sparse LT code. First, we give the merged-rLT coding strategy to reduce the computation and transmission costs. Then, the maximum degree decoding (MDD) strategy is proposed to speed up the decoding process. Finally, we analyze the latency performance for the whole network by designing the optimal merging parameter and sub-block size. The wireless non-linear merged-rLT coded computation (WNLMrLTCC) algorithm minimizes the total latency. Theoretical analysis and numerical simulation show that our proposed scheme has significant advantages over the existing ones for non-linear tasks.

Indoor positioning is an important application of integrated sensing and communication technology in the sixth generation (6G) wireless communications. To address the limitations of existing fingerprint-based positioning methods (FPMs) under severe multipath effects, a novel channel state information (CSI)-based channel fingerprint structure and a novel line-of-sight (LoS)/non-LoS (NLoS) identification-assisted positioning method (IAPM) is proposed for 6G indoor multiple-input multiple-output (MIMO) communications. The proposed channel fingerprint structure uses the proposed maximum received power path to enhance the feature discrimination. The proposed IAPM incorporates a LoS/NLoS identification module and an improved weighted random forest (IWRF) positioning algorithm for accurate positioning. Evaluations on both channel measurement data and channel synthetic data generated by ray tracing demonstrate that the proposed method achieves superior accuracy and robustness compared with widely used FPMs.

Cloud-edge collaborative computing has emerged as a promising solution to satisfy the demands for intensive computation and low latency. However, the straggler effect in cloud-edge collaborative computing systems is serious but has not been addressed. This can be mitigated by designing an effective computing scheme, with the assistance of cloud server. Moreover, the problem of transmission failure becomes more severe in cloud-edge collaborative computing systems. Specifically, the result downloaded from the edge nodes will be affected by poor channel conditions, while the result transmitted from the cloud to edge nodes will also suffer from packet loss. In this paper, we propose a coding assisted cloud-edge collaborative computing (CA-CECC) scheme to solve these problems. To make it more tractable, we separate the transmission phase from the computation phase, and decouple cooperative transmission from single-point transmission. By dividing the set of cooperative nodes into two sets, we further propose two simplified schemes of low complexity. Their latencies are established as upper bounds on the latency of CA-CECC. Numerical simulation results verify the superiority of our proposed scheme.

In wireless communications, the communication channel between the transmitter and receiver can be monitored by an eavesdropper. The eavesdropper uses deep learning (DL) to quickly identify the modulation parameters of signals and further disrupt legitimate communications. Since DL has been proven to be vulnerable to adversarial attacks, this paper proposes to attack the eavesdropper’s model by designing adversarial waveforms, preventing the eavesdropper from correctly identifying the modulation schemes used by legitimate users, and thereby preventing the eavesdropper from interfering with normal communications. This paper proposes an attention-based black-box attack method, which uses the prediction of different networks in the ensemble model to assign adversarial attention factors to each network. This greatly improves the transmission attack performance of the designed adversarial examples. In addition, by analysing the influence of the channel on the adversarial waveform, we further design the adversarial waveform that can be transmitted in the channel to improve the practicability of the attack algorithm. Finally, we theoretically derive the bounds of the adversarial risk increase that the attack brings to the target model. Simulation results show that the proposed method can improve the success rate of the attack on the eavesdropper’s modulation detection model, cause the model to misidentify the signal modulation type, and improve the security and reliability of legitimate transceivers in wireless communication systems.

In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) and a beam domain channel model (BDCM) for sixth-generation (6G) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) systems are proposed. The spherical wavefront and space-time-frequency non-stationarity introduced by massive MIMO, movements of the user and clusters, and large bandwidth of mmWave communications are incorporated. The shared clusters between the sensing channel and communication channel caused by the scattering characteristics are also considered. Based on the proposed channel model, important statistical properties are derived and simulated, including the space-time-frequency correlation function (STF-CF), root mean square (RMS) beam spread, RMS delay spread, RMS Doppler spread, coherence time, and channel capacity. By comparing the statistical properties of the sensing channel and the communication channel, it is found that the sensing channel exhibits more significant temporal non-stationarity. Moreover, the distribution of clusters for the sensing channel and communication channel shows significant difference, which is confirmed by the simulation results of RMS beam spread and RMS delay spread.

With the development of science and technology, the amount of data has experienced explosive growth, and the localization of communication emitters under the backdrop of big data has become a hot topic of research. To solve this problem, we propose a fingerprint matching method based on the nearest neighbor correlation coefficient (MCC-KNN) in this paper. Firstly, we model the existing anomalous data in the process of localizing communication emitters, and we filter out the anomalies using a method based on generalized nuclear norms and Laplace scale mixture. Then, based on the emitter fingerprint after excluding abnormal data, we calculate the correction factor using the initial fingerprint database to create the corresponding fingerprint database. Finally, we utilize the emitter based on the MCC-KNN fingerprint matching method to accomplish communication emitter localization. Simulation results show that the proposed method has superior location accuracy compared to existing methods.

To reduce the energy used in minimum frequency shift keying (MSK) signal reception and enable green communications, we propose a new joint blind estimation method of MSK modulation parameters in the presence of alpha-stable noise for green communications in this paper. Firstly, the generalized second-order cyclic statistics (GSOCS) of the MSK signal is calculated where the received MSK signal is transformed nonlinearly in this calculation that alpha-stable noise in the received signal can be eliminated. Secondly, the specific time delay cross section of the GSOCS is extracted, and the related cyclic frequency set is obtained by using adaptive double threshold detection. Using these values, the modulation frequency interval is estimated using the spacing of adjacent cyclic frequencies in the cyclic frequency set, and the symbol period is estimated according to the modulation index of the MSK signal. The performances of these estimators are evaluated by deriving their corresponding Cramér-Rao lower bound (CRLB). Moreover, the asymptotic properties of modulation frequency interval and symbol period are analyzed. Simulation results demonstrate that the performance of the proposed method is close to its CRLB in the presence of alpha-stable noise, and it has good estimation accuracy. In particularly, the performance of the proposed method is better than the existing techniques in low generalized signal-to-noise ratio (GSNR).

In emergency rescue scenarios, field robots can be dispatched to enhance rescue operations, and unmanned aerial vehicles (UAVs) can be utilized to serve field robots thanks to their flexibility and on-demand deployment. To support the robots efficiently, UAVs should be equipped with sensors, base stations (BSs), and mobile edge computing (MEC) servers. The whole process of a typical rescue task can be regarded as a sensing-communication-computing-control (SC3) closed loop. In this paper, we focus on the closed-loop performance of SC3loops, which is essential for mission-critical tasks. Specifically, we propose a joint communication and computing resource allocation problem, aiming to minimize the sum linear quadratic regulator (LQR) cost of SC3loops. We prove the convexity of the optimization problem by introducing auxiliary variables. Numerical results are provided to show that our proposed scheme can enhance the system’s control performance. Our work also shows that it is essential to jointly consider the communication, computing, and sensing capabilities in unmanned rescue tasks.

Unmanned aerial vehicle (UAV) assisted wireless communication is essential for the next-generation mobile networks. In coping with the increased dynamics in UAV networks, the design of control information transmission is essential, requiring ultra reliability, low latency, and high security. In this paper, considering both the large-scale path loss and the Nakagami-m small-scale fading, we investigate the secrecy performance of UAV control information transmission in a NOMA ground-air network with an external flying eavesdropper. A spherical secrecy protection zone is set, and the closed-form expressions for average secure BLER and average achievable secrecy throughput are derived. After that, the asymptotic performance in the high SNR regime is analyzed to get more insights. Ultimately, simulation results verify the accuracy of analysis.

Abstract Joint sensing and communications systems have gained significant research interest by merging sensing capabilities with communication functionalities. However, few works have examined the case of multiple users. This work investigates a dual‐user joint sensing and communications system, focusing on the interference between the users that explores the optimal performance trade‐offs through a time‐division approach. Bi‐static radar setting is considered. Two typical strategies under this approach are studied: one in which both users follow the same order of communications and then sensing, and the other in which the tasks are performed in opposite order at two users. In each strategy, the sum rate and the detection probability are evaluated and optimized. The results show that the opposite order strategy offers superior performance to the same order strategy, and they also quantify their performance difference. This research highlights the potential benefits of time‐division strategies and multiple users in joint sensing and communications systems.

Ambient backscatter communication enables low-cost low-rate wireless interconnections for Internet of Things (IoT) applications. In this work, new signal detectors for different cases of ambient backscatter communications are derived. Specifically, both coherent and partially coherent detectors are obtained for Gaussian ambient signals and phase shift keying (PSK) ambient signals. Maximum likelihood detection method and improved energy detection method (including energy detection and magnitude detection as special cases) are adopted. Numerical results show that the energy detection method has the best performance when the ambient signals are Gaussian, while the magnitude detection method has the best performance when the ambient signals are PSK modulated. Both are comparable to the optimum maximum likelihood detection. Numerical results also show that the improved energy detection method is very flexible and that detectors for PSK ambient signals are slightly better than those for Gaussian ambient signals.

Among many envisaged drivers for sixth generation (6G), one is from the United Nation’s Sustainability Development Goals 2030 to eliminate digital inequality. Remote coverage in sparsely populated areas, difficult terrains or emergency scenarios requires on-demand access and flexible deployment with minimal capex and opex. In this context, network-in-a-box (NIB) is an exciting solution which packs the whole wireless network into a single portable and reconfigurable box to support multiple access technologies, such as WiFi, 2G–5G, etc. In this article, we propose low-altitude platform station (LAPS)-based NIBs with stratospheric high-altitude platform station (HAPS) as backhaul. Specifically, backhaul employs nonorthogonal multiple access (NOMA) with superposition coding at the transmitting HAPS and successive interference cancellation (SIC) at the receiving NIBs, whereas the access link (AL) employs superposition coding along with the regularized zero-forcing (RZF) precoding at the NIB in order to elevate the computational overhead from the ground users (GUs). The required number of airborne NIBs to serve a desired coverage area, their optimal placement, user association (UA), beam optimization, and resource allocation are optimized by maximizing the sum rate of the AL while maintaining the quality of service. Our findings reveal the significance of thorough system planning and communication parameters optimization for enhanced system performance and best coverage under limited resources.

The expansion of data coverage and the accuracy of decoding of the narrowband-internet of things (NB-IOT) mainly depend on the quality of channel equalizers. Without using training sequences, blind equalization is an effective method to overcome adverse effects in the internet of things (IoT). The constant modulus algorithm (CMA) has become a favorite blind equalization algorithm due to its least mean square (LMS)-like complexity and desirable robustness property. However, the transmission of high-order quadrature amplitude modulation (QAM) signals in the IoT can degrade its performance and the convergence speed. This paper investigates a family of modified constant modulus algorithms for blind equalization of IoT using high-order QAM. Our theoretical analysis for the first time illustrates that the classical CMA has the problem of artificial error using high-order QAM signals. In order to effectively deal with these issues, a modified constant modulus algorithm (MCMA) is proposed to decrease the modulus matched error, which can efficiently suppress the artificial error and misadjustment at the expense of reduced sample usage rate. Moreover, a generalized form of the MCMA (GMCMA) is developed to improve the sample usage rate and guarantee the desirable equalization performance. Two modified Newton methods (MNMs) for the proposed MCMA and GMCMA are constructed to obtain the optimal equalizer. Theoretical proofs are presented to show the fast convergence speed of the two MNMs. Numerical results show that our methods outperform other methods in terms of equalization performance and convergence speed.

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.

Internet of Things (IoT) based on cognitive radio (CR) exhibits strong dynamic sensing and intelligent decision-making capabilities by effectively utilizing spectrum resources. The federal learning (FL) framework-based modulation recognition (MR) is an essential component, but its use of uninterpretable deep learning (DL) introduces security risks. This article combines traditional signal interference methods and data poisoning in FL to propose a new adversarial attack approach. The poisoning attack in distributed frameworks manipulates the global model by controlling malicious users, which is not only covert but also highly impactful. The carefully designed pseudo-noise in MR is also extremely difficult to detect. The combination of these two techniques can generate a greater security threat. We have further advanced our proposal with the introduction of the new adversarial attack method called “chaotic poisoning attack” to reduce the recognition accuracy of the FL-based MR system. We establish effective attack conditions, and simulation results demonstrate that our method can cause a decrease of approximately 80% in the accuracy of the local model under weak perturbations and a decrease of around 20% in the accuracy of the global model. Compared to white-box attack methods, our method exhibits superior performance and transferability.

Cognitive radio-enabled Internet of Things (CR-IoT) is considered as a promising technology to handle spectrum scarcity for IoT applications. Spectrum sensing enables unlicensed secondary users to exploit spectrum holes under the condition of avoiding interference with primary users in CR-IoT networks. Previous studies often assume that the noise is Gaussian while ignoring the influence of non-Gaussian noise. Moreover, multi-antenna-based spectrum sensing algorithms only consider the partial information of covariance matrix. This paper develops two multi-antenna-based spectrum sensing schemes, using fractional low-order covariance matrices to address the issue of performance degradation in impulsive noise. Specifically, the first scheme, namely, diagonal element weighting detection, exploits the diagonal element weighting of the fractional low-order covariance matrix. The latter scheme is called off-diagonal element weighting detection, which adopts the diagonal matrix weighting strategy that exploits the off-diagonal elements of fractional low-order covariance matrices. The approximate analytical expressions of the false alarm probability and detection probability are derived. These developed schemes do not employ any priori knowledge of the primary user signal. Simulation results indicate that two proposed schemes achieve acceptable performance and are robust to the characteristic exponent of the alpha-stable noise, e.g., these proposed methods could achieve a detection probability of 90% with a false alarm probability of 0.1 at GSNR = -16dB, respectively.

Modulation recognition using deep learning (DL) can efficiently recognize modulated signals in cognitive radio-enabled Internet of Things (IoT). However, it is vulnerable to the attack of adversarial examples designed by attackers, leading to a decrease in its accuracy. Different adversarial techniques can be used for attacks, but these attacks have limited efficiency. This article proposes a double loop iterative method. Different from the traditional attack methods, the new method designs an additional external loop iteration for high efficiency. When generating adversarial examples, the initial conditions of each iteration can be updated as the number of iterations changes, so that the adversarial examples can cross the decision boundary of the model as much as possible. In addition, this article uses knowledge distillation to improve the traditional adversarial training defense, which improves the robustness of the model. Simulation results show that the proposed attack and defense methods have better performance than traditional methods.

To provide customized high-quality services for all users in the sixth-generation (6G) wireless communication systems, it is fundamental to study all 6G channel scenarios and establish accurate channel models for these scenarios correspondingly. However, the absence of comprehensive 6G scenario categorization and the difficulties of modeling the channels for all scenarios bring huge challenges. In this article, we aim to give a thorough overview of channel scenarios, identification algorithms, and intelligent channel modeling theories. First, different standardized scenario categorization principles are reviewed. A unified and exclusive scenario categorization method is elaborated with detailed 6G scenario definitions. Second, scenario features, feature selection principles, ML-based identification algorithms, as well as data preprocessing methods are surveyed for the benefit of accurate scenario identification. Third, the intelligent scenario adaptive channel modeling theory based on 6GPCM is specified. Statistical properties for industrial IoT and HST scenarios are simulated and compared with those from measurements. Finally, future research directions and challenges are addressed.

Positioning and sensing over wireless networks are imperative for many emerging applications. However, since traditional wireless channel models over-simplify the user equipment (UE) as a point target, they cannot be used for sensing the attitude of the UE, which is typically described by the spatial orientation. In this paper, a comprehensive electromagnetic propagation modeling (EPM) based on electromagnetic theory is developed to precisely model the near-field channel. For the noise-free case, the EPM model establishes the non-linear functional dependence of observed signals on both the position and attitude of the UE. To address the difficulty in the non-linear coupling, we first propose to divide the distance domain into three regions, separated by the defined Phase ambiguity distance and Spacing constraint distance. Then, for each region, we obtain the closed-form solutions for joint position and attitude estimation with low complexity. Next, to investigate the impact of random noise on the joint estimation performance, the Ziv-Zakai bound (ZZB) is derived to yield useful insights. The expected Cramér-Rao bound (ECRB) is further provided to obtain the simplified closed-form expressions for the performance lower bounds. Our numerical results demonstrate that the derived ZZB can provide accurate predictions of the performance of estimators in all signal-to-noise ratio (SNR) regimes. More importantly, we achieve the millimeter-level accuracy in position estimation and attain the 0.1-level accuracy in attitude estimation.

In wireless networks with distributed computing, the computational performance is limited by stragglers. To mitigate the stragglers’ effect, coded computation is adopted through computational redundancy. Moreover, in wireless transmission, transmission errors may occur due to noise, channel fading and so on. Existing works design coded computation and error detection separately. However, this leads to frequent encoding and inefficient allocation. In this paper, we propose a joint computation and transmission coding (JCTC) scheme to design coded computation and error detection jointly. The coded computation is based on Luby transform (LT) code and linear error-detecting codes are applied for the re-transmission mechanism. To achieve the low dynamic encoding, two-layer encoding is adopted. Then, the performances of JCTC scheme are analyzed in terms of latency and computation reliability. Finally, in order to achieve efficient task and redundancy allocation, the wireless LT coded computation with error detection (WLTCC-ED) algorithm is given from both iterative and low-complexity perspectives respectively. Through theoretical analysis and numerical simulation, it shows that our proposed JCTC scheme has significant advantages over separate designs.

Efficient implementation of Tomlinson-Harashima precoding (THP) is crucial in massive multiple-input-multiple-output (MIMO) systems with a large number of antennas at the base station (BS) serving many user equipments (UEs). To address the high computational complexity of THP, in this paper, we first propose novel THP update algorithms that can avoid recomputing the THP filters when a new UE arrives or departs. Specifically, by using the Gram-Schmidt process and a series of Givens matrices, the THP filters are computed without full matrix operations. Then we extend the THP update algorithms to a more general scenario when multiple multi-antenna UEs arrive or depart. In this case, the proposed algorithms use both direct and iterative approaches. Moreover, the computational complexity of the proposed algorithms is derived and compared with that of the conventional THP. Finally, to further align with the practical scenario, we analyze and derive the approximate close-form expressions for the sum achievable rate of the proposed algorithms under imperfect channel state information (CSI). Simulation results are provided to illustrate the effectiveness of the proposed algorithms. The impact of quasi-static fading and slow time-varying scenarios with imperfect CSI on the communication performance of the proposed algorithms is also evaluated.

Unmanned aerial vehicle (UAV) assisted wireless communication is a key component of the next-generation mobile networks. In coping with the increased dynamics in UAV networks, the transmission of control information is indispensable, requiring not only ultra reliability and low latency, but also high security. In this paper, we investigate the secrecy performance of the control information in a NOMA ground-air short-packet wireless network with an untrusted internal UAV or an external flying eavesdropper, respectively. Both the large-scale path loss and the Nakagami-m small-scale fading are considered. First, the closed-form expressions of the average secure block error rate (BLER) and the average achievable secrecy throughput in each scenario are derived. Then, the asymptotic performance in the high signal-to-noise ratio (SNR) regime is analyzed to get more insights from both scenarios. Specifically, analytical results show that error floors occur with the increase of SNR. Moreover, a one-dimensional search is applied to maximize the average achievable secrecy throughput by optimizing the blocklength. Simulation results are provided to verify the accuracy of analysis and the effectiveness of optimization.

Semantic communication shows great promise in reducing network traffic and alleviating spectrum shortage. While many semantic theories have been put forward, how to measure the importance of semantic information theoretically remains an open issue. In this paper, we propose semantic value, a metric that measures the importance of semantic information, for text transmission. First, we model a semantic communication system for text transmission, in which semantic information is represented by semantic triplets. Then, we propose a hybrid communication mechanism to ensure the success of text transmission. Finally, we compare the performances of the conventional mode and the semantic mode in terms of latency and derive conditions leading to minimum latency.

The performance of multiple-input multiple-output (MIMO) integrated sensing and communication systems (ISAC) can be evaluated from the perspectives of information theory and estimation theory to provide more fundamental insights. In this paper, we study the relationship between mutual information (MI) and minimum mean square error (MMSE) by characterizing the Pareto boundary for a general ISAC scenario, a dual-functional BS simultaneously estimates the target response matrix while communicating with a user. First, optimization problems are formulated to achieve MI Pareto boundary and MMSE Pareto boundary, respectively. Then, we show that under the same maximum transmit power constraint and set of transmit filters, MI Pareto bounary can be transformed to MMSE Pareto boundary with optimized MSE-weights in ISAC with colored Gaussian noise. Subsequently, based on unified MI and MMSE performance, we propose Data-dependent alternate algorithm (DDA) to obtain the MI Pareto boundary with colored Gaussian noise. In order to reduce complexity, we propose Data-independent alternate algorithm (DIA) when noise degenerates into white Gaussian noise. Finally, simulation results show DDA almost achieves the MI Pareto boundary with colored Gaussian noise and DIA achieves almost the same performance as DDA with white Gaussian noise at a lower cost to implement.

Signal identification, a vital task of intelligent communication radios, finds its applications in various military and civil communication systems. Previous works on identification for space-time block codes (STBC) of multiple-input multiple-output (MIMO) system employing orthogonal frequency division multiplexing (OFDM) are limited to additive white Gaussian noise. In this paper, we develop a novel automatic identification algorithm to exploit the generalized cross-correntropy function of the received signals to classify STBC-OFDM signals in the presence of Gaussian noise and impulsive interference. This algorithm first introduces the generalized cross-correntropy function to fully utilize the space-time redundancy of STBC-OFDM signals. The strongly-distinguishable discriminating matrix is then constructed by using the generalized cross-correntropy for multiple receive antennas. Finally, a decision tree identification algorithm is employed to identify the STBC-OFDM signals which is extended by the binary hypothesis test. The proposed algorithm avoids the traditionally required pre-processing tasks, such as channel coefficient estimation, noise and interference statistics prediction and modulation type recognition. Numerical results are presented to show that the proposed scheme provides good identification performance by exploiting the generalized cross-correntropy function of STBC-OFDM signals under impulsive interference circumstances.

Air transportation communication jamming recognition model based on deep learning (DL) can quickly and accurately identify and classify communication jamming, to improve the safety and reliability of air traffic. However, due to the vulnerability of deep learning, the jamming recognition model can be easily attacked by the attacker’s carefully designed adversarial examples. Although some defense methods have been proposed, they have strong pertinence to attacks. Thus, new attack methods are needed to improve the defense performance of the model. In this work, we improve the existing attack methods and propose a double level attack method. By constructing the dynamic iterative step size and analyzing the class characteristics of the signals, this method can use the adversarial losses of feature layer and decision layer to generate adversarial examples with stronger attack performance. In order to improve the robustness of the recognition model, we use adversarial examples to train the model, and transfer the knowledge learned from the model to the jamming recognition models in other wireless communication environments by transfer learning. Simulation results show that the proposed attack and defense methods have good performance.

In unmanned space exploration, the cooperation among space robots requires advanced communication techniques. In this paper, we propose a communication optimization scheme for a specific cooperation system named the “mother-daughter system”. In this setup, the mother spacecraft orbits the planet, while daughter probes are distributed across the planetary surface. During each control cycle, the mother spacecraft senses the environment, computes control commands and distributes them to daughter probes for actions. They synergistically form sensing-communication-computing-control ($\mathbf {SC^{3}}$) loops. Given the indivisibility of the$\mathbf {SC^{3}}$loop, we optimize the mother-daughter downlink for closed-loop control. The optimization objective is the linear quadratic regulator (LQR) cost, and the optimization parameters are the block length and transmit power. To solve the nonlinear mixed-integer problem, we first identify the optimal block length and then transform the power allocation problem into a tractable convex problem. We further derive the approximate closed-form solutions for the proposed scheme and two communication-oriented schemes: the max-sum rate scheme and the max-min rate scheme. On this basis, we analyze their power allocation principles. In particular, for time-insensitive control tasks, we find that the proposed scheme demonstrates equivalence to the max-min rate scheme. These findings are verified through simulations.

Deep learning (DL) has shown great potential in revolutionizing the traditional communications system. Many applications in communications have adopted DL techniques due to their powerful representation ability. However, the learning-based methods can be dependent on the training dataset and perform worse on unseen interference due to limited model generalizability and complexity. In this paper, we consider the semantic communication (SemCom) system with multiple users, where there is a limited number of training samples and unexpected interference. To improve the model generalization ability and reduce the model size, we propose a knowledge distillation (KD) based system where Transformer based encoder-decoder is implemented as the semantic encoder-decoder and fully connected neural networks are implemented as the channel encoder-decoder. Specifically, four types of knowledge transfer and model compression are analyzed. Important system and model parameters are considered, including the level of noise and interference, the number of interfering users and the size of the encoder and decoder. Numerical results demonstrate that KD significantly improves the robustness and the generalization ability when applied to unexpected interference, and it reduces the performance loss when compressing the model size.

This paper investigates an emergency rescue system, which comprises a multi-functional unmanned aerial vehicle (UAV) and multiple robots. The UAV carries sensing, communication, and computing modules. It senses system states, calculates commands, and instructs field robots to take actions. In this way, the UAV and robots form multiple sensing-communication-computing-control $(\mathbf{SC} ^{3})$ loops, which could finish many mission-critical tasks without human participation. To activate these $\mathbf{SC} ^{3}$ loops, we propose a closed-loop optimization scheme. Unlike traditional studies that primarily focus on the communication link, the proposed scheme emphasizes the $\mathbf{SC} ^{3}$ loop and adopts the linear quadratic regulator (LQR) cost as the objective. Focusing on the UAV-robot downlink, we model the data transmission in the finite block length regime and take the transmit power and block lengths as optimization variables. We solve the nonlinear integer problem by exploiting the monotonicity and convexity of the objective rate-cost function. The closed-form solution of the transmit power is derived in the assure-to-be-stable region. On this basis, we compare the proposed scheme with the max-sum rate scheme. Through comparisons, the fairness-minded nature of the proposed scheme is revealed.

Effective spectrum management control through multi-agent deep reinforcement learning holds promising potential for advancing wireless communication systems. However, backdoor attacks can compromise the integrity and security of multi-agent deep reinforcement learning models, allowing attackers to manipulate their behaviour and cause significant damage to the system. In this paper, we have defined a four-step process for designing a general backdoor in spectrum management based on multi-agent deep reinforcement learning, which involves searching for the most observed channels, determining the backdoor power limit, selecting feasible poisoned channels, and setting up induced rewards. Experimental results demonstrate the effectiveness of this attack, which allows the system to perform spectrum management without triggering the backdoor. However, when the backdoor is triggered, it results in severe communication interruptions. Overall, this paper contributes to the field of secure and reliable spectrum management by providing insights into the impact of backdoor attacks on deep learning-based systems.

Applying deep learning (DL) to modulation recognition can significantly improve the efficiency of communication in cognitive radio (CR) systems, but it may be attacked by adversarial examples. The black-box attack has vital practical significance because it does not need to master the parameters and architecture of the target model. The ensemble attack is an essential black-box attack method, which attacks the model by improving the transferability of adversarial examples. However, the existing ensemble attacks only simply adopt the average method when fusing the outputs of different networks, without fully considering the characteristics of the ensemble model, resulting in poor transferability. This paper proposes an attention-based ensemble attack method, which uses the prediction performance of different networks to assign attention factors to express the influence of these networks, so that the example can pass through the decision boundaries of all networks within a limited number of iterations. Simulation results show that the proposed method can improve the transferability of adversarial examples and effectively attack the black-box modulation recognition model.

Signal classification plays a pivotal role in cognitive radio networks. This problem becomes more challenging for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems employing linear space-time block code (STBC). This paper introduces a novel classification scheme to exploit the generalized cross-correntropy function to classify STBC-OFDM signals in the presence of impulsive interference. This scheme relies on the generalized cross-correntropy statistics of the STBC-OFDM signals to construct the strongly-distinguishable discriminating matrix. The proposed scheme avoids the estimation of channel coefficients, noise and interference statistics, and modulation types. Numerical results are presented to show that the proposed scheme provides acceptable classification performance in the presence of Gaussian noise and impulsive interference.

In the orthogonal frequency division multiplexing (OFDM) systems, inter-carrier interference (ICI) caused by carrier frequency offset (CFO) is considered to be one of the most crucial problems for OFDM over multipath channels, which will bring difficulties in the modulation classification of OFDM. In order to deal with the influence of ICI on subcarrier modulation classification, this paper presents a novel blind modulation classification method of OFDM systems with CFO over multipath channels. The virtual subcarrier signals and the pilot subcarrier signals with CFO are classified by using the second-order moment, and then the modulation subcarriers are classified with the fourth-order cumulants and the sixth-order cumulants. Simulations are conducted to verify the proposed method not only has a good classification performance, but also has a low computational complexity. The proposed method can reduce energy consumption and is beneficial for green radios.

Network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for wide-area robots, hybrid satellite-terrestrial networks are a key technology. Via hybrid networks, computation-intensive and latency-sensitive tasks of robots can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading for satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme is to make the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. Simulation results show that the proposed scheme can effectively reduce the service delay.

While 5G networks have accelerated the development of the Internet of Things (IoT), they have also introduced a large number of vulnerable IoT devices into the network, which would lead to severe Distributed Denial-of-Service (DDoS) attacks. The newly emerging DDoS attack methods generally have a shorter duration, which imposes higher requirements for the response time of DDoS mitigation technologies. Existing DDoS defense methods cannot achieve real-time detection due to the difficulty of reducing the delay of feature extraction and large-scale data processing. In this article, we focus on the timeliness of DDoS detection and mitigation. We hope that deploying effective defense countermeasures at the source side will block the majority of DDoS attack traffic in real time before it enters the data network (DN). To this end, we propose a real-time DDoS defense framework based on multidomain collaboration that combines multisource information to detect attack sessions with high accuracy in 5G networks. To operate the framework at line rate, we propose an optimal packet sampling strategy based on the accurate session size estimation, which can greatly reduce the detection overhead while ensuring good accuracy. In a typical scenario with an attack session size larger than 10, this method can achieve a 99% detection rate while reducing the packet inspection rate (PIR) to less than 37%.

The sixth-generation (6G) network is envisioned to integrate communication and sensing functions, so as to improve the spectrum efficiency and support explosive novel applications. Although the similarities of wireless communication and radio sensing lay the foundation for their combination, there is still considerable incompatible interest between them. To simultaneously guarantee the communication capacity and the sensing accuracy, the multiple-input and multiple-output (MIMO) technique plays an important role due to its unique capability of spatial beamforming and waveform shaping. However, the configuration of MIMO also brings high hardware cost, high power consumption, and high signal processing complexity. How to efficiently apply MIMO to achieve balanced communication and sensing performance is still open. In this survey, we discuss joint communication and sensing (JCAS) in the context of MIMO. We first outline the roles of MIMO in the process of wireless communication and radar sensing. Then, we present current advances in both communication and sensing coexistence and integration in detail. Three novel JCAS MIMO models are subsequently discussed by combining cutting-edge technologies, i.e., cloud radio access networks (C-RANs), unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RISs). Examined from the practical perspective, the potential and challenges of MIMO in JCAS are summarized, and promising solutions are provided. Motivated by the great potential of the Internet of Things (IoT), we also specify JCAS in IoT scenarios and discuss the uniqueness of applying JCAS to IoT. In the end, open issues are outlined to envisage a ubiquitous, intelligent, and secure JCAS network in the near future.

Pulse-based integrated sensing and communication (ISAC) systems have the advantages of high ranging resolution and strong resistance to self-interference, compared with continuous wave (CW) based systems. However, for pulse-based ISAC systems, multi-path channels pose various challenges to data recovery and ranging by providing diversity gain for data recovery but incurring the interference to the identification of the first path in ranging. In this paper, we design a pulse-based ISAC receiver for multi-path channels. The designed receiver can obtain the diversity gain by correlating the received signal with the estimated template signal. Meanwhile, it can detect the arrival of the first path by using a threshold detection method based on a constant false alarm rate (CFAR). Furthermore, we extend the pulse-based ISAC design to a low-resolution analog-to-digital converter (ADC) scenario. A low-cost receiver design is provided for the pulse-based ISAC system that can recover data and estimate range simultaneously considering the non-linear effect caused by the low-resolution ADC. Simulation results show that compared with the generalized maximum likelihood (GML) based receiver, the proposed full-resolution pulse-based ISAC receiver has 1dB signal-to-noise ratio (SNR) loss in bit error rate (BER) and almost the same mean squared error (MSE) performance with the significantly reduced computational complexity. Also, compared with the full-resolution ISAC receiver, the ISAC receiver with 3-level quantization incurs only 0.8dB SNR loss in BER and 1dB SNR loss in MSE.

To improve both sensing and communication performances, this paper proposes a coordinated multi-point (CoMP) transmission design for a dual-functional radar-communication (DFRC) system. In the proposed CoMP-DFRC system, the central processor (CP) coordinates multiple base stations (BSs) to transmit both the communication signal and the dedicated probing signal. The communication performance and the sensing performance are both evaluated by the signal-to-interference-plus-noise ratio (SINR). Given the limited backhaul capacity, we study the waveform and clustering design from both the radar-centric perspective and the communication-centric perspective. Dinkelbach's transform is adopted to handle the single-ratio fractional objective for the radar-centric problem. For the communication-centric problem, we adopt quadratic transform to convexitify the multi-ratio fractional objective. Then, the rank-one constraint of communication beamforming vector is relaxed by semidefinite relaxation (SDR), and the tightness of SDR is further proved to guarantee the optimal waveform design with fixed clustering. For dynamic clustering, equivalent continuous functions are used to represent the non-continuous clustering variables. Successive convex approximation (SCA) is further utilized to convexitify the equivalent functions. Simulation results are provided to verify the effectiveness of all proposed designs.

The sixth generation (6G) pervasive channel model (6GPCM) can characterize channels for all spectra from the sub-6 GHz band to the visible light communication (VLC) band and all scenarios, such as maritime, (ultra-)massive multiple-input multiple-output (MIMO), and industrial Internet of things (IIoT) communication scenarios in 6G wireless systems. The unified channel model can enable us to analyze channel statistical properties in systems using different scales of antenna arrays, different frequency bands, and different scenarios with different movement speeds. In this paper, we conduct a complete study on space-time-frequency (STF) statistical properties of the 6GPCM. Mathematical derivations and simulations are provided, including STF correlation function (STFCF), spatial/temporal/frequency correlation functions, angular/Doppler/delay power spectral densities (PSDs), root mean square (RMS) angular/Doppler/delay spreads, coherence distance/time/bandwidth, stationary distance/time/bandwidth, and level-crossing rates (LCRs)/average fade durations (AFDs) in STF domains. In addition, we classify these statistical properties according to their definitions and then reveal the complex relationships between them and channel model parameters. This work will lay a solid foundation and offer useful guidelines for research on 6G wireless communication systems.

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications in the next-generation wireless networks. Considering the massive multiple-input multiple-output (MIMO) configuration, this paper proposes a novel three-dimensional (3D) beam domain channel model (BDCM) for UAV communications. Through dividing the large antenna array into several sub-arrays and classifying multipath components as near-field and far-field components, the proposed BDCM takes the spherical wave front (SWF) and array non-stationarity into account. Channel statistical properties including spatial-temporal-frequency correlation function (STF-CF), root-mean-squared (RMS) Doppler spread, beam spread, channel matrix collinearity (CMC), and stationary time interval are derived and simulated for the proposed BDCM. Influences of SFW and non-stationary properties on the statistical properties and system performance are analyzed. Simulation results show that, compared with the equivalent geometry-based stochastic model (GBSM), the proposed BDCM has better temporal correlation, while BDCM and GBSM are equivalent in the system performance evaluation. Furthermore, the performance of the proposed BDCM is evaluated in terms of accuracy, complexity, and pervasiveness. The results show that the proposed BDCM can represent massive MIMO channel properties accurately with low complexity and good compatibility.

In time-division duplexing (TDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the reciprocity mismatch severely degrades the performance of the hybrid beamforming (HBF). In this work, to mitigate the detrimental effect of the reciprocity mismatch, we investigate reciprocity calibration for the mmWave-HBF system with a fully-connected phase shifter network. To reduce the overhead and computational complexity of reciprocity calibration, we first decouple digital radio frequency (RF) chains and analog RF chains with beamforming design. Then, the entire calibration problem of the HBF system is equivalently decomposed into two subproblems corresponding to the digital-chain calibration and analog-chain calibration. To solve the calibration problems efficiently, a closed-form solution to the digital-chain calibration problem is derived, while an iterative-alternating optimization algorithm for the analog-chain calibration problem is proposed. To measure the performance of the proposed algorithm, we derive the Cramér-Rao lower bound on the errors in estimating mismatch coefficients. The results reveal that the estimation errors of mismatch coefficients of digital and analog chains are uncorrelated, and that the mismatch coefficients of receive digital chains can be estimated perfectly. Simulation results are presented to validate the analytical results and to show the performance of the proposed calibration approach.

Current fifth-generation (5G) networks do not cover maritime areas, causing difficulties in developing maritime Internet of Things (IoT). To tackle this problem, we establish a nearshore network by collaboratively using on-shore terrestrial base stations (TBSs) and tethered unmanned aerial vehicles (UAVs). These TBSs and UAVs form virtual clusters in a user-centric manner. Within each virtual cluster, non-orthogonal multiple access (NOMA) is adopted for agilely including various maritime IoT devices, which are sparsely distributed over the vast ocean. The nearshore network also shares the spectrum with marine satellites. In such a NOMA-based hybrid satellite-UAV-terrestrial network, interference among different network segments, different clusters, and different users occurs. We thereby formulate a joint power allocation problem to maximize the sum rate of the network. Different from existing studies, we use large-scale channel state information (CSI) only for optimization to reduce system overhead. The large-scale CSI is obtained by using the position information of maritime IoT devices. The problem is non-convex with intractable non-linear constraints. We tackle these difficulties by adopting max-min optimization, the auxiliary function method, and the successive convex approximation technique. An iterative power allocation algorithm is accordingly proposed, which is shown to be effective for coverage enhancement by simulations. This shows the potential of NOMA-based hybrid satellite-UAV-terrestrial networks for maritime on-demand coverage.

Nowadays, satellite and aerial platforms are playing an important role in realizing global seamless wireless coverage. In this paper, a cooperative satellite-aerial-terrestrial network (SATN) is considered, in which two kinds of relaying links, satellite and aerial relaying links, are used to assist a group of aerial terminals to forward their information to a remote terrestrial destination (D). Specifically, we model these aerial platforms sharing the same frequency band as a Matérn hard-core point process type-II. Also, a group of aerial jammers at D’s side is modeled as a Poisson point process. To demonstrate the end-to-end (e2e) performance of the two relaying links, the statistical characteristics of the received signal-to-interference are characterized and then a closed-form expression for the outage probability (OP) over the uplink from the aerial source to the satellite/the aerial relay, the downlink from the satellite/the aerial relay to D, and the inter-aerial relay link are derived. Numerical results are presented to verify the proposed analysis models and compare the outage performance of the considered cooperative SATN with the two relay links under numerous scenarios.

Benefiting from the fusion of communication and intelligent technologies, network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for robots in wide areas, hybrid satellite-terrestrial networks are a key technology. Through hybrid networks, computation-intensive and latency-sensitive tasks can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading initiated by satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme makes the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. The convergence and complexity of the improved RL algorithm are theoretically analyzed, and the simulation results show that the proposed scheme can effectively reduce the offloading delay.

This paper proposes a bi-criteria optimisation frame-work that maximises both the network rate and the harvested energy, which are contradictory objectives. Using the practical non-linear energy harvesting (EH) model, we jointly optimise re-lay selection (RS), power splitting (PS) and power allocation (PA). We decouple the relay selection variables from the other resource allocation variables to convert the original mixed-integer non-linear programming (MINLP) problem into a tractable problem. For PS and PA, the well-known ∊-constraint method is applied to convert the bi-criteria problem into a convex problem. For RS, we propose a sub-optimal algorithm based on a selection order function with linear complexity. The simulation results indicate that the proposed schemes perform better than the benchmarks, drastically reducing computational complexity from exponential to polynomial.

Distribution test, a method to evaluate the goodness-of-fit, has been applied to automatic modulation clas-sification (AMC) in recent years. It compares the empirical cumulative distribution function (ECDF) of the sample signal with the theoretical cumulative distribution function (TCDF) under each candidate modulation format. Most existing works in MIMO systems assume the additive noise is Gaussian. However, additive noise often exhibits non-Gaussian characteristics in practical systems. The TCDF of each modulation developed for Gaussian noise can not perform well in practical system. To solve this issue, this paper proposes a practical MIMO system model, which assumes that the noise is Cauchy-Gaussian bi-parameter mixture and analyzes the TCDF corresponding to different modulation modes under this noise model. The Kolmogorov-Smirnov (KS) distribution test is used to AMC in flat-fading channel for both Quadrature Amplitude Modulation and Phase Shift Keying. Extensive simulation results demonstrate that, compared with two-sample KS test based classifier, one-sample KS test based classifier can achieve good recognition results under the non-Gaussian noise model.

Rate-splitting multiple access (RSMA) is an emerging multiple access strategy, with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) as its two special cases. RSMA divides the messages required by users into the private and common parts, and the private streams are naturally suitable for the secure transmission. In this paper, we establish a unified rate-splitting framework to ensure the secure transmission for the three multiple access systems mentioned above. The precoders at the multi-antenna transmitter are conjointly optimized to improve the transmission rate of common message. Successive interference cancellation (SIC) is utilized, and the private message for the downlink broadcasting RSMA network can be effectively hidden in the high-power common message. Simulation results demonstrate that the proposed rate-splitting framework can effectively guarantee the secure transmission of the secrecy information.

Unmanned aerial vehicle (UAV) communication is a promising technology that provides swift and flexible on-demand wireless connectivity for devices without infrastructure support. The proliferation of UAV communication equipment is causing the limited spectrum to become crowded. To deal with this issue, spectrum sharing policy (SSP) is introduced to support UAV communication. Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications. In this paper, we propose spectrum sensing for opportunistic spectrum access in UAV communication to improve the spectrum utilization efficiency. Different from most existing works, we focus on the problem of spectrum sensing with randomly arriving primary signals in the presence of non-Gaussian noise/interference. We propose a novel spectrum sensing scheme to improve the spectrum utilization efficiency in UAV communication. We construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process. Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and are arriving randomly during spectrum sensing in the UAV communication.

The integration of radar and communication system is attracting considerable interest due to spectral scarcity and increasing demand for unified radar and communication applications. Two novel schemes combining channel estimation, radar detection, and information exchange are investigated for unified radar communication systems: channel estimation power splitting (EPS) and information exchange power splitting (CPS). Their achievable rate and tradeoff measure are derived and examined with different parameters. Numerical results show that the optimal time ratio and power ratio for channel estimation, radar detection, and information exchange exist. Also, the CPS scheme has the best performance.

Machine-type communication (MTC) networks face the challenges of massive access and diverse quality of service (QoS) requirements. In this paper, we focus on satisfying various QoS requirements for massive number of MTC devices. By dividing these devices into multiple clusters based on their QoS characteristics, we formulate an access control problem to maximize the access efficiency while satisfying both the access and transmission delay requirements. An efficient algorithm is proposed to solve the problem by adaptively adjusting the access time intervals and back-off factors of the clusters. Simulation results show that the proposed scheme outperforms other schemes in terms of access efficiency. The impacts of various parameters including delay and traffic rate on the performance are disclosed.

Abstract The development of future wireless communications systems faces a big challenge of spectrum scarcity. Co‐existence of radar and communications systems is thus of great interest. In this work, the performance of a communications system with hardware impairment (HWI) as well as interference from radar systems will be studied. The impact of radar interference, I/Q imbalance coefficients as well as channel state information (CSI) will be evaluated in terms of outage probability and symbol error rate. Simulation results prove that the proposed detectors provide explicit and conducive insights for further exploration of joint radar‐communications designs.

Abstract In this paper, machine learning (ML) algorithms are used for channel prediction in wireless communications. The performances of five ML algorithms are compared in terms of the prediction accuracy and the symbol error rate (SER) of different modulation schemes based on the prediction. The result shows that, for channel prediction, support vector machine (SVM) has the best performance in terms of accuracy and stability. For signal detection, SVM and linear regression (LR) have their own advantages in different ranges of signal to noise ratio (SNR). At high constellation size, ML methods give similar performances to existing scheme. From the numerical examples, the SERs based on SVM and LR can both reach lower than 10 −3 in binary phase shift keying and 16‐ary quadrature amplitude modulation signalling, and can reach 1.13 and 4.28 in 16‐ary phase shift keying signalling respectively. In terms of prediction time, SVM is more efficient.

Radio sensing has become increasingly important, as the demand for “smartness” is drastically increasing. Unlike conventional sensing, radio sensing uses existing radio signals or devices to passively sense the ambient environment for low cost and wide deployment. In this article, a comprehensive overview of radio sensing using the recent fifth-generation (5G) signals is provided. 5G systems have many merits, such as high frequency, large bandwidth, massive antenna array, and dense network, making them ideal for radio sensing. In the overview, basic theories and concepts of 5G radio sensing are first introduced. Then, different state-of-the-art 5G sensing works are discussed based on their applications. These applications show that 5G radio sensing represents a step change in radio sensing. After that, several open challenges in 5G radio sensing are illustrated with relevant insights. These insights manifest that 5G radio sensing has great potentials to explore.

Estimating the location parameters of moving target is an important part of intelligent surveillance for the Internet of Vehicles (IoV). Satellite has the potential to play a key role in many applications of space–air–ground-integrated networks (SAGINs). In this article, a novel passive location parameter estimator using multiple satellites for the moving aerial target is proposed. In this estimator, the direct wave signals in reference channels are first filtered by a band-pass filter, followed by a sequence cancelation algorithm to suppress the direct-path interference and multipath interference. Then, the fourth-order cyclic cumulant cross ambiguity function (FOCCCAF) of the signals in the reference channels and the four-weighted fractional Fourier transform FOCCCAF (FWFRFT-FOCCCAF) of signals in the surveillance channels are derived. Using them, the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) are estimated and the distance between the target and the receiver and the velocity of the moving aerial target are estimated by using multiple satellites. Finally, the Cramer–Rao lower bounds of the proposed location parameter estimators are derived to benchmark the estimator. The simulation results show that the proposed method can effectively and precisely estimate the location parameters of the moving aerial target.

Massive connections and diverse Quality of Service (QoS) requirements pose a major challenge for machine-type communication (MTC) networks. In this article, to satisfy the various QoS requirements of a massive number of MTC devices (MTCDs), the devices are divided into multiple clusters based on the QoS characteristics. The cluster access control and intracluster resource allocation problems are studied to satisfy the double delay requirements in the access and data transmission phases in a cross-layer approach. Specifically, we formulate an access control problem to maximize the access efficiency with constraints on access and transmission delays. An efficient algorithm is proposed to adaptively adjust the access time intervals and backoff factors of the clusters for different numbers of active MTCDs and transmission rates. Given the access parameters, nonorthogonal multiple access is adopted in resource allocation to maximize the system utility function while guaranteeing the delay requirements for each accessed MTCD. An efficient sequential convex programming iterative algorithm is proposed to solve the NP-hard nonconvex problem with two typical utility objectives: 1) total throughput and 2) consumed power. Simulation results show that the proposed scheme can achieve better performance in terms of access efficiency, delay, throughput, and consumed power than other schemes. The impacts of various parameters, including delay and traffic rate, on the performance, are disclosed.

Identification of transmit-antenna number is of importance in cognitive Internet of Things (IoT) with multiple-input–multiple-output (MIMO). Previous studies on transmit-antenna number detection only consider Gaussian noise and ignore impulsive interference. In the practical wireless communication, impulsive interference may exist due to low-frequency atmospheric noise, multiple access, and electromagnetic disturbance. Such interference can usually be modeled as symmetric alpha stable ($S\alpha S$), which cause the performance degradation of conventional algorithms based on the Gaussian model. In this article, we present a novel scheme to detect the transmit-antenna number for MIMO systems in cognitive IoT, assuming that signals are corrupted by both$S\alpha S$interference and Gaussian noise. We first introduce a new approach to characterize the generalized correlation matrix (GCM), and provide its bound with$S\alpha S$interference. Then, the discriminating feature vector is constructed by utilizing the higher order moments (HOMs) of eigenvalues of the GCM. Finally, an advanced clustering algorithm is employed to detect the transmit-antenna number, using the cluster where the minimum eigenvalue is located. The proposed algorithm avoids the need fora prioriinformation about the transmitted signals, such as coding mode, modulation type, and pilot patterns. Simulation experiments demonstrate the feasibility of the proposed transmit-antenna number detection scheme in MIMO systems with Gaussian noise and$S\alpha S$interference.

Spatial beamforming is an efficient way to realize dual-functional radar-communication (DFRC). In this paper, we study the DFRC design for a general scenario, where the dual-functional base station (BS) simultaneously detects the target as a multiple-input-multiple-output (MIMO) radar while communicating with multiple multi-antenna communication users (CUs). This necessitates a joint transceiver beamforming design for both MIMO radar and multi-user MIMO (MU-MIMO) communication. In order to characterize the performance tradeoff between MIMO radar and MU-MIMO communication, we first define the achievable performance region of the DFRC system. Then, both radar-centric and communication-centric optimizations are formulated to achieve the boundary of the performance region. For the radar-centric optimization, successive convex approximation (SCA) method is adopted to solve the non-convex constraint. For the communication-centric optimization, a solution based on weighted mean square error (MSE) criterion is obtained to solve the non-convex objective function. Furthermore, two low-complexity beamforming designs based on CU-selection and zero-forcing are proposed to avoid iteration, and the closed-form expressions of the low-complexity beamforming designs are derived. Simulation results are provided to verify the effectiveness of all proposed designs.

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.

This work investigates the secrecy outage performance of the uplink transmission of a radio-frequency (RF)-free-space optical (FSO) hybrid cooperative satellite-aerial-terrestrial network (SATN). Specifically, in the considered cooperative SATN, a terrestrial source (S) transmits its information to a satellite receiver (D) via the help of a cache-enabled aerial relay (R) terminal with the most popular content caching scheme, while a group of eavesdropping aerial terminals (Eves) trying to overhear the transmitted confidential information. Moreover, RF and FSO transmissions are employed over S-R and R-D links, respectively. Considering the randomness of R, D, and Eves, and employing a stochastic geometry framework, the secrecy outage performance of the cooperative uplink transmission in the considered SATN is investigated and a closed-form analytical expression for the end-to-end secrecy outage probability is derived. Finally, Monte-Carlo simulations are shown to verify the accuracy of our analysis.

Computation over multiple access channels (CoMAC) has been proposed to solve the problem of spectrum scarcity in wireless networks, which combines communication and computation efficiently using the superposition property of wireless channels. In this paper, we consider a multi-cluster CoMAC network, whose performance is affected by the inter-cluster interference and the non-uniform fading. To minimize the sum mean squared error of signals aggregated at different fusion centers (FCs), we propose a transceiver design for multi-cluster CoMAC. Specifically, we adopt a uniform-forcing transmitter design to formulate the receiver design as a quadratic sum-of-ratios problem with nonconvex quadratic constraints. Then, we propose a branch-and-bound algorithm to find its optimal solution with a given error tolerance. To solve the problem in a decentralized way, we develop a distributed algorithm based on the primal decomposition theory. Each subproblem is solved by using the successive convex approximation method. Further combining Lagrange duality, we derive the optimal solution structure of each subproblem, based on which we can find the solution with lower complexity. Simulation results demonstrate the effectiveness of the proposed distributed transceiver design.

The performance of distributed computing is restricted by the slowest worker nodes, known as stragglers, in the system. Coded computation has emerged as an efficient technique to mitigate the straggler effects in distributed computing. Most existing works only considered the computation straggler for single-hop networks. However, in multi-hop networks, the straggler effects will occur not only on worker nodes but also on relay nodes. In this paper, we consider a heterogeneous multi-hop network. The nodes in the network are heterogeneous, i.e., their computation capacities and transmission capacities are different. We propose a hierarchical coding scheme for such a network. Firstly, we reorganize it into a hierarchical network containing multiple layers. Each layer in the network consists of several groups. Then, a new hierarchical coding scheme is proposed, where coding is applied to each group to mitigate the stragglers. By taking both the computation time and transmission time into consideration, the overall task completion time is derived. To improve the performance of the network, heterogeneous hierarchical coded computation (HHCC) algorithm is proposed to provide an asymptotically optimal task allocation strategy. Compared with existing uniform uncoded, load balanced uncoded, and heterogeneous coded matrix multiplication schemes, HHCC has significant improvement.

Due to the proliferation of mobile devices, provisioning of massive connectivity has become a major challenge for future networks. The combination of millimeter wave (mmWave) with non-orthogonal multiple access (NOMA) provides a promising solution to massive connectivity. However, the security issue therein cannot be ignored due to the openness of wireless channels. To overcome the security challenge in mmWave-NOMA based networks, the nonorthogonal interference can be exploited to improve the security. In this paper, we propose a novel mmWave-NOMA framework where the users are classified as secure users (SUs) and common users (CUs), to satisfy their heterogeneous security service needs with the presence of randomly located eavesdroppers. According to their channel disparity, the NOMA users with stronger channel gains are deemed as SUs for better secrecy performance, while the remaining ones are served as CUs. To further enhance the security, hybrid precoding for SUs is designed to strengthen the desired signal and reduce interference. In addition, to reduce the complexity and satisfy the diverse demands, user grouping and power allocation are jointly optimized to maximize the sum rate of CUs subject to the SUs’ requirements. To solve the intractable non-convex problem, we decompose it into two subproblems, i.e., user grouping and power optimization, and a hybrid SU-CU grouping algorithm and a successive convex approximation based algorithm are proposed to solve them, respectively. Finally, simulation results are provided to show the advantages of the proposed scheme.

The integration of intelligent reflecting surface (IRS) and multiple access provides a promising solution to improved coverage and massive connections at low cost. However, securing IRS-aided networks remains a challenge since the potential eavesdropper also has access to an additional IRS reflection link, especially when the eavesdropping channel state information is unknown. In this paper, we propose an IRS-assisted non-orthogonal multiple access (NOMA) scheme to achieve secure communication via artificial jamming, where the multi-antenna base station sends the NOMA and jamming signals together to the legitimate users with the assistance of IRS, in the presence of a passive eavesdropper. The sum rate of legitimate users is maximized by optimizing the transmit beamforming, the jamming vector and the IRS reflecting vector, satisfying the quality of service requirement, the IRS reflecting constraint and the successive interference cancellation (SIC) decoding condition. In addition, the received jamming power is adapted at the highest level at all legitimate users for successful cancellation via SIC. To tackle this non-convex optimization problem, we first decompose it into two subproblems, and then each subproblem is converted into a convex one using successive convex approximation. An alternate optimization algorithm is proposed to solve them iteratively. Numerical results show that the secure transmission in the proposed IRS-NOMA scheme can be effectively guaranteed with the assistance of artificial jamming.

Considering the security issue in mmWave-NOMA based networks, the nonorthogonal interference can be exploited to improve the security. In this paper, we propose a novel mmWave-NOMA framework where the users are classified as secure users (SUs) and common users (CUs), to satisfy their heterogeneous security service needs with the presence of ran-domly located eavesdroppers. For better secrecy performance, the NOMA users with stronger channel gains are deemed as SUs, and the hybrid precoding for SUs is designed to strengthen the desired signal and reduce interference. In addition, to reduce the complexity and satisfy the diverse demands, user grouping and power allocation are jointly optimized to maximize the sum rate of CUs subject to the SUs' requirements. The non-convex problem is decomposed into two subproblems, i.e., user grouping and power optimization, and a hybrid SU-CU grouping algorithm and a successive convex approximation based algorithm are proposed to solve them, respectively. Finally, simulation results are provided to show the advantages of the proposed scheme.

Outside the coverage of terrestrial cellular networks, non-terrestrial infrastructures, e.g., satellites and unmanned aerial vehicles (UAVs), should be utilized, to efficiently cover the remote areas. This requires a cognitive satellite-UAV network, where satellites and UAVs share the spectrum to save cost, and the network resources are orchestrated in an on-demand manner. In this paper, we focus on the physical layer security issue of the cognitive satellite-UAV networks, which is important due to the openness of both satellite links and UAV links. We formulate a joint power and channel allocation problem, using only the slowly-varying large-scale channel state information (CSI), to maximize the sum secrecy rate of UAV users. By resorting to the random matrix theory, the max-min optimization tool, as well as the bipartite graph matching algorithm, we propose a sub-optimal low-complexity solution, the superiority of which is verified by simulation results.

Unmanned aerial vehicles (UAVs) have been used in millimeter-wave (mmWave) networks as relays to assist remote or blocked communication nodes. In this paper, we perform secrecy analysis for UAV-based mmWave relaying networks, where a cooperative jamming scheme is proposed via utilizing the destination and an external UAV to cooperatively disrupt the eavesdroppers at the two stages of relaying, respectively. Considering the probability of line-of-sight (LoS) between the UAV and ground nodes, the three-dimensional (3D) antenna gain, and the Nakagami-m small-scale fading model, closed-form SOP of the network is obtained by employing the Gauss-Chebyshev quadrature. Simulation results are presented to validate the theoretical expressions of SOP and to show the effectiveness of the proposed scheme.

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.

Covert communication can hide the information transmission process from the warden to prevent adversarial eavesdropping. However, it becomes challenging when the warden can move. In this paper, we propose a covert communication scheme against a mobile warden, which maximizes the connectivity throughput between a multi-antenna transmitter and a full-duplex jamming receiver with the covert outage probability (COP) limit. First, we analyze the monotonicity of the COP to obtain the optimal location the warden can move. Then, under this worst situation, we optimize the transmission rate, the transmit power and the jamming power of covert communication to maximize the connection throughput. This problem is solved in two stages. Under this worst situation, we first maximize the connection probability over the transmit-to-jamming power ratio within the maximum allowed COP for a fixed transmission rate. Then, the Newton's method is applied to maximize the connection throughput via optimizing the transmission rate iteratively. Simulation results are presented to evaluate the effectiveness of the proposed scheme.

Non-orthogonal multiple access (NOMA) is an important technology for the forthcoming 5G and beyond. However, its privacy often suffers from adversarial eavesdropping, especially for the users with higher transmit power. In this paper, we propose a jamming-aided secure transmission scheme for cooperative NOMA networks with a full-duplex (FD) relay. In this scheme, two pairs of users perform secure transmission with the help of a decode-forward (DF) relay, which forwards information and generates artificial jamming to counteract eavesdropping. The precoding vectors are designed to zero-force the artificial jamming at legal receivers. Then, the channel statistics are calculated, based on which the expressions of secrecy outage probability (SOP) are derived. Simulation results show the accuracy of our analysis, and demonstrate that the proposed scheme can effectively reduce the SOP and improve the effective secrecy throughput via artificial jamming and FD relaying.

In this paper, we propose a time-efficient data collection scheme, in which multiple ground devices upload their data to the unmanned aerial vehicle (UAV) via uplink nonorthogonal multiple access (NOMA). The total flight time of the UAV is equally divided into N time slots. The duration of each time slot is minimized by jointly optimizing the straight-line trajectory, device scheduling, and transmit power. To solve this mixed integer non-convex optimization problem, we decompose it into two steps. In the first step, we study the device scheduling strategy based on the UAV trajectory and the channel gains between the UAV and ground devices, through which the original problem can be greatly simplified. In the second step, the duration of each time slot is minimized by optimizing the transmit power and the UAV trajectory. An iterative algorithm based on alternating optimization is proposed, where each subproblem can be alternatively solved by applying successive convex approximation with the device scheduling updated at the end of each iteration. Numerical results are presented to evaluate the effectiveness of the proposed scheme.

Abstract In this article, a relaying network with simultaneous wireless information and power transfer (SWIPT) is considered, where the relaying nodes are energy‐constrained. The relaying nodes rely on energy harvesting (EH) technique for energy supply and they use different directive transmitters for information transmission and energy transfer, respectively, to enhance the SWIPT relaying performance. A novel discrete‐time‐switch and power‐split (DTS‐PS) based energy harvesting mixture transmission (EH‐MT) protocol for each EH decode and forwarding (DF) relaying node is proposed. The expressions for the average information rate and the energy transfer power are derived analytically and verified by simulations. An optimization function is formulated, and the optimal parameters are obtained analytically and verified by simulation.

Terrestrial communication networks mainly focus on users in urban areas but have poor coverage performance in harsh environments, such as mountains, deserts, and oceans. Satellites can be exploited to extend the coverage of terrestrial fifth-generation networks. However, satellites are restricted by their high latency and relatively low data rate. Consequently, the integration of terrestrial and satellite components has been widely studied to take advantage of both sides and enable the seamless broadband coverage. Due to the significant differences between satellite communications (SatComs) and terrestrial communications (TerComs) in terms of channel fading, transmission delay, mobility, and coverage performance, the establishment of an efficient hybrid satellite-terrestrial network (HSTN) still faces many challenges. In general, it is difficult to decompose an HSTN into a sum of separate satellite and terrestrial links due to the complicated coupling relationships therein. To uncover the complete picture of HSTNs, we regard the HSTN as a combination of basic cooperative models that contain the main traits of satellite-terrestrial integration but are much simpler and thus more tractable than the large-scale heterogeneous HSTNs. In particular, we present three basic cooperative models, i.e., model X, model L, and model V, and provide a survey of the state-of-the-art technologies for each of them. We discuss future research directions toward establishing a cell-free, hierarchical, decoupled HSTN. We also outline open issues to envision an agile, smart, and secure HSTN for the sixth-generation ubiquitous Internet of Things.

With the rapid development of marine activities, there has been an increasing number of Internet-of-Things (IoT) devices on the ocean. This leads to a growing demand for high-speed and ultrareliable maritime communications. It has been reported that a large performance loss is often inevitable if the existing fourth-generation (4G), fifth-generation (5G), or satellite communication technologies are used directly on the ocean. Hence, conventional theories and methods need to be tailored to this maritime scenario to match its unique characteristics, such as dynamic electromagnetic propagation environments, geometrically limited available base station (BS) sites and rigorous service demands from mission-critical applications. Toward this end, we provide a survey on the demand for maritime communications enabled by state-of-the-art hybrid satellite-terrestrial maritime communication networks (MCNs). We categorize the enabling technologies into three types based on their aims: 1) enhancing transmission efficiency; 2) extending network coverage; and 3) provisioning maritime-specific services. Future developments and open issues are also discussed. Based on this discussion, we envision the use of external auxiliary information, such as sea state and atmosphere conditions, to build up an environment-aware, service-driven, and integrated satellite-air-ground MCN.

In fifth generation (5G) and beyond Internet of Things (IoT), it becomes increasingly important to serve a massive number of IoT devices outside the coverage of terrestrial cellular networks. Due to their own limitations, unmanned aerial vehicles (UAVs) and satellites need to coordinate with each other in the coverage holes of 5G, leading to a cognitive satellite-UAV network (CSUN). In this paper, we investigate multi-domain resource allocation for CSUNs consisting of a satellite and a swarm of UAVs, so as to improve the efficiency of massive access in wide areas. Particularly, the cell-free on-demand coverage is established to overcome the cost-ineffectiveness of conventional cellular architecture. Opportunistic spectrum sharing is also implemented to cope with the spectrum scarcity problem. To this end, a process-oriented optimization framework is proposed for jointly allocating subchannels, transmit power and hovering times, which considers the whole flight process of UAVs and uses only the slowly-varying large-scale channel state information (CSI). Under the on-board energy constraints of UAVs and interference temperature constraints from UAV swarm to satellite users, we present iterative multi-domain resource allocation algorithms to improve network efficiency with guaranteed user fairness. Simulation results demonstrate the superiority of the proposed algorithms. Moreover, the adaptive cell-free coverage pattern is observed, which implies a promising way to efficiently serve wide-area IoT devices in the upcoming sixth generation (6G) era.

Hardware impairment is inevitable in many wireless systems. It is particularly severe in low-cost applications due to the imperfect components used. In this paper, the channel estimation and non-coherent detection problems of hardware impaired signals are studied for a single-carrier, single-antenna and single-hop system. Specifically, three different cases are investigated: signals with additive distortion only, signals with in-phase and quadrature imbalance only, and signals with both impairments. The maximum likelihood and Gaussian approximation methods are used to derive the new non-coherent detectors for amplitude modulated signals, while the maximum likelihood and moment-based methods are employed to design the new channel estimators for all signals. Numerical results show that the new non-coherent detectors outperform the existing non-coherent detectors in the presence of hardware impairment. The performance gain can be as high as 8 dB. They also show that the new channel estimators have much higher accuracy than the existing estimator. In some conditions, the accuracy of the new estimator is about 100 times that of the existing estimator.

Computation over multiple access channel (CoMAC) scheme provides a promising solution to future large-scale wireless networks by utilizing the superposition property of the wireless channel to compute a class of functions with a summation structure (e.g., mean, norm, etc.). However, its implementation usually requires all nodes' channel state information (CSI) and its performance is limited by the channel condition of the worst node. In order to avoid massive CSI aggregation and improve the limited performance, we propose an automatic repeat request (ARQ)-aided CoMAC scheme in this paper. The transmitters and signaling procedures are designed to achieve the tradeoff between the achievable function rate and the transmission delay. The corresponding performance of the proposed ARQ-aided CoMAC scheme and the traditional ARQ-aided communication scheme are compared for both homogeneous networks and heterogeneous networks. By optimizing the ARQ level, we further maximize the achievable function rate of the proposed scheme. Asymptotic closed-form expressions are derived by resorting to the extreme value theory and point mass approximation. Monte Carlo simulations are given to illustrate and verify the performance of the proposed designs.

Energy-efficient designs play an important role in the Internet of Things (IoT) that monitors multiple phenomena, due to the limited power supply and complicated observation. In this paper, taking into account the power consumptions of observation, coding, and communication, we propose a joint sparse observation and coding scheme for energy-efficient monitoring of multiple phenomena using IoT. Through the analysis of outage performance, we find that the sparse observation and coding scheme can achieve the performance of the full observation scheme in which all nodes observe all phenomena with lower power consumption due to the dynamic and selective observation and coding. With the derived achievable rates and network power consumption, we study the trade-off between achievable rates and network power consumption that is determined by both the observation matrix and the coding matrix. For given rate constraints, we propose an optimization problem to minimize the network power consumption by jointly designing the observation and coding matrices. To solve this NP-hard problem efficiently, we propose a low-complexity algorithm with the convex-concave procedure. Moreover, to improve performance in high noise environment, we adopt collaboration among nodes to suppress observation noises and equalize bad observations by utilizing observation diversity. Finally, simulation results illustrate the superior performance of the proposed schemes.

Unmanned aerial vehicles (UAVs) communications have been widely exploited in our daily life, which leads to rising concerns about the security issue. This work investigates the secrecy performance of a UAV-to-vehicle (UAV-2-V) communication system, where the information delivered over both downlink and uplink between a UAV ($S$) acting as a temporary aerial base-station and a legitimate vehicle ($D$) moving along a road which is overheard by an eavesdropping vehicle ($E$) on the same road. The location of$S$is assumed to be uniformly distributed in the sky, while the locations of$D$and$E$are uniformly distributed on the highway. The statistical characteristics, including the cumulative distribution function and probability density function of the received signal-to-noise ratio over both downlink and uplink, are characterized respectively. Closed-form expressions for the approximate and asymptotic secrecy outage probability (SOP) of the downlink experiencing Rician fading channels have been derived accordingly. Moreover, the secrecy outage performance of the uplink is investigated by deriving the closed-form expression of the exact and asymptotic SOP in two cases: the eavesdropping channel suffers Rician and Weibull fading, respectively. Finally, Monte-Carlo simulations are shown to verify our proposed analytical models.

Due to the mobility and line-of-sight conditions, unmanned aerial vehicle (UAV) is deemed as a promising solution to sensor data collection. On the other hand, it is vital to guarantee the timeliness of information for UAV-assisted data collection. In this paper, we propose a time-efficient data collection scheme, in which multiple ground devices upload their data to the UAV via uplink non-orthogonal multiple access (NOMA). The total flight time of the UAV is equally divided into$N$time slots. The duration of each time slot is minimized by jointly optimizing the straight-line trajectory, device scheduling, and transmit power. To solve this mixed integer non-convex optimization problem, we decompose it into two steps. In the first step, we study the device scheduling strategy based on the UAV trajectory and the channel gains between the UAV and ground devices, through which the original problem can be greatly simplified. In the second step, the duration of each time slot is minimized by optimizing the transmit power and the UAV trajectory. An iterative algorithm based on alternating optimization is proposed, where each subproblem can be alternatively solved by applying successive convex approximation with the device scheduling updated at the end of each iteration. Numerical results are presented to evaluate the effectiveness of the proposed scheme.

For future wireless networks, enormous numbers of interconnections are required, creating a multi-hop topology and leading to a great challenge on data aggregation. Instead of collecting data individually, a more efficient technique, computation over multi-access channels (CoMAC), has emerged to compute functions by exploiting the signal-superposition property of wireless channels. However, it is still an open problem on the implementation of CoMAC in multi-hop wireless networks considering fading channel and resource allocation. In this paper, we propose multi-layer CoMAC (ML-CoMAC) by combining CoMAC and orthogonal communication to compute functions in the multi-hop network. Firstly, to make the multi-hop network more tractable, we reorganize it into a hierarchical network with multiple layers that consists of subgroups and groups. Then, in the hierarchical network, the implementation of ML-CoMAC is given by computing and communicating subgroup and group functions over layers, where CoMAC is applied to compute each subgroup function and orthogonal communication is adopted for each group to obtain the group function. The general computation rate is derived and the performance is further improved through time allocation and power control. The closed-form solutions to optimization problems are obtained, which suggests that orthogonal communication and existing CoMAC schemes are generalized.

Although the use of coordinated direct and relay transmission (CDRT) in non-orthogonal multiple access (NOMA) can extend the coverage, its duplicated transmission reduces the spectrum efficiency (SE) of NOMA. To improve the SE, we propose a spectrum-efficient scheme for NOMA-based CDRT over Nakagami-m fading channels. In this scheme, the base station (BS) connects with a cell-center user (CCU) directly while communicating with a cell-edge user (CEU) via a relay and the CCU. Then, the relay and the CCU use network coding to process and retransmit the signals sent by the BS first and the CEU later. Finally, the BS and the relay simultaneously broadcast downlink signals. We derive the closed-form expressions for the average SE, the user fairness index and the energy efficiency (EE) as well as the asymptotic average SE using both perfect and imperfect successive interference cancellation (SIC). Simulations verify the correctness of our theoretical analysis and the superiority of the proposed scheme in SE and EE.

Satellite industrial Internet of Things (IIoT) plays an important role in industrial manufactures without requiring the support of terrestrial infrastructures. However, due to the scarcity of spectrum resources, existing satellite frequency bands cannot satisfy the demand of IIoT, which have to explore other available spectrum resources. Cognitive satellite networks are promising technologies and have the potential to alleviate the shortage of spectrum resources and enhance spectrum efficiency by sharing both spectral and spatial degrees of freedom. For effective signal estimations, multiple features of wireless signals are needed at receivers, the transmissions of which may cause considerable overhead. To mitigate the overhead, part of parameters, such as modulation order, constellation type, and signal to noise ratio (SNR), could be obtained at receivers through signal estimation rather than transmissions from transmitters to receivers. In this article, a grid method is utilized to process the constellation map to obtain its equivalent probability density function. Then, binary feature matrix of the probability density function is employed to construct a cost function to estimate the modulation order and constellation type for multiple quadrature amplitude modulation (MQAM) signal. Finally, an improved M2M∞method is adopted to realize the SNR estimation of MQAM. Simulation results show that the proposed method is able to accurately estimate the modulation order, constellation type, and SNR of MQAM signal, and these features are extremely useful in satellite-based IIoT.

In industrial sensor networks, complex industrial environments may be encountered leading to a mix of signals of different types. Complicated interference caused by mixed signals on industrial equipments may significantly degrade the classification rate of signals, which may result in a long training time in order to extract features. In addition, with limited channel resources, it is difficult to make the global optimal decision in industrial distributed wireless sensor networks. To address this problem, a signal classification method using feature fusion is proposed for industrial Internet of Things in this article. In the proposed method, the received signals of nodes are processed by frequency reduction and sampling pretreatment, based on which intelligent representations of signals are obtained. Using federated learning, the data samples are trained with the feature fusion network. Moreover, the trained deep learning network is used on each sensor node to classify signals, the results of which will be transmitted to aggregation center. In the aggregation center, the improved evidence theory method is used to aggregate the recognition results of each sensor node to achieve the final classification. Simulation shows that the proposed method has excellent classification performances. Notably, it is not required for the proposed method to transmit signals from nodes to the aggregation center, which could effectively protect the privacy of industrial information.

Covert communication can hide the information transmission process from the warden to prevent adversarial eavesdropping. However, it becomes challenging when the location of warden is uncertain. In this paper, we propose a covert communication scheme against a warden with uncertain locations, which maximizes the connectivity throughput between a multi-antenna transmitter and a full-duplex jamming receiver with the limit of covert outage probability (the probability of the transmission found by the warden). First, we analyze the monotonicity of the covert outage probability to obtain the optimal location for the warden. Then, under this worst situation, we optimize the transmission rate, the transmit power and the jamming power of covert communication to maximize the connection throughput. This problem is solved in two stages. First, we derive the transmit-to-jamming power ratio limit from the maximum allowed covert outage probability. With this constraint, the connection probability is maximized over the transmit-to-jamming power ratio for a fixed transmission rate. Since the connection probability and the transmission rate are coupled, the bisection method is applied to maximize the connectivity throughput via optimizing the transmission rate iteratively. Simulation results are presented to evaluate the effectiveness of the proposed scheme.

Time-division-duplexing massive multiple-input multiple-output (MIMO) systems estimate the channel state information (CSI) by leveraging the uplink-downlink channel reciprocity, which is no longer valid when the mismatch arises from the asymmetric uplink and downlink radio frequency (RF) chains. Existing works treat the reciprocity mismatch as constant for simplicity. However, the practical RF chain consists of nonlinear components, which leads to nonlinear reciprocity mismatch. In this work, we examine the impact and the calibration approach of the nonlinear reciprocity mismatch in massive MIMO systems. To evaluate the impact of the nonlinear mismatch, we first derive the closed-form expression of the ergodic achievable rate. Then, we analyze the performance loss caused by the nonlinear mismatch to show that the impact of the mismatch at the base station (BS) side is much larger than that at the user equipment side. Therefore, we propose a calibration method for the BS. During the calibration, polynomial function is applied to approximate the nonlinear mismatch factor, and over-the-air training is employed to estimate the polynomial coefficients. After that, the calibration coefficients are computed by maximizing the downlink achievable rate. Simulation results are presented to verify the analytical results and to show the performance of the proposed calibration approach.

Employing unmanned aerial vehicles (UAVs) in millimeter-wave (mmWave) networks as relays has emerged as an appealing solution to assist remote or blocked communication nodes. In this case, the network security becomes a great challenge due to the presence of malicious eavesdroppers. In this paper, we perform a secrecy analysis for a UAV-based mmWave relaying network. We first investigate the relaying scheme without jamming where the UAV decodes and forwards the information from the source to the destination with malicious eavesdropping. Furthermore, to enhance the secrecy performance, we propose a cooperative jamming scheme via utilizing the destination and an external UAV to cooperatively disrupt the eavesdroppers at the two stages of relaying, respectively. Using the probability of line-of-sight (LoS) between the UAV and ground nodes, the three-dimensional (3D) antenna gain, and the Nakagami-m small-scale fading model, the secrecy outage probability (SOP) of the two schemes with and without jamming is analyzed. Closed-form expressions for the SOP of the two schemes are obtained by employing the Gauss-Chebyshev quadrature. Simulation results are presented to validate the theoretical expressions of SOP and to show the effectiveness of the proposed schemes.

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.

Non-orthogonal multiple access (NOMA) aided unmanned aerial vehicle (UAV) is becoming a promising technique for future wireless networks. However, its security remains a great challenge due to the line-of-sight in UAV communications and high transmit power for weak users in NOMA. Thus, in this paper, we propose a power allocation (PA) scheme for NOMA-UAV networks with circular trajectory, to maximize the sum rate of common users while guaranteeing the security for a specific user. To achieve this, we consider three cases based on the distance from the UAV to the secure user. Specifically, the lowest transmit power is assigned to the secure user in each time slot to guarantee its security, with the remaining power allocated to common users to maximize their sum rate. To further improve the transmission rate of the secure user, we also derive the upper bound for its decoding threshold, and analyze the linear relationship between the secure decoding threshold and the sum rate of common users. Simulation results are demonstrated to evaluate the effectiveness of the proposed secure PA scheme in NOMA-UAV networks.

In this study, the performance of downlink simultaneous wireless information and power transfer (SWIPT) networks over Nakagami‐ m fading is analysed. The SWIPT network is modelled as a two‐tier heterogeneous network, where one tier is the information transmission network and the other is the power transmission network. The seamless integration enables both data and energy to be transferred from access points to the users. Using the stochastic geometry theory, the expressions for outage probability at the information receiver are derived in decoupled and integrated SWIPT networks. Also, the average harvested energy at the power receiver is derived assuming a non‐linear energy harvesting model. Simulation results validate the analytical expressions and the impacts of various system parameters on the SWITP performance are investigated.

Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs.

Joint radar-communication designs are exploited in applications where radar and communications systems share the same frequency band or when both radar sensing and information communication functions are required in the same system. Finding a waveform that is suitable for both radar and communication is challenging due to the difference between radar and communication operations. In this paper, we propose a new method of designing dual-functional waveforms for both radar and communication using signals from multiplexed communications users. Specifically, signals from different communications users multiplexed in the time, code or frequency domains across different data bits are linearly combined to generate an overall radar waveform. Three typical radar waveforms are considered. The coefficients of the linear combination are optimized to minimize the mean squared error with or without a constraint on the signal-to-noise ratio (SNR) for the communications signals. Numerical results show that the optimization without SNR constraint can almost perfectly approximate the radar waveform in all the cases considered, giving good dual-functional waveforms for both radar and communication. Also, among different multiplexing techniques, time division multiple access is the best option to approximate the radar waveform, followed by code division multiple access and orthogonal frequency division multiple access.

The next generation wireless network is expected to connect billions of nodes, which brings up the bottleneck on the communication speed for distributed data fusion. To overcome this challenge, computation over multiple access channel (CoMAC) was recently developed to compute the desired functions with a summation structure (e.g., mean, norm, etc.) by using the superposition property of wireless channels. This work aims to maximize the achievable function rate of reliable CoMAC in wireless networks. More specifically, considering channel fading and transceiver design, we derive the achievable function rate adopting the quantization and the nested lattice coding, which is determined by the number of nodes, the maximum value of messages and the quantization error threshold. Based on the derived result, the transceiver design is optimized to maximize the achievable function rate of the network. We first study a single cluster network without inter-cluster interference (ICI). Then, a multi-cluster network is further analyzed in which the clusters work in the same channel with ICI. In order to avoid the global channel state information (CSI) aggregation during the optimization, a low-complexity signaling procedure irrelevant with the number of nodes is proposed utilizing the channel reciprocity and the defined effective CSI.

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications. In practice, UAV-aided aerial base stations are not separate. Instead, they rely on existing satellites/terrestrial systems for spectrum sharing and efficient backhaul. In this case, how to coordinate satellites, UAVs and terrestrial systems is still an open issue. In this paper, we deploy UAVs for coverage enhancement of a hybrid satellite-terrestrial maritime communication network. Using a typical composite channel model including both large-scale and small-scale fading, the UAV trajectory and in-flight transmit power are jointly optimized, subject to constraints on UAV kinematics, tolerable interference, backhaul, and the total energy of the UAV for communications. Different from existing studies, only the location-dependent large-scale channel state information (CSI) is assumed available, because it is difficult to obtain the small-scale CSI before takeoff in practice and the ship positions can be obtained via the dedicated maritime Automatic Identification System. The optimization problem is non-convex. We solve it by using problem decomposition, successive convex optimization and bisection searching tools. Simulation results demonstrate that the UAV fits well with existing satellite and terrestrial systems, using the proposed optimization framework.

Cooperative beamforming can provide significant performance improvement for relaying systems with the help of the channel state information (CSI). In time-division duplexing (TDD) mode, the estimated CSI will deteriorate due to the reciprocity mismatch. In this work, we examine the impact and the calibration of the reciprocity mismatch in relaying systems. To evaluate the impact of the reciprocity mismatch for all devices, the closed-form expression of the achievable rate is first derived. Then, we analyze the performance loss caused by the reciprocity mismatch at sources, relays, and destinations respectively to show that the mismatch at relays dominates the impact. To compensate the performance loss, a two-stage calibration scheme is proposed for relays. Specifically, relays perform the intra-calibration based on circuits independently. Further, the inter-calibration based on the discrete Fourier transform (DFT) codebook is operated to improve the calibration performance by cooperation transmission, which has never been considered in previous work. Finally, we derive the achievable rate after relays perform the proposed reciprocity calibration scheme and investigate the impact of estimation errors on the system performance. Simulation results are presented to verify the analytical results and to show the performance of the proposed calibration approach.

Unmanned aerial vehicles (UAVs) are playing an important role in wireless networks, due to their cost effectiveness and flexible deployment. Particularly, integrating UAVs into existing cellular networks has great potential to provide high-rate and ultra-reliable communications. In this paper, we investigate the uplink transmission in a cellular network from a UAV using non-orthogonal multiple access (NOMA) and from ground users to base stations (BSs). Specifically, we aim to maximize the sum rate of uplink from UAV to BSs in a specific band as well as from the UAV's co-channel users to their associated BSs via optimizing the precoding vectors at the multi-antenna UAV. To mitigate the interference, we apply successive interference cancellation (SIC) not only to the UAV-connected BSs, but also to the BSs associated with ground users in the same band. The precoding optimization problem with constraints on the SIC decoding and the transmission rate requirements is formulated, which is non-convex. Thus, we introduce auxiliary variables and apply approximations based on the first-order Taylor expansion to convert it into a second-order cone programming. Accordingly, an iterative algorithm is designed to obtain the solution to the problem with low complexity. Numerical results are presented to demonstrate the effectiveness of our proposed scheme.

Combination of unmanned aerial vehicle (UAV) and non-orthogonal multiple access (NOMA) is deemed as an promising solution to achieving massive connectivity in future wireless networks. In this paper, a UAV-aided NOMA scheme is proposed to achieve simultaneous wireless information and power transfer (SWIPT) and guarantee the secure transmission for ground passive receivers (PRs), in which the nonlinear energy harvesting model is applied. Each time frame is divided into two phases. In the first phase, the received power at each PR is maximized to achieve rapid charging. In the second phase, SWIPT is performed via NOMA with the remaining energy at each PR, and artificial jamming is generated at UAV together with the NOMA information to guarantee the security. The throughput of PRs is maximized, with the highest received jamming power cancelled at each PR via successive interference cancellation (SIC). This disrupts the eavesdropping effectively by jamming without affecting the legitimate transmission. Due to the non-convexity of these two optimization problems, we first convert them to convex ones and then propose iterative algorithms to solve them. Simulation results are presented to show the effectiveness of the proposed scheme.

Blind estimation of parameters for M-ary frequency-shift-keying (M-FSK) signals is great of importance in intelligent receivers. Many existing algorithms have assumed white Gaussian noise. However, their performance severely degrades when grossly corrupted data, i.e., outliers, exist. This article solves this issue by developing a novel approach for parameter estimation of M-FSK signals in the presence of alpha-stable noise. Specifically, the proposed method exploits the generalized first- and second-order cyclostationarity of M-FSK signals with alpha-stable noise, which results in closed-form solutions for unknown parameters in both time and frequency domains. As a merit, it is computationally efficient and thus can be used for signal preprocessing, symbol timing estimation, signal and noise power estimation. Furthermore, substantial theoretical analysis on the performance of the proposed approach is provided. Simulations demonstrate that the proposed method is robust to alpha-stable noise and that it outperforms the state-of-the-art algorithms in many challenging scenarios.

INTERNET of Thing (IoT) technology has attracted intensive interest in the automotive industry to meet the new demands in the market while continuing to achieve their conservative goals [item 1) in the Appendix]. As for Industrial Internet of Things (IIoT), randomly moving wireless nodes are often carried by humans and communicate with each other when they are in close proximity. The interaction between nodes shows strong regularity or sociality, i.e., a wireless node always communicates with several social-closed or distance-closed nodes. This special section collects the latest ideas and research on the social and cognitive mobile computing in IIoT. Particularly, 15 original articles are accepted and included in the collection on the following pages. The topics of these articles are mainly concerned with social and cognitive mobility modeling, routing protocol, resource allocation, and so forth. We believe that these articles will play a role in inspiring our readers. Summaries of accepted articles are provided.

Federated learning (FL) provides a communication-efficient approach to solve machine learning problems concerning distributed data, without sending raw data to a central server. However, existing works on FL only utilize first-order gradient descent (GD) and do not consider the preceding iterations to gradient update which can potentially accelerate convergence. In this article, we consider momentum term which relates to the last iteration. The proposed momentum federated learning (MFL) uses momentum gradient descent (MGD) in the local update step of FL system. We establish global convergence properties of MFL and derive an upper bound on MFL convergence rate. Comparing the upper bounds on MFL and FL convergence rates, we provide conditions in which MFL accelerates the convergence. For different machine learning models, the convergence performance of MFL is evaluated based on experiments with MNIST and CIFAR-10 datasets. Simulation results confirm that MFL is globally convergent and further reveal significant convergence improvement over FL.

Massive numbers of nodes will be connected in future wireless networks. This brings great difficulty to collect a large amount of data. Instead of collecting the data individually, computation over multi-access channels (CoMAC) provides an intelligent solution by computing a desired function over the air based on the signal-superposition property of wireless channels. To improve the spectrum efficiency in conventional CoMAC, we propose the use of non-orthogonal multiple access (NOMA) for functions in CoMAC. The desired functions are decomposed into several sub-functions, and multiple sub-functions are selected to be superposed over each resource block (RB). The corresponding achievable rate is derived based on sub-function superposition, which prevents a vanishing computation rate for large numbers of nodes. We further study the limiting case when the number of nodes goes to infinity. An exact expression of the rate is derived that provides a lower bound on the computation rate. Compared with existing CoMAC, the NOMA-based CoMAC not only achieves a higher computation rate but also provides an improved non-vanishing rate. Furthermore, the diversity order of the computation rate is derived, which shows that the system performance is dominated by the node with the worst channel gain among these sub-functions in each RB.

Unmanned aerial vehicles (UAVs) have the capability of supplementing as well as improvising terrestrial cellular communications as aerial base stations to improve network coverage. This puts significance on the effective deployment of multiple UAVs while maximizing the coverage area in presence of co- channel interference generated by these UAVs. To this end, it is important to determine the parameters that affects the coverage area performance. In this paper, we investigate the effect of co-channel interference on the effective coverage of UAV-based small cells (USCs) deployed in a certain geographical area to satisfy a target signal-to-interference-plus-noise ratio (SINR) at the cell edge. We propose a coordinated multi- UAV strategy to evaluate the trade-off between the UAV separation distance and the overall coverage area assuming symmetric placement of UAVs at a common optimal altitude to ensure minimum transmit power. Numerical results unveil that the number of UAVs and the separation distance between them should be carefully designed to achieve the optimal coverage area performance.

Unmanned aerial vehicle (UAV) swarm-enabled aerial network has emerged as an effective solution to ondemand communications, especially in unexpected scenarios. Due to the broadcast nature of the air-to-ground link, UAV swarm-enabled wireless communications are inherently prone to eavesdropping. The paper investigates power allocation for UAV swarm-enabled secure networks. To depict air-to-ground link, a composite channel consisting of small-scale and large-scale fading is taken into account. Because of the difficulty in acquiring the time-varying small-scale fading, we use the large-scale channel state information (CSI). An optimization framework in a whole- trajectory-oriented manner is proposed to maximize secrecy throughput with the constraints on the transmission power and the transmission durations as well as the overall transmission energy per UAV over a given flight period. The formulated problem is not convex. To deal with that, we first derive a closed form of secrecy throughput in the form of high-order fixed-point equations. Then, we propose an iterative algorithm with successive convex approximation technique by alternately optimizing the variables. Numerical results validate the effectiveness of the proposed scheme and show that our proposed scheme can achieve a good secrecy performance.

In this paper, we propose a novel scheme to guarantee the security of UAV-relayed networks with caching via jointly optimizing the UAV trajectory and time scheduling. For the two users that have cached the required file for the other, the UAV broadcasts the files together to these two users and the eavesdropping can be disrupted. For the user without caching, we maximize its secrecy rate by jointly optimizing the trajectory and scheduling, with the secrecy rate of the caching users satisfied. The corresponding optimization problem is difficult to solve due to its non-convexity, and we propose an iterative algorithm via successive convex optimization to solve it approximatively. Simulation results are provided to show the effectiveness and efficiency of our proposed scheme.

Non-orthogonal multiple access (NOMA) is a prospective technology for radio resource constrained future mobile networks. However, NOMA users far from base station (BS) tend to be more susceptible to eavesdropping because they are allocated more transmit power. In this paper, we aim to jointly optimize the precoding vectors at BS to ensure the legitimate security in a downlink multiple-input single-output (MISO) NOMA network. In the proposed scheme, we can maximize the sum secrecy rate by joint precoding optimization. Owing to its non-convexity, the problem is converted into a convex one, which is solved by a second-order cone programming based iterative algorithm. Simulation results are presented to demonstrate that the proposed schemes can improve the security performance for MISO NOMA systems effectively.

In this paper, we investigate the uplink transmission in a cellular network from a UAV and ground users to ground base stations (BSs). Specifically, we aim to maximize the sum rate of the uplink from the UAV to ground BSs in a specific idle frequency band as well as from the co-channel users to their associated BSs by optimizing precoding vectors at UAV. To mitigate the interference, we apply successive interference cancellation (SIC) not only to the BSs connected with UAV using non-orthogonal multiple access (NOMA) for transmission, but also to other BSs communicating with ground users in the same band. The precoding optimization problem with constraints on the SIC decoding and the uplink transmission rate is formulated, which is non-convex and intractable. Thus, we introduce auxiliary variables and apply first-order approximations based on Taylor expansion to convert it into a second-order cone programming. An iterative algorithm is proposed with low complexity to calculate the solution to the non-convex precoding optimization problem. Numerical results demonstrate the effectiveness of our proposed scheme.

In this paper, user selection and transceiver design are proposed to guarantee the secure transmission in a multiple-input multiple-output interference network with an eavesdropper. First, user selection is performed to select the most suitable user to transmit confidential information according to the topology and path loss in each time slot. Then, based on user selection, the transceivers are jointly designed to maximize the secrecy rate of the selected user while guaranteeing a minimum transmission rate for other users. Due to the non-convexity of the problem, an alternate iteration algorithm is proposed to obtain the optimal solution with the help of successive approximations. Finally, simulation results are presented to show the effectiveness and efficiency of the proposed schemes.

Non-orthogonal multiple access (NOMA) has been developed as a key multi-access technique for 5G. However, secure transmission remains a challenge in NOMA. Especially, the user with weakest channel is most threatened by eavesdropping, due to its highest transmit power. In this paper, we propose a novel scheme to generate artificial jamming at the NOMA base station (BS), aiming at disrupting the potential eavesdropping without affecting the legitimate transmission. In the scheme, the transmit power of artificial jamming is maximized, with its received power at each receiver higher than that of other users. Thus, the jamming signal can be eliminated via successive interference cancellation before others, and the eavesdropping can be disrupted effectively. Due to the non-convexity of the optimization problems, we first convert it to a convex one and then provide an iterative algorithm to solve it. Simulation results are presented to show the effectiveness of the proposed scheme in guaranteeing the security of NOMA networks.

Idle nodes are used in cooperative communications to achieve diversity gain or coverage extension. Energy harvesting (EH) allows cooperative communications to be less dependent on batteries. In this study, the performance of EH decode‐and‐forward relaying is analysed in terms of achievable rate for non‐ideal relaying nodes whose circuit power cannot be neglected. Two time switching protocols, continuous time switching and discrete time switching, are studied. The analytical expressions for the achievable rate are derived and optimised. Numerical results show the validity of the analytical expressions and determine the influence of the circuit power consumption.

This study focuses on spectrum sensing under Laplacian noise. To mitigate the negative effects caused by the heavy‐tailed behaviour of Laplacian noise, the fractional lower order moments (FLOM) technology is employed to pre‐process the received samples before spectrum sensing. Through exploiting the asymmetrical difference between the distribution for the FLOM of received samples in the absence and presence of primary users, the authors formulate the spectrum sensing problem under Laplacian noise as a unilateral goodness‐of‐fit (GoF) test problem. Based on this test problem, they propose a new GoF‐based detector, which is called a unilateral left‐tail Anderson Darling (ULAD) detector. The analytical expressions for the theoretical performance, in terms of false‐alarm and detection probabilities, of the ULAD are derived. Moreover, a closed‐form expression for the optimal detection threshold is also derived to minimise the total error rate. Simulation results are provided to validate the theoretical analyses and to demonstrate the superior performance of the proposed detector than others.

Energy harvesting provides a promising alternative to batteries and mains used by current wireless devices. However, the power supply in energy harvesting communications becomes dynamic due to the random channels. In this work, the authors study two energy harvesting communications protocols, ‘harvest‐store‐use’ and ‘harvest‐use’. A linear harvester is used to collect energy over multiple time slots for the two protocols. The effective throughput for ‘harvest‐store‐use’ and the average bit error rate for ‘harvest‐use’ are derived. Numerical results show that there exists an optimum number of time slots for the ‘harvest‐store‐use’ protocol to balance the transmission probability and the delay and that the effective throughput approaches an upper limit when the number of time slots increases. Also, for the ‘harvest‐use’ protocol, one should use a small number of time slots to balance the bit error rate performance and the battery consumption.

The unmanned aerial vehicle (UAV) is a promising enabler of the Internet of Things (IoT) vision, due to its agile maneuverability. In this paper, we explore the potential gain of UAV-aided data collection in a generalized IoT scenario. Particularly, a composite channel model, including both large-scale and small-scale fading is used to depict typical propagation environments. Moreover, rigorous energy constraints are considered to characterize IoT devices as practically as possible. A multiantenna UAV is employed, which can communicate with a cluster of single-antenna IoT devices to form a virtual MIMO link. We formulate a whole-trajectory-oriented optimization problem, where the transmission duration and the transmit power of all devices are jointly designed to maximize the data collection efficiency for the whole flight. Different from previous studies, only the slowly varying large-scale channel state information is assumed available, to coincide with the fact that practically it is quite difficult to predictively acquire the random small-scale channel fading prior to the UAV flight. We propose an iterative scheme to overcome the nonconvexity of the formulated problem. The presented scheme can provide a significant performance gain over traditional schemes and converges quickly.

When natural disasters strike, the coverage for Internet of Things (IoT) may be severely destroyed, due to the damaged communications infrastructure. Unmanned aerial vehicles (UAVs) can be exploited as flying base stations to provide emergency coverage for IoT, due to its mobility and flexibility. In this paper, we propose multiantenna transceiver design and multihop device-to-device (D2D) communication to guarantee the reliable transmission and extend the UAV coverage for IoT in disasters. First, multihop D2D links are established to extend the coverage of UAV emergency networks due to the constrained transmit power of the UAV. In particular, a shortest-path-routing algorithm is proposed to establish the D2D links rapidly with minimum nodes. The closed-form solutions for the number of hops and the outage probability are derived for the uplink and downlink. Second, the transceiver designs for the UAV uplink and downlink are studied to optimize the performance of UAV transmission. Due to the nonconvexity of the problem, they are first transformed into convex ones and then, low-complexity algorithms are proposed to solve them efficiently. Simulation results show the performance improvement in the throughput and outage probability by the proposed schemes for UAV wireless coverage of IoT in disasters.

Mobile edge computing (MEC) plays an important role in next-generation networks. It aims to enhance processing capacity and offer low-latency computing services for Internet of Things (IoT). In this paper, we investigate a resource allocation policy to maximize the available processing capacity (APC) for MEC IoT networks with constrained power and unpredictable tasks. First, the APC which describes the computing ability and speed of a served IoT device is defined. Then its expression is derived by analyzing the relationship between task partitioning and resource allocation. Based on this expression, the power allocation solution for the single-user MEC system with a single subcarrier is studied and the factors that affect the APC improvement are considered. For the multiuser MEC system, an optimization problem of APC with a general utility function is formulated and several fundamental criteria for resource allocation are derived. By leveraging these criteria, a binary-search water-filling algorithm is proposed to solve the power allocation between local CPU and multiple subcarriers, and a suboptimal algorithm is proposed to assign the subcarriers among users. Finally, the validity of the proposed algorithms is verified by Monte Carlo simulation.

Generalized likelihood ratio test is an effective method for target detection in passive radar systems. The distribution of its decision variable in the presence of a direct path is unknown but is required for the calculation of the detection threshold and the detection probability. In this letter, several new approximations to this distribution are proposed by using moment matching. Numerical results show that the generalized extreme value approximation works consistently well for both null and alternative hypotheses with large or small signal-to-noise ratios. On the other hand, the Gaussian and logistic approximations only work well in the null hypothesis.

In a downlink non-orthogonal multiple access (NOMA) system, the reliable transmission of cell-edge users cannot be guaranteed due to severe channel fading. On the other hand, the presence of eavesdroppers can severely threaten the secure transmission due to the open nature of wireless channel. Thus, a two-user NOMA system assisted by a multi-antenna decode-and-forward relay is considered in this paper, and a two-stage jamming scheme, full-duplex-jamming (FDJam), is proposed to ensure the secure transmission of NOMA users. In the FDJam scheme, using full-duplex, the relay transmits the jamming signal to the eavesdropper while receiving confidential messages in the first stage, and the base station generates the jamming signal in the second stage. Furthermore, we eliminate the self-interference and the jamming signal at the relay and the legitimate node, respectively, through relay beamforming. To measure the secrecy performance, analytical expressions for secrecy outage probability (SOP) are derived for both the cell-center and cell-edge users, and the asymptotic SOP analysis at high transmit power is presented as well. Moreover, two benchmark schemes, half-duplex-jamming and full-duplex-no-jamming, are also considered. Simulation results are presented to show the accuracy of the analytical expressions and the effectiveness of the proposed scheme.

Distributing data aggregation through multiple access channel (MAC) has been challenging in large wireless networks. In order to tackle the challenge, a computing over the MAC (CP-MAC) scheme has been proposed as a promising communication-computation integrated way for function-centric networks. In this paper, we analyze the performance of the CP-MAC scheme, compared with the traditional communication-computation separated way, i.e., a communicating over the MAC (CM-MAC) scheme. Function-centric wireless networks are considered, where the fusion center (FC) does not need the individual data of each node but only the target function. We begin with the ideal uniform-MAC scenarios, where the CP-MAC scheme is always better than the CM-MAC scheme. Then, practical non-uniform MAC scenarios are studied for both homogeneous networks with Rayleigh fading and heterogeneous networks with a different path loss. Closed-form expressions of the achievable function rate are provided using the asymptotic theory of ordered statistics. It is found that the CP-MAC scheme is not always superior to the CM-MAC scheme. Simulation results are provided to verify and illustrate our derived results.

Unmanned aerial vehicle (UAV) can be utilized as a relay to connect nodes with long distance, which can achieve significant throughput gain owing to its mobility and line-of-sight (LoS) channel with ground nodes. However, such LoS channels make UAV transmission easy to eavesdrop. In this paper, we propose a novel scheme to guarantee the security of UAV-relayed wireless networks with caching via jointly optimizing the UAV trajectory and time scheduling. For every two users that have cached the required file for the other, the UAV broadcasts the files together to these two users, and the eavesdropping can be disrupted. For the users without caching, we maximize their minimum average secrecy rate by jointly optimizing the trajectory and scheduling, with the secrecy rate of the caching users satisfied. The corresponding optimization problem is difficult to solve due to its non-convexity, and we propose an iterative algorithm via successive convex optimization to solve it approximately. Furthermore, we also consider a benchmark scheme in which we maximize the minimum average secrecy rate among all users by jointly optimizing the UAV trajectory and time scheduling when no user has the caching ability. Simulation results are provided to show the effectiveness and efficiency of our proposed scheme.

The explosive data traffic and connections in 5G networks require the use of non-orthogonal multiple access (NOMA) to accommodate more users. Unmanned aerial vehicle (UAV) can be exploited with NOMA to improve the situation further. In this paper, we propose a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously. The sum rate is maximized by jointly optimizing the UAV trajectory and the NOMA precoding. To solve the optimization, we decompose it into two steps. First, the sum rate of the UAV-served users is maximized via alternate user scheduling and UAV trajectory with its interference to the BS-served users below a threshold. Then, the optimal NOMA precoding vectors are obtained using two schemes with different constraints. The first scheme intends to cancel the interference from the BS to the UAV-served user, while the second one restricts the interference to a given threshold. In both schemes, the non-convex optimization problems are converted into tractable ones. An iterative algorithm is designed. Numerical results are provided to evaluate the effectiveness of the proposed algorithms for the hybrid NOMA and UAV network.

Non-orthogonal multiple access (NOMA) has been developed as a key multi-access technique for 5G. However, secure transmission remains a challenge in NOMA. Especially, the user with weakest channel is most threatened by eavesdropping, due to its highest transmit power. Two schemes are proposed to generate artificial jamming at the NOMA base station (BS), aiming at disrupting the potential eavesdropping without affecting the legitimate transmission. In the first scheme, the transmit power of artificial jamming is maximized, with its received power at each receiver higher than that of other users. Thus, the jamming signal can be eliminated via successive interference cancellation before others. When the transmit power of the BS is inadequate, the transmit jamming power is maximized with the jamming signal zero-forced at each receiver. Thus, the legitimate transmission is not affected by the jamming, and the eavesdropping can be disrupted effectively. Due to the non-convexity of these two optimization problems, we first convert them to convex ones and, then, provide an iterative algorithm to solve them. Simulation results are presented to show the effectiveness of the proposed schemes in guaranteeing the security of NOMA networks.

Non-orthogonal multiple access (NOMA) has been proposed as a promising multiple access approach for 5G mobile systems because of its superior spectrum efficiency. However, the privacy between the NOMA users may be compromised due to the transmission of a superposition of all users' signals to successive interference cancellation (SIC) receivers. In this paper, we propose two schemes based on beamforming optimization for NOMA that can enhance the security of a specific private user while guaranteeing the other users' quality of service (QoS). Specifically, in the first scheme, when the transmit antennas are inadequate, we intend to maximize the secrecy rate of the private user, under the constraint that the other users' QoS is satisfied. In the second scheme, the private user's signal is zero-forced at the other users when redundant antennas are available. In this case, the transmission rate of the private user is also maximized while satisfying the QoS of the other users. Due to the non-convexity of optimization in these two schemes, we first convert them into convex forms, and then, an iterative algorithm based on the Concave-Convex Procedure is proposed to obtain their solutions. The extensive simulation results are presented to evaluate the effectiveness of the proposed schemes.

Future networks are expected to connect an enormous number of nodes wirelessly using wide-band transmission. This brings great challenges. To avoid collecting a large amount of data from the massive number of nodes, computation over multi-access channel (CoMAC) is proposed to compute a desired function over the air utilizing the signal-superposition property of wireless channel. Due to frequency-selective fading, wide-band CoMAC is more challenging and has never been studied before. In this paper, we propose the use of orthogonal frequency division multiplexing (OFDM) in wide-band CoMAC to transmit functions in a similar way to bit sequences through division, allocation, and reconstruction of functions. An achievable rate without any adaptive resource allocation is derived. To prevent a vanishing computation rate from the increase in the number of nodes, a novel sub-function allocation of sub-carriers is derived. Furthermore, we formulate an optimization problem considering power allocation. A sponge-squeezing algorithm adapted from the classical water-filling algorithm is proposed to solve the optimal power allocation problem. The improved computation rate of the proposed framework and the corresponding allocation has been verified through both theoretical analysis and simulation.

Small-cell network is a promising solution to high video traffic. However, with the increasing number of mobile devices, it cannot meet the requirements from all users. Thus, we propose a caching device-to-device (D2D) scheme for small-cell networks, in which caching placement and D2D establishment are combined. In this scheme, a limited cache is equipped at each user, and the popular files can be prefetched at the local cache during off-peak period. Thus, dense D2D connections can be established during peak time aided by these cached users. To do this, first, an optimal caching scheme is formulated according to the popularity to maximize the total offloading probability of the D2D system. Then, the sum rate of D2D links is analyzed in different signal-to-noise ratio (SNR) regions. Furthermore, three D2D-link scheduling schemes are proposed with the help of bipartite graph theory and Kuhn-Munkres algorithm for low, high and medium SNRs, respectively. Simulation results are presented to show the effectiveness of the proposed scheme.

Security is a challenging issue for cognitive radio (CR) networks. Conventionally, interference will degrade the performance of a primary user (PU) when the spectrum is shared with secondary users (SUs). However, when properly designed, SUs can serve as friendly jammers to guarantee the secure transmission of PU. Thus, in this paper, we propose a optimal transceiver design scheme to improve the sum rate of SUs while guaranteeing the secrecy rate of PU. In the scheme, the secondary transceivers are jointly designed to maximize their sum rate while satisfying a threshold on the PU's secrecy rate. Due to the non-convex nature, it is first converted into a convex one and then, an alternating optimization algorithm based on the second-order cone programming is proposed to solve it. Finally, simulation results are presented to verify the effectiveness of the proposed scheme for secure CR networks.

Unmanned aerial vehicles (UAVs) have found many important applications in communications. They can serve as either aerial base stations or mobile relays to improve the quality of services. In this paper, we study the use of multiple UAVs in relaying. Considering two typical uses of multiple UAVs as relays that form either a single multi-hop link or multiple dual-hop links, we first optimize the placement of the UAVs by maximizing the end-to- end signal-to-noise ratio for two common relaying protocols. Based on the optimum placement, the two relaying uses are then compared in terms of outage and bit error rate. Numerical results show that the dual-hop option is better when the source-to- destination distance is small. Also, decode-and- forward UAVs provide better performances than amplify-and-forward UAVs. The investigation has also revealed the effects of important system parameters on the optimum UAV positions and the relaying performances to provide useful design guidelines.

Unmanned aerial vehicle (UAV) communication provides a promising solution to emergency recovery and coverage extension. For Internet of Things (IoT) communications system, utilizing UAV as an on-demand gateway is an efficient technique to enable the communication between IoT devices over a long distance. In this paper, we investigate a mobile UAV-aided Internet of Things (IoT) communications system, where one UAV actes as a dynamic aerial base station to serve all the IoT devices. We aim to maximize the sum rate of the mobile UAV by jointly optimizing IoT device-UAV scheduling, the uplink transmission power of the IoT devices and the UAV altitude. This optimization is a mixed-integer non-convex problem, and an efficient iterative algorithm is proposed by means of the block coordinate descent technique. Finally, simulation results are presented to demonstrate that our proposed scheme significantly outperforms the existing one.

Wireless powered communications allow sustainable operations of low‐power applications by supplying wireless power remotely. In this study, the performance of wireless powered communications is evaluated in terms of achievable rate and bit error rate (BER), for applications where the downlink and the uplink are correlated, in contrast to previous works that assume independent uplink and downlink. Semi‐closed expressions for the achievable rate and series expressions for the BER are derived in Nakagami m fading channels, based on which the effect of link correlation is examined. Numerical results show that the link correlation has a significant impact on the achievable rate. Consequently, the optimum system parameter for correlated links is very different from that for independent links, showing the usefulness of the authors' results. Also, the link correlation has a noticeable effect on the BER, depending on the system parameters considered.

Non‐orthogonal multiple access (NOMA) has been proposed as a promising technique for the fifth generation wireless networks. In this study, the authors investigate the performance of a cooperative relaying scheme using NOMA (termed NOMA‐AF‐CRS), where two sources communicate with their corresponding destinations over the same frequency simultaneously through a shared amplify‐and‐forward (AF) relay. First, the closed‐form approximations are derived for the outage probability. The analytical results are further evaluated in the high signal‐to‐noise ratio region to characterise the diversity order achieved by the system. Then, the ergodic sum capacity of NOMA‐AF‐CRS is analysed and an upper bound to the ergodic sum capacity is derived. Finally, simulation results are presented to verify the proposed analysis and demonstrate the advantages of NOMA‐AF‐CRS over the conventional orthogonal multiple access techniques.

Over-the-air computation combines communication and computation efficiently by utilizing the superposition property of wireless channels, when Internet of Things (IoT) networks focus more on the computed functions than the individual messages. In this paper, we study the computation of multiple linear functions of Gaussian sources over-the-air using antenna arrays at both the IoT devices and the IoT access point (AP). The key challenges in this paper are the intranode interference of multiple functions, the nonuniform fading between different IoT devices and the massive channel state information (CSI) required at the IoT AP. We propose a novel transmitter design at the IoT devices with zero-forcing beamforming to cancel the intranode interference and uniform-forcing power control to compensate the nonuniform fading. In order to avoid massive CSI requirement, receive antenna selection is adopted at the IoT AP and a corresponding signaling procedure is proposed utilizing the “OR” property of the wireless channel. The performance of the proposed transceiver design is analyzed. The closed-form expression for the mean squared function error (MSFE) outage is derived. Due to the complexity of the expression, an asymptotic analysis of the MSFE outage is further provided to demonstrate the diversity order in terms of the transmit power constraint and the number of IoT devices. Simulation results are presented to show the performance of the proposed design.

Security is a challenging issue for cognitive radio (CR) to be used in future 5G mobile systems. Conventionally, interference will degrade the performance of a primary user (PU) when the spectrum is shared with secondary users (SUs). However, when properly designed, SUs can serve as friendly jammers to guarantee the secure transmission of PU. Thus, in this paper, we propose two schemes to improve the sum rate of SUs while guaranteeing the secrecy rate of PU. In the first scheme, the secondary transceivers are jointly designed to maximize their sum rate while satisfying a threshold on the PU's secrecy rate. Due to the non-convex nature, it is first converted into a convex one and then, an alternating optimization algorithm based on the second-order cone programming is proposed to solve it. In the second scheme, the principle of interference alignment is employed to eliminate interference from PU and other SUs at each secondary receiver, and the interference from SUs is zero-forced at the primary receiver. Thus, interference-free transmission can be performed by the legitimate CR network, with eavesdropping towards PU disrupted by SUs. The key features and performances of the two proposed schemes are also compared. Finally, simulation results are presented to verify the effectiveness of the two proposed schemes for secure CR networks.

In this paper, new enhanced cognitive radio networks (E-CRNs) based on spectrum sharing (SS) and spectrum aggregation (SA) are proposed for fifth generation (5G) wireless networks. The E-CRNs jointly exploit the licensed spectrum shared with the primary user (PU) networks and the unlicensed spectrum aggregated from the industrial, scientific, and medical bands. The PU networks include TV systems in TV white space and different incumbent systems in the long term evolution time division duplexing bands. The harmful interference from the E-CRNs to the PU networks are delicately controlled. Furthermore, the coexistence between the E-CRNs and other unlicensed systems, such as WiFi, is studied. The E-CRNs framework including dynamic spectrum management (DSM) is designed for the key parameters of licensed SS and unlicensed SA. The essential tradeoff between sharing efficiency and aggregation efficiency for the E-CRNs is discussed. Based on this tradeoff, a spectrum lean-management scheme is proposed to fulfill the DSM. Moreover, a water-filling algorithm is designed to dynamically access the available spectrum. Numerical results demonstrate that the proposed E-CRNs can significantly improve the system performance in terms of data rate, outage probability, and spectrum efficiency. In particular, the E-CRNs framework provides a spectrum usage prototype for 5G wireless communication networks.

Unmanned aerial vehicles (UAVs) can help small-cell base stations (SBSs) offload traffic via wireless backhaul to improve coverage and increase rate. However, the capacity of backhaul is limited. In this paper, UAV assisted secure transmission for scalable videos in hyper-dense networks via caching is studied. In the proposed scheme, UAVs can act as SBSs to provide videos to mobile users in some small cells. To reduce the pressure of wireless backhaul, UAVs and SBSs are both equipped with caches to store videos at off-peak time. To facilitate UAVs, a single antenna is equipped at each UAV and thus, only the precoding matrices of SBSs should be cooperatively designed to manage interference by exploiting the principle of interference alignment. On the other hand, the SBSs replaced by UAVs will be idle. Thus, in order to guarantee secure transmission, the idle SBSs can be further exploited to generate jamming signal to disrupt eavesdropping. The jamming signal is zero-forced at the legitimate users through the precoding of the idle SBSs, without affecting the legitimate transmission. The feasibility conditions of the proposed scheme are derived, and the secrecy performance is analyzed. Finally, simulation results are presented to verify the effectiveness of the proposed scheme.

Unmanned aerial vehicles (UAVs) have found many important applications in communications. They can serve as either aerial base stations or mobile relays to improve the quality of services. In this paper, we study the use of multiple UAVs in relaying. Considering two typical uses of multiple UAVs as relays that form either a single multi-hop link or multiple dual-hop links, we first optimize the placement of the UAVs by maximizing the end-to-end signal-to-noise ratio for three useful channel models and two common relaying protocols. Based on the optimum placement, the two relaying setups are then compared in terms of outage and bit error rate. Numerical results show that the dual-hop multi-link option is better than the multi-hop single link option when the air-to-ground path loss parameters depend on the UAV positions. Otherwise, the dual-hop option is only better when the source-to-destination distance is small. Also, decode-and-forward UAVs provide better performances than the amplify-and-forward UAVs. The investigation also reveals the effects of important system parameters on the optimum UAV positions and relaying performances to provide useful guidelines.

The performance of a frequency division duplexing (FDD) massive multiple input multiple output (MIMO) system is traditionally limited by the large amount of overhead for downlink channel training and uplink channel state information (CSI) feedback. In this paper, we propose an environment-aware scheme to exploit massive MIMO gains in the FDD mode. Under a quasi-static scattering geometry and slow user mobility, the propagation environment can be known at a low cost. Given a priori environment information, the angular domain channel statistics can be obtained accordingly. In the proposed scheme, the angular domain is partitioned into several angular bins and the same number of predefined precoding vectors are generated accordingly. Based on the environment-specific angular domain information, the system topology is modeled as a bipartite graph. An efficient user scheduling algorithm is proposed, which is equivalent to finding a match of the bipartite graph, and a remarkable multiplexing gain is achieved by removing the overlapped angular bins. After user scheduling, each selected user is allocated to one predefined precoding vector. The numerical results have confirmed the validity of the proposed scheme.

Interference alignment (IA) networks seem secure, due to the fact that signals from the legitimate network may act as interference to disrupt the external eavesdropping. However, when some users inside the network are cooperating to eavesdrop one certain user, it will not be secure any longer. Thus, we concentrate on the eavesdropping attacks in this paper, and propose a novel collusive eavesdropping scheme (CES) in a K- user IA network, where one of the users is eavesdropped by an eavesdropper with the aid of the other (K - 2) cooperators. To perform the passive eavesdropping without being noticed by the targeted user, the precoding and decoding matrices of the eavesdropper and cooperators are re-designed, and some of the cooperators should sacrifice their own quality of transmission to help the eavesdropper meet the feasibility condition. Extensive simulation results are provided to show the eavesdropping effectiveness of the proposed CES in IA networks.

Idle nodes are used in cooperative communication to achieve performance gains. Energy harvesting (EH) technology makes cooperative communication less dependent on batteries. In this study, the performance of EH amplify‐and‐forward relaying is analysed by considering the throughput for Rician fading channels and non‐ideal relaying nodes, compared with previous works that focused on Rayleigh fading channels with ideal relaying nodes. The authors consider discrete time EH protocol and continuous time EH protocol. In addition, they derive the analytical expressions for the average throughput. Numerical results are presented to show the good performance of the system in Rician fading channels and the satisfactory performance while using the non‐ideal relaying node by examining various system parameters using the analytical expressions.

In this study, a wireless powered dual‐hop relay system, which consists of a source, a destination and a relay that is equipped with ( ) receiving antennas and finite energy storage, is considered. Antenna selection scheme is employed for the relay to harvest energy from the received signals, under which the ( ) strongest received signals are combined for information processing and the other copies of signals are used for energy harvesting. Considering Nakagami‐ m fading scenarios, the authors first derive the closed‐form expressions for the probability density function and the cumulative distribution function of the signal‐to‐noise ratio with generalised selection combining (GSC), and then the exact and asymptotic analytical expressions for outage probability are derived, respectively. Finally, simulation results are presented to verify the proposed analysis model.

This paper investigates the secrecy performance of maximal ratio combining (MRC) and selection combining (SC) with imperfect channel state information (CSI) in the physical layer. In a single-input multipleoutput (SIMO) wiretap channel, a source transmits confidential messages to the destination equipped with M antennas using the MRC/SC scheme to process the received multiple signals. An eavesdropper equipped with N antennas also adopts the MRC/SC scheme to promote successful eavesdropping. We derive the exact and asymptotic closed-form expressions for the ergodic secrecy capacity (ESC) in two cases: (1) MRC with weighting errors, and (2) SC with outdated CSI. Moreover, two important indicators, namely high signal-to-noise ratio (SNR) slope and high SNR power offset, which govern ESC at the high SNR region, are derived. Finally, simulations are conducted to validate the accuracy of our proposed analytical models. Results indicate that ESC rises with the increase of the number of antennas and the received SNR at the destination, and fades with the increase of those at the eavesdropper. Another finding is that the high SNR slope is constant, while the high SNR power offset is correlated with the number of antennas at both the destination and the eavesdropper.

In this paper, we study the average, the probability density function, and the cumulative distribution function of the harvested power. The signals are transmitted from multiple sources. The channels are assumed to be either Rician fading or Gamma-shadowed Rician fading. The received signals are then harvested by using either a single harvester for simultaneous transmissions or multiple harvesters for transmissions at different frequencies, antennas or time slots. Both linear and nonlinear models for the energy harvester at the receiver are examined. Numerical results are presented to show that, when a large amount of harvested power is required, a single harvester or the linear range of a practical nonlinear harvester are more efficient, to avoid power outage. Further, the power transfer strategy can be optimized for fixed total power. Specifically, for Rayleigh fading, the optimal strategy is to put the total power at the source with the best channel condition and switch off all other sources, while for general Rician fading, the optimum magnitudes and phases of the transmitting waveforms depend on the channel parameters.

Considering a dual-hop energy-harvesting (EH) relaying system, this paper advocates novel relaying protocols based on adaptive time-switching (TS) for amplify-and-forward and decode-and-forward modes, respectively. The optimal TS factor is first studied, which is adaptively adjusted based on the dual-hop channel state information (CSI), accumulated energy, and threshold signal-to-noise ratio (SNR), to achieve the maximum throughput efficiency per block. To reduce the CSI overhead at the EH relay, a low-complexity TS factor design is presented, which only needs single-hop CSI to determine the TS factor. Theoretical results show that, in comparison with the conventional solutions, the proposed optimal/low-complexity TS factor can achieve higher limiting throughput efficiency for sufficiently small threshold SNR. As the threshold SNR approaches infinity, the throughput efficiency of the proposed optimal/low-complexity TS factor tends to zero in a much slower pace than that of the conventional solutions. Simulation results are presented to corroborate the proposed methodology.

Interference alignment (IA) can be secure due to the fact that the received signal of a targeted user at the eavesdropper may be embedded by interference from other concurrent users. However, when some malicious users inside the network cooperate to eavesdrop one specific user, the network will not be secure any more. Thus, we focus on eavesdropping attacks, and propose a novel collusive eavesdropping scheme (CES) in a K-user IA-based network. In this scheme, one user is eavesdropped on by an eavesdropper with the aid of the other (K - 2) users. To perform passive eavesdropping without being noticed by the targeted user, the precoding and decoding matrices of the eavesdropper and its cooperators are redesigned, and some of the cooperators sacrifice their own quality of transmission to help the eavesdropper meet the feasibility condition. Therefore, the feasibility condition of CES is derived, based on which the minimal number of low-quality cooperators and the maximal number of receiving antennas at each user are obtained. The received power of eavesdropping is analyzed with different numbers of antennas at each receiver, which also affects the eavesdropping performance. Extensive simulation results are provided to show the effectiveness of CES.

Existing channel estimators for amplify-and-forward relaying often transmit pilots to the destination node by using the relay node's own energy. This extra energy consumption discourages the relay node from taking part in relaying. We propose two new estimators for amplify-and-forward relaying channels. In these estimators, the relay node harvests energy from the pilots sent by the source node first and then uses the harvested energy to forward the pilots from the source node as well as transmit its own pilots to the destination node. Both time-switching and power-splitting harvesting strategies are considered. The mean squared error is examined. Numerical results show that these new estimators have very good performances. They also show that optimum choices of certain system parameters exist.

In this study, a new spectrum measurement modelling method is proposed for several important frequency bands by using the Daubechies wavelets. On the basis of this method, spectrum measurement prediction is also proposed by using regression. Unlike most previous works that model or predict the occupancy rate of the frequency band of interest, this study models and predicts the power measurements directly to remove the dependence of the model on the challenging detection threshold and also to provide more comprehensive descriptions of the licenced user signals for performance improvement in cognitive radios (CRs). Numerical results show that the new spectrum models have acceptable accuracies. They also show that the proposed spectrum measurement prediction method tracks the trend of the true values well. Therefore, these results are very useful in CR designs.

In this study, two optical wireless indoor multiple‐input–multiple‐output systems using on–off keying and pulse position modulation are studied. Then, by using the Matlab curve‐fitting, the training parts of the experimental results are fitted and evaluation models are developed, where root mean squared error and additional testing data are used to verify the accuracy of the evaluation models.

We investigate the secrecy outage performance of maximal ratio combining (MRC) in cognitive radio networks over Rayleigh fading channels. In a single-input multiple-output wiretap system, we consider a secondary user (SU-TX) that transmits confidential messages to another secondary user (SU-RX) equipped with M ( M ≥ 1) antennas where the MRC technique is adopted to improve its received signal-to-noise ratio. Meanwhile, an eavesdropper equipped with N ( N ≥ 1) antennas adopts the MRC scheme to overhear the information between SU-TX and SU-RX. SU-TX adopts the underlay strategy to guarantee the service quality of the primary user without spectrum sensing. We derive the closed-form expressions for an exact and asymptotic secrecy outage probability.

In this paper, a multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system, including one base station (BS) equipped with multiple antennas, one desired single-antenna information receiver (IR), and N (N > 1) single-antenna energy-harvesting receivers (ERs) is considered. Assuming that the information signal to the desired IR may be eavesdropped by ERs if ERs are malicious, we investigate the secrecy performance of the target MISO SWIPT system when imperfect channel state information (CSI) is available and adopted for transmit antenna selection at the BS. Considering that each eavesdropping link experiences independent but not necessarily identically distributed Rayleigh fading, the closed-form expressions for the exact and the asymptotic secrecy outage probability, and the average secrecy capacity are derived and verified by simulations. Furthermore, the optimal power splitting factor is derived for each ER to realize the tradeoff between the energy harvesting and the information eavesdropping. Our results reveal the impact of the imperfect CSI on the secrecy performance of MISO SWIPT systems in the presence of multiple wiretap channels.

Energy-harvesting relaying is a promising solution to the extra energy requirement at the relay. It can transfer energy from the source to the relay. This will encourage more idle nodes to be involved in relaying. In this paper, the outage probability and the throughput of an amplify-and-forward relaying system using energy harvesting are analyzed. Both time switching and power-splitting harvesting schemes are considered. The analysis takes into account both the Nakagami-m fading caused by signal propagation and the interference caused by other transmitters. Numerical results show that time switching is more sensitive to system parameters than power splitting. Also, the system performance is more sensitive to the transmission rate requirement, the signal-to-interference-plus-noise ratio in the first hop and the relaying method.

Abstract Consider a system in which the signal is transmitted from the source to the destination via a relay. The source–relay link and the relay–destination link are assumed to have Rician fading. For such a system, in this paper, we propose a novel non‐coherent receiver that detects the signal without knowledge of channel state information (CSI). In addition, we also propose novel receivers for the situation when only partial CSI is available at the destination; that is, channel knowledge of either the source–relay link or the relay–destination link, but not both, is available. These receivers are termed as “half‐coherent receivers” because they have the CSI of only one of the two links in the system. These half‐coherent receivers can be used in applications in which only the relay can afford additional complexity (e.g., the relay may be a base station) and includes a channel estimation module to acquire channel knowledge of the source–relay link or only the destination includes the channel estimation module to acquire channel knowledge of the relay–destination link. The symbol error rate performances of the proposed receivers forM‐ary frequency‐shift keying are derived for Rician fading. Numerical results show that considerable performance gains can be achieved by using these new receivers, especially at a low signal‐to‐noise ratio where the gain is needed most. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Energy detection is widely used in cognitive radio due to its low complexity. One fundamental challenge is that its performance degrades in the presence of noise uncertainty, which inevitably occurs in practical implementations. In this work, three novel detectors based on uniformly distributed noise uncertainty as the worst‐case scenario are proposed. Numerical results show that the new detectors outperform the conventional energy detector with considerable performance gains. Copyright © 2015 John Wiley & Sons, Ltd.

Bio‐inspired techniques, including firefly algorithm, fish school search, and particle swarm optimisation, are utilised in this study to evaluate the optimal weighting vectors used in the data fusion centre. This evaluation is performed for more realistic signals that suffer from non‐linear distortions, caused by the power amplifiers. The obtained optimal weighting vectors are then used for collaborative spectrum sensing and spectrum allocation in cognitive radio networks. Numerical results show that bio‐inspired techniques outperform the conventional algorithms used for spectrum sensing and allocation by deriving optimal weights that ensure the highest value of probability of detection and guarantee the maximum proportional fair reward for users.

In this study, secrecy outage performance is studied for partial relay selection (PRS) schemes in cooperative systems over Rayleigh fading channels. In the considered systems, there are one source (S), one destination (D), one eavesdropper, one jammer that can send interference to the eavesdropper, and N ( N > 1) relay candidates, one of which is selected as the relay (R). By considering the practical scenarios and to protect the transmitted information over R–D link from being eavesdropped, the authors propose a new transmission scheme: let D send random information to R while S sending information to R. Then, R processes the messages from S and D by XOR operation in order to prevent the eavesdropper's interception while the information is delivered between R and D. They study the secrecy outage performance for two PRS schemes: (i) the optimal source–relay link (OSRL) scheme; and (ii) the optimal source–relay–destination link (OSRDL) scheme. The closed‐form expressions for the secrecy outage probability (SOP) of the two PRS schemes are derived, as well as the closed‐form upper bound for SOP under OSRL scheme and the lower bound for SOP under OSRDL scheme. Finally, simulation results are given to verify the presented analytical models and the proposed transmission scheme.

In this paper, a single-input multiple-output (SIMO) simultaneous wireless information and power transfer system consisting of one base station, one desired information receiver and N (N > 1) energy-harvesting receivers is considered. Each link in the considered system suffers not only from small-scale fading but also path-loss. In particular, we focus on the physical layer security performance including secrecy outage and secrecy capacity in the presence of eavesdroppers. We derive the closed-form analytical expression for the exact and the asymptotic secrecy outage probability while considering each eavesdropping link experiences independent but not necessarily identical Rayleigh fading. The closed-form analytical expression for the average secrecy capacity has also been derived for the case that all eavesdropping links experience independent and identical Rayleigh fading. Numerical and Monte Carlo simulation results are presented to verify our derived analytical models.

Three new approximations to the probability density function (PDF) of the symmetric alpha stable (SαS) distribution are proposed. The first two approximations use rational functions while the third approximation uses power functions. Using these approximations, new detectors for signals in symmetric alpha stable noise are also derived. Numerical results show that all these new approximations have good accuracies. Numerical results also show that the new detectors based on these approximations outperform the existing detectors, especially when the characteristic exponent of the symmetric alpha stable distribution is small.

Bit error rates (BERs) for amplify-and-forward (AF) relaying systems with two different pilot-symbol- aided channel estimation methods, disintegrated channel estimation (DCE) and cascaded channel estimation (CCE), are derived in Rayleigh fading channels. Based on these BERs, the pilot powers at the source and at the relay are optimized when their total transmitting powers are fixed. Numerical results show that the optimized system has a better performance than other conventional nonoptimized allocation systems. They also show that the optimal pilot power in variable gain is nearly the same as that in fixed gain for similar system settings.

The sum of ratios of products of independent 2642 2642α - μ random variables (RVs) is approximated by using the Generalized Gamma ratio approximation (GGRA) with Gamma ratio approximation (GRA) as a special case. The proposed approximation is used to calculate the outage probability of the equal gain combining (EGC) or maximum ratio combining (MRC) receivers for wireless multihop relaying or multiple scattering systems considering interferences. Numerical results show that the newly derived approximation works very well verified by the simulation, while GRA has a slightly worse performance than GGRA when outage probability is below 0.1 but with a more simplified form.

Rational approximation to the non‐linear score function used in the locally optimum detector is derived for signals corrupted by the impulsive symmetric alpha stable noise. The new approximation uses a third‐order polynomial in the numerator and a fourth‐order polynomial in the denominator, compared with the existing approximation that uses a first‐order polynomial in the numerator and a second‐order polynomial in the denominator. The parameters of the polynomials are derived using non‐linear least squares curve fitting. The relationships between the polynomial parameters and the value of the characteristic exponent are also obtained. Numerical results show that the proposed approximation has superior accuracy to the existing approximations. The proposed new approximation is then applied to the locally optimum detector by replacing the score function in the decision variable. Numerical results show that the proposed detector, optimised according to a curve‐fitting approach, outperforms previous approximations of the locally optimum detector, also optimised according to a curve‐fitting approach, for binary phase shift keying signals and in some cases for on–off keying signals.

In this work, the impact of outdated channel state information because of feedback delay is evaluated for the performances of several generalised relay selection schemes. Multi‐hop amplify‐and‐forward relaying is considered over Rayleigh fading channels. Numerical results show that different relay selection schemes suffer from different performance degradations when the hop index increases. Interestingly, for generalised relay selection, numerical results also show that selecting more relays does not necessarily lead to better power outage probability and bit error rate performances. These observations provide useful guidance on relaying system designs.

The effect of spectrum sensing errors on the performance of bit loading algorithms in cognitive radio systems based on orthogonal frequency division multiplexing is studied in terms of throughput and average bit error rate. Different spectrum sensing parameters and data transmission parameters are examined to evaluate the performance degradation. Numerical results show that the sensing errors cause significant performance degradation and that, when the signal‐to‐noise ratio is high, the inter‐carrier interferences caused by carrier frequency offset are dominant in the performance degradation.

Closed‐form expressions of the upper and lower bounds of the outage probability are derived for interference‐limited dual‐hop decode‐and‐forward and channel‐state‐information‐assisted amplify‐and‐forward relay systems using three relay selection (RS) schemes [optimal source–relay link (OSRL) scheme, optimal relay–destination link (ORDL) scheme and optimal source–relay–destination link (OSRDL) scheme] over independent log‐normal fading channels. Multiple interferers at both relay and destination are considered. The diversity order of the targeted cooperative system under the three different RS schemes is analysed. The analysis shows that the asymptotic relative diversity orders (ARDO) for OSRL, ORDL and OSRDL schemes are 2, 2 and ( N + 1) (where N is the number of the relay candidates), respectively. Finally, the ARDO is verified by simulation results.

Bit error rate (BER) and outage probability for amplify-and-forward (AF) relaying systems with two different channel estimation methods, disintegrated channel estimation and cascaded channel estimation, using pilot-aided maximum likelihood method in slowly fading Rayleigh channels are derived. Based on the BERs, the optimal values of pilot power under the total transmitting power constraints at the source and the optimal values of pilot power under the total transmitting power constraints at the relay are obtained, separately. Moreover, the optimal power allocation between the pilot power at the source, the pilot power at the relay, the data power at the source and the data power at the relay are obtained when their total transmitting power is fixed. Numerical results show that the derived BER expressions match with the simulation results. They also show that the proposed systems with optimal power allocation outperform the conventional systems without power allocation under the same other conditions. In some cases, the gain could be as large as several dB's in effective signal-to-noise ratio.

In this paper, we study power allocation strategies for a fixed-gain amplify-and-forward relay network employing multiple relays. We consider two optimization problems for the relay network: 1) maximizing the end-to-end signal-to-noise ratio (SNR) and 2) minimizing the total power consumption while maintaining the end-to-end SNR over a threshold value. We investigate these two problems for two relaying protocols of all-participate (AP) relaying and selective relaying and two cases of feedback to the relays, namely full and limited. We show that the SNR maximization problem is concave and that the power minimization problem is convex for all protocols and feedback cases considered. We obtain closed-form expressions for the two problems in the case of full feedback and solve the problems through convex programming for limited feedback. Numerical results show the benefit of having full feedback at the relays for both optimization problems. However, they also show that feedback overhead can be reduced by having only limited feedback to the relays with only a small degradation in performance.

This paper investigates the energy-efficiency enhancement of a variable-gain dual-hop amplify-and-forward (AF) relay network utilizing selective relaying. The objective is to minimize the total consumed power while keeping the end-to-end signal-to-noise-ratio (SNR) above a certain peak value and satisfying the peak power constraints at the source and relay nodes. To achieve this objective, an optimal relay selection and power allocation strategy is derived by solving the power minimization problem. Numerical results show that the derived optimal strategy enhances the energy-efficiency as compared to a benchmark scheme in which both the source and the selected relay transmit at peak power.

Previous works on spectrum sensing use either samples free of secondary user interference based on dedicated sensing period or samples with partly ‘cleaned’ secondary user interference based on specific characteristics of orthogonal frequency division multiplexing signals or decoded signals. In this article, collaborative spectrum sensing using samples corrupted by secondary user interference without dedicated sensing period or ‘cleaning’ procedure is studied, and its performance is analysed. Two different cases of primary user signals are considered. In the first case, the primary user signal suffers from fast fading, whereas in the second case, the primary user signal suffers from slow fading. New detectors for these two cases are derived, based on which their performances are analysed. Numerical results show that the diversity gain is lost because of the secondary user interference. For fast‐fading primary user signal, the sensing performance deteriorates quickly when the number of interfering secondary users increases, whereas for slowly fading primary user signal, an error floor exists when the number of interfering secondary users increases. On the other hand, by choosing appropriate system parameters, high sensing accuracy can still be achieved with acceptable loss in detection probability, but with long transmission time when no dedicated sensing period or ‘cleaning’ procedure are used.

In a cognitive relay system, the secondary user is permitted to transmit data via a relay when licensed frequency bands are detected to be free. Previous studies mainly focus on reducing or limiting the interference of the secondary transmission to the primary users. On the other hand, however, the primary user traffic also affects the data transmission performance of the secondary users. In this paper, we mainly investigate the impact of the primary user traffic on the bit error rate (BER) of the secondary transmission, when the secondary user adopts adaptive transmission with a partially selected relay. In addition, the average collision time and the average collision probability have been used to measure the interference level caused by the secondary transmission to the primary users. Based on some selected numerical results, we can see that the primary user traffic seriously degrades the average BER. The worse-link partial selection can perform almost as well as the global selection when the channel conditions of the source-relay links and the relay-destination links are quite different. Although the relay selection improves the spectral efficiency of the secondary transmission, numerical results show that it only has slight impact on the overall average BER, which implies that the robustness of the system will not be affected by the relay selection.

ABSTRACT Collaborative spectrum sensing with user selection is analyzed for correlated shadowing in the practical cases of imperfect power measurements and noisy secondary links. The user selection is performed using the minimum probability of missed opportunity criterion and the random search criterion. Both the Neyman–Pearson rule and the minimum probability of error rule are considered. The sensing performance is examined with respect to different system parameters. Numerical results show that user selection can achieve a large amount of resource saving at little performance loss. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT The channel capacity and the bit error rate of ultra‐wide bandwidth transmitted‐reference systems are optimized with respect to time delay between the reference and the adjacent data pulses. Approximate and theoretical expressions for the signal‐to‐interference‐plus‐noise ratio are derived. Numerical results show that optimizing the receiver can provide a significant capacity improvement of up to 2.2 bits/s/GHz and a bit error rate performance gain of up to 2 dB in effective signal‐to‐noise ratio. Copyright © 2011 John Wiley & Sons, Ltd.

The performance of spectrum sensing using the received secondary frames is analyzed. Unlike the previous work that assumes perfect decoding of the secondary signal, the new analysis takes the decoding errors into account and therefore provides a more realistic comparison between the new model and the conventional model. Both the receiver operating characteristics for spectrum sensing and the achievable throughput for data transmission are derived. Numerical results show that the new model that considers the decoding error outperforms the conventional model when the number of transmitted secondary frames is below a certain threshold. An upper bound performance can also be obtained by ignoring the decoding error.

In this paper, a new resource allocation scheme to minimize Symbol Error rate (SER) for relay assisted cognitive radio networks is studied. A cognitive relay network with amplify-and-forward (AF) fixed gain relays and opportunistic access to the licensed spectrum is considered. The SER objective function and the optimization constraints are derived first. The resource allocation algorithm is then derived using the Langrangian multiplier method. Closed form expressions are derived for the source and relay powers and, symbol time. Analytical results show that the optimal power allocation follows an extended water-filling solution.

A novel analytical framework for evaluating the statistics of the products of independent random variables is proposed. Compared with other methods which use either an infinite series or a special function, the new method provides simple and efficient closed-form approximations in terms of elementary functions, such as powers and exponentials, and therefore, is very easy to implement. The accuracy of the new approximation is examined. Numerical results show that it is quite accurate in most regions of interest. As an application, these new approximations are used in wireless communications theory to derive novel closed-form expressions for the outage probability of cascaded fading channels. Numerical examples show that the newly derived closed-form expressions provide insights on the behavior of important performance metrics as the outage probability, the bit error rate and the channel capacity.

In this paper, we present two optimal resource allocation schemes that maximize throughput and symbol correct rate (SCR). The throughput and SCR are derived. The derived throughput and SCR are optimized with respect to the sensing time, the source transmission power and the relay transmission power. Numerical results show that the optimal sensing time is dependent on the primary user's signal-to-noise-ratio (SNR). They also show that SCR increases with increase in the number of relays.

The bit error rate (BER) performance of chaos shift keying is improved for an additive white Gaussian noise channel by applying a trimming operation to the Chebyshev polynomial function of order 2 and the piecewise linear map to make the input source more Gaussian distributed. Analytical expressions for the improved BER are also derived based on the curve-fitting approximation. Numerical results show that the proposed method can achieve performance gains of up to 3.5 dB in signal-to-noise ratio over the conventional method without trimming.

The effect of spectrum sensing errors on the performance of cognitive radio transmission based on orthogonal frequency division multiplexing is evaluated by deriving analytical expressions for the average capacity and the average bit error rate as functions of different spectrum sensing parameters and data transmission parameters in the Rayleigh fading channels. Both the case with carrier frequency offset and the case without carrier frequency offset are considered. Numerical results are presented to show that spectrum sensing errors cause significant performance degradation and that the amount of performance degradation depends on the specific values of sensing and transmission parameters. In particular, the performance degradation caused by the primary user interference is more sensitive to the interfering amplitude and the number of subcarriers than to the probability of detection in spectrum sensing, the interfering frequency and the availability of the licensed band. Numerical results also show that the primary user interference caused by the sensing errors is dominant for small to medium values of the operating signal-to-noise ratio, while the inter-carrier interference caused by carrier frequency offset is dominant for large values of the operating signal-to-noise ratio.

New coherent receivers are derived for a pilot-symbol-aided distributed space-time block-coded system with imperfect channel state information which do not perform channel estimation at the destination by using the received pilot signals directly for decoding. The derived receivers are based on new metrics that use distribution of the channels and the noise to achieve improved symbol-error-rate (SER) performance. The SER performance of the derived receivers is further improved by utilizing the decision history in the receivers. The decision history is also incorporated in the existing Euclidean metric to improve its performance. Simulation results show that, for 16-quadrature-amplitude-modulation in a Rayleigh fading channel, a performance gain of up to 2.5 dB can be achieved for the new receivers compared with the conventional mismatched coherent receiver.

The performance of spectrum sensing using the recovered secondary frames is analyzed. Unlike the previous work that assumes perfect decoding of the secondary signal, the new analysis takes the decoding errors into account and therefore provides a more realistic comparison between the new model and the conventional model. Both the receiver operating characteristics curves for spectrum sensing and the achievable throughput for data transmission are derived. Effects of fading and error control codes are also investigated. Numerical results show that the new model that considers the decoding error outperforms the conventional model when the number of transmitted secondary frames is below a certain threshold. An upper bound performance can also be obtained by ignoring the decoding error. The threshold is determined by the primary user traffic, the spectrum sensing technique and the secondary signal modulation scheme.

Abstract The performance of collaborative spectrum sensing is examined for lognormal shadowing channels when secondary user interferences (SUIs) occur. Both soft decision rule and hard decision rule are considered. Computer simulation shows that the SUIs can be ignored in some cases, depending on the signal‐to‐noise ratio (SNR), the signal‐to‐interference ratio, the number of secondary users, the sample correlation, and the decision rule. Thus, by choosing appropriate system parameters, one may conduct collaborative spectrum sensing at the same time as secondary data transmission to save overheads. Copyright © 2010 John Wiley & Sons, Ltd.

An amplify-and-forward (AF) multihop relaying system employing constant-power rate-adaptive M-QAM transmission is investigated. Accurate expressions for performance evaluation in Nakagami-m fading of adaptive continuous rate (ACR) and adaptive discrete rate (ADR) M-QAM are obtained. The analysis is based on a new approximation for the instantaneous end-to-end received SNR in the form of a scaled version of the harmonic mean of the individual per-hop SNRs. The scaling factor is determined based on the average link SNRs. Performance results evaluated using the proposed approximation are more accurate than the performance bounds obtained using the conventional harmonic- mean-based upper bound on the instantaneous received SNR, even though they are obtained with the same computational complexity. Numerical results show that the approximate performance results are very close to the exact results especially in small SNR regimes and for larger values of the Nakagami parameter. It is shown that the ACR QAM achieves an average spectral efficiency within a certain distance of the Shannon limit. The ADR QAM achieves smaller bit error rates than the target bit error rate but at the expense of an additional penalty on the average spectral efficiency, especially for smaller numbers of region boundaries.

In this paper, we propose a new spectrum handoff scheme with spectrum admission control (SAC). In the proposed scheme, the secondary users (SUs) make up secondary user groups (SUGs) to achieve appointed detection probability of primary user signals, and perform spectrum handoff to an available spectrum when the primary users (PUs) reuse the spectrum in cognitive radio networks (CRNs). A simple Markov model is adopted to analyze the performance of spectrum handoff in terms of blocking probability, forced termination probability and throughput of the cognitive radio system. Numerical results show that the new handoff strategy is suitable for multi-user cognitive radio systems, and that spectrum admission control and cooperative sensing can effectively increase the efficiency of spectrum handoff.

The performance of adaptive modulation for cognitive radio with opportunistic access is analyzed by considering the effects of spectrum sensing and primary user traffic for Nakagami-$m$ fading channels. Both the adaptive continuous rate scheme and the adaptive discrete rate scheme are considered. Numerical results show that spectrum sensing and primary user traffic cause considerable degradation to the bit error rate performance of adaptive modulation in a cognitive radio system with opportunistic access to the licensed channel. They also show that primary user traffic does not affect the link spectral efficiency performance of adaptive modulation, while the spectrum sensing degrades the link spectral efficiency performance.

New low-complexity adaptive receivers are proposed for time-hopping binary phase shift keying ultra-wide bandwidth systems with multiple users. Both additive white Gaussian noise and multipath fading channels are considered. Numerical results show that the new adaptive receivers outperform the conventional matched-filter receivers in both additive white Gaussian noise and multipath fading channels, when the multiple-access interference is present. Moreover, the performance gain increases when the number of users decreases or the signal-to-noise ratio increases.

For a single relay network, disintegrated channel estimation (DCE), where the source-relay channel is estimated at the relay and the relay-destination channel is estimated at the destination, gives better performance than the cascaded channel estimation. We derive novel receivers for the relay network with disintegrated channel estimation. The derived receivers do not require channel estimation at the destination, as they use the received pilot signals and the source-relay channel estimate for decoding directly. We also consider the effect of quantized source-relay channel estimate on the performance of the designed receivers. Simulation results show that a performance gain of up to 2.2 dB can be achieved by the new receivers, compared with the conventional mismatched coherent receiver with DCE.

Coherent receivers are derived for a pilot-symbol aided distributed Alamouti-coded system with imperfect channel state information. The derived coherent receivers do not perform channel estimation but rather use the received pilot signals for decoding. The derived receiver metrics use the statistics of the channel to give improved performance. The performance is further improved by using the decision history. Simulation results show that a performance gain of up to 1.8 dB can be achieved for the new receivers with decision history as compared with the conventional mismatched coherent receiver.

A new approximation to the average symbol error rate of amplify-and-forward diversity is derived for independent and non-identically distributed Nakagami-m fading channels. The new approximation has low computational complexity. Numerical results show that it is almost identical to the exact average symbol error rate in all the cases considered.

The performances of four commonly used feature-based detectors for spectrum sensing are compared by assuming that the primary user may arrive or depart during the sensing period, a realistic case when the primary user traffic is high or the sensing period is long. Numerical results show that the frequent state transition of the licensed channel degrades the detector performances and that the amount of degradation depends on the primary user traffic intensity, the number of samples used, as well as the primary user signal-to-noise ratio. Also, our numerical examples show that different feature-based detectors have different robustness to the primary user traffic, allowing us to choose the best feature-based detector in terms of the primary user traffic.

The instantaneous end-to-end received signal-to-noise ratio (SNR) in K-hop amplify-and-forward (AF) relaying systems is commonly upper bounded by 1/k of the harmonic mean of the individual per-hop instantaneous SNRs. It is shown that performance results in Nakagami-m fading of AF multihop relaying systems obtained based on this upper bound are not tight for small values of SNR or for larger values of the Nakagami parameter and, depending on certain channel conditions, these bounds may become looser as the number of hops increases. A new approximation to the instantaneous end-to-end received SNR is introduced in the form of a scaled version of the harmonic mean of the individual per-hop SNRs. The scaling factor is determined based on the average link SNRs. Performance results evaluated using the proposed approximation are more accurate than all performance bounds previously reported in the literature, even though they are obtained with the same computational complexity. Numerical results show that the approximate performance results are very close to the exact results especially in small SNR regimes or for larger values of the Nakagami parameter.

The effect of the primary user traffic on the performance of the secondary network is investigated for the tradeoff between the sensing quality and the achievable throughput. Numerical results show that the actual secondary network performance when the random departure or arrival of the primary user is taken into account is worse than the predicted secondary network performance in the literature assuming constant occupancy state of the primary user. The degree of degradation depends on the traffic intensity as well as the received signal-to-noise ratio at the secondary user. Also, unlike the conventional model where the occupancy state of the primary user is assumed constant, the optimal sensing time in the new model varies for different primary channel conditions when the primary user traffic is considered.

New combining schemes for partial decisions using one-bit and two-bit information of the received samples are derived for Rayleigh fading channels with diversity. Compared to previous partial decision combining schemes, these new schemes have either better performances, or simpler structures, or both. Simulation results show that some of the new schemes can achieve considerable performance gains over the conventional partial decision combiner in Rayleigh fading or lognormal shadowed Rayleigh fading, while their structures are much simpler than the conventional partial decision combiner. This confirms the advantages of the newly designed combining schemes.

New and improved energy detectors for cognitive radios are derived by considering the effect of the primary user traffic on spectrum sensing. The new energy detectors are designed based on the assumption that the primary user randomly arrives or departs during the sensing period. Numerical results show that the new energy detector outperforms the conventional energy detector in all the cases examined. The performance gain depends on the operating signal-to-noise ratio as well as the sample size used.

The signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR) for multiuser time-hopping binary phase shift keying ultra-wide bandwidth impulse radio systems are jointly estimated using the moment-based method. Both additive white Gaussian noise channels and multipath fading channels are considered. Numerical results show that the new SNR and SIR estimators have normalized root-mean-squared errors of less than 0.2 in most cases considered.

Novel non-data-aided near-maximum-likelihood estimators for the delays and the attenuations in an ultra-wide bandwidth channel are proposed by using an approximation to the maximum likelihood equation. Numerical results show that these new estimators outperform previous approximate non-data-aided maximum likelihood channel estimators reported in the literature. Moreover, in some cases, the performances of the new non-data-aided estimators approach those of the data-aided estimators, enabling a reduction in the overhead expense of pilot symbols.

A new approximation to the average symbol error rate of cooperative diversity is derived for independent and non-identically distributed Nakagami-m fading channels. The new approximation has the same computational complexity as previous lower bounds in the literature, while it is much more accurate than the lower bounds, especially when the signal-to-noise ratio is small to medium. Numerical results show that the new approximation is almost graphically identical to the exact average symbol error rate for moderate values of the signal-to-noise ratio.

New and improved energy detector for random signals in Gaussian noise is proposed by replacing the squaring operation of the signal amplitude in the conventional energy detector with an arbitrary positive power operation. Numerical results show that the best power operation depends on the probability of false alarm, the probability of detection, the average signal-to-noise ratio or the sample size. By choosing the optimum power operation according to different system settings, new energy detectors with better detection performances can be derived. These results give useful guidance on how to improve the performances of current wireless systems using the energy detector. It also confirms that the conventional energy detector based on the generalized likelihood ratio test using the generalized likelihood function is not optimum in terms of the detection performance.

The performance of collaborative spectrum sensing using censored energy detection is analyzed. Unlike the conventional energy detector that applies the measurements from the interested band directly to the decision-making process, the censored energy detector selects the measurements at different collaborating users by comparing them with two pre-determined limits before applying them to collaborative spectrum sensing. Both soft decision and hard decision rules are considered. Using the Neyman-Pearson criterion, analytical expressions for the probability of detection are derived and are verified by simulation. A simplified censored energy detector based on the Gamma approximation is also obtained. Using the derived results, it is shown that the censored energy detector outperforms the conventional energy detector when the optimum limits are used in the censoring. The performance gain depends on the decision rule, the operating signal-to-noise ratio and the number of collaborating users.

Generalized ultra-wide bandwidth (UWB) transmitted reference (TR) receivers proposed previously in the literature are improved with respect to the energy allocation in the data packet and the integration interval length in the correlator. The improvement refers to a best-effort technique to reduce the receiver bit error rate. Simulation results show that the improvement can provide a performance gain of up to 4.2 dB in signal-to-noise ratio. These results give useful guidance on the design of generalized UWB TR receivers.

Novel non-data-aided maximum likelihood estimators for the delays and the attenuations in an ultra-wide bandwidth channel are proposed. Numerical results show that these new estimators outperform the previous non-data-aided maximum likelihood channel estimators derived in the literature. Moreover, in some cases, the performances of the new non-data-aided estimators approach those of the data-aided estimators, enabling us to reduce the overhead expense of pilot symbols.

The performance of collaborative spectrum sensing for cognitive radio in a lognormal shadowing and Rayleigh fading channel is investigated. Unlike previous works that assume perfect knowledge of the average channel signal-to-noise ratio, this paper considers the realistic case where estimation of the average channel signal-to-noise ratio has error. Furthermore, while previous works have designed and examined estimators based on assuming that the channel samples used in the average signal-to-noise ratio estimators are noiseless, the present work assumes that the channel samples are noisy, as is the case in practical systems. Numerical results show that the probability of missed opportunity decreases as the estimation error decreases, as expected. Perhaps unexpectedly, the results also show that in the presence of noise, there exists a threshold phenomenon for the noise level. Below a particular threshold, the probability of missed opportunity increases as the noise level increases. Yet above this threshold, the probability of missed opportunity decreases as the noise level increases.

Statistical moments of the inter-path interference, the inter-chip interference and the intersymbol interference in a direct-sequence binary phase shift keying ultra-wide bandwidth (UWB) system are analyzed for the IEEE UWB channel models. Exact expressions for the first- and second-order moments are derived. The effects of the duration of the monocycle pulse, the spreading gain, and the number of interfering symbols on the average powers of the interferences are examined.

Maximum likelihood (ML) receivers for space-time coded multiple-input multiple-output (MIMO) systems with Gaussian channel estimation errors are proposed. Two different cases are considered. In the first case, the conditional probability density function (PDF) of the channel estimate is assumed Gaussian and known. In the second case, the joint PDF of the channel estimate and the true channel gain is assumed Gaussian and known. In addition to ML signal detection for space-time coded MIMO with ML and minimum mean-squared-error channel estimation, ML signal detection without channel estimation is also studied. Two suboptimal structures are derived. The Alamouti space-time codes are used to examine the performances of the new receivers. Simulation results show that the new receivers can reduce the gap between the conventional receiver with channel estimation errors and the receiver with perfect channel knowledge at least by half in some cases.

The performance of collaborative spectrum sensing for cognitive radio in a lognormal shadowing and Rayleigh fading channel is investigated. Unlike previous works that assume perfect knowledge of the average channel signal-to-noise ratio, this paper considers the realistic case where estimation of the average channel signal-to-noise ratio has error. Furthermore, while previous works have designed and examined estimators based on assuming that the channel samples used in the average signal-to-noise ratio estimators are noiseless, the present work assumes that the channel samples are noisy, as is the case in practical systems. Numerical results show that the probability of missed opportunity decreases as the estimation error decreases, as expected. Perhaps unexpectedly, the results also show that in the presence of noise, there exists a threshold phenomenon for the noise level. Below a particular threshold, the probability of missed opportunity increases as the noise level increases. Yet above this threshold, the probability of missed opportunity decreases as the noise level increases.

Analytical results for the symbol error rate (SER) of M-ary phase shift keying (MPSK) in a slow, flat Rayleigh fading channel for a multiple-input multiple-output (MIMO) system using an Alamouti transmission scheme and generalized selection combining (GSC) scheme are given. Two new receiver selection schemes, generalized space-time sum-of-squares (GSTSoS) selection diversity and generalized space-time sum-of-magnitudes (GSTSoM) selection diversity are proposed. The first provides the same performance as conventional GSC, and the second provides slightly poorer performance, but neither requires channel state information and both have much simpler implementations. The SER of MPSK in Rayleigh fading using these two selection schemes is studied and compared to that of conventional GSC. The effects of channel estimation errors on each selection scheme are examined.

Accurate approximations to the bit error rates of equal gain combining and maximal ratio combining diversity are obtained for pulse-amplitude-and-position-modulation operating in IEEE ultra-wide bandwidth (UWB) channel models. Numerical results are presented to show the accuracy of the approximations for the IEEE CM1 and CM3 UWB channel models. Results for pulse amplitude modulation and pulse position modulation are obtained as special cases of the more general results. Precise estimates of the loss of equal gain combining relative to maximal ratio combining are obtained for both line-of-sight CM1 and non-line-of-sight CM3 channels. Noteworthy, while the performance loss of equal gain combining with respect to maximal ratio combining is less than 0.4 dB for the non-line-of-sight channel, it can be as much as 2 dB for the line-of-sight channel.

The performances of two recently proposed receiver antenna selection schemes based on maximum sum-of- squares and maximum sum-of-amplitudes are investigated for the Alamouti space-time block coded multiple-input multiple-output system with correlated receiver antennas and M-ary signals. These schemes require neither channel estimation nor channel quality ordering. Their performances are compared with the conventional diversity selection scheme based on maximum signal- to-noise ratio, which does require channel quality information. Unlike the independent antenna case, where the performances of the new selection schemes are very close to that of the conventional scheme, the performance differences between the new schemes and the conventional scheme is significant in some cases when the receiver antennas are correlated.

Novel maximum likelihood (ML) receivers for multiple-input and multiple-output systems with imperfect channel estimation are proposed. Two different cases are considered. In the first case, the conditional probablity density function (PDF) of the channel estimate is assumed Gaussian and known. In the second case, the joint PDF of the channel estimate and the true channel gain is assumed Gaussian and known. In addition to optimal signal detection with channel estimation, optimal signal detection without channel estimation is also studied. To reduce the implementation cost of optimal receivers, two suboptimal structures are derived. As an example, the Alamouti space-time codes are used to examine the performances of the new receivers. Simulation results show that the new receivers can reduce the gap between the conventional receiver with channel estimation errors and the receiver with perfect channel knowledge at least by half in some cases.

Five new receivers for generalized transmitted- reference ultra-wide bandwidth systems are proposed. Three of these receivers are derived by optimally combining the channel estimates using the reference symbols, the data decisions and the data symbols. Two of these receivers are derived by using iterative algorithms. These receivers have very simple structures. Simulation results show that they outperform previous receivers.

Statistical properties of the four IEEE ultra-wide bandwidth channel models are derived. In particular, the correlations and probability density functions for the gains and powers of different multipath components are calculated. It is shown that most multipath components are correlated, and that most multipath components don’t follow a lognormal distribution. These results are useful in evaluating performances of ultra-wide bandwidth systems. As an application, the bit error rate of a maximal ratio combining Rake receiver is analyzed.

Three new receivers for generalized transmitted-reference ultra-wide bandwidth systems are proposed. The receivers are derived by optimally combining the channel estimates using the reference symbols, the data decisions and the data symbols. The structures are very simple. Simulation results show that they outperform previous receivers.

Infinite integrals involving the products of two, three and four Gaussian Q-functions of different arguments are solved in closed-form or in single integral form with finite upper and lower limits. These solutions provide novel expressions in the error rate analyses of signals such as general rectangular quadrature amplitude modulation, differentially encoded quaternay phase shift keying and new solutions for the error rate of the repetition code. As an example, closed-form expressions for the average symbol error rates of general rectangular quadrature amplitude modulation and the repetition code are obtained for a Nakagami-m fading channel.

Statistical moments of the inter-path interference, the inter-chip interference and the intersymbol interference in a direct-sequence binary phase shift keying ultra-wide bandwidth (UWB) system are analyzed for the IEEE UWB channel models. Exact expressions for the first- and second-order moments are derived. Using these expressions, the effects of the channel model parameters and the duration of the monocycle pulse are examined. For comparison, the first- and second-order moments of the signal component are also obtained. An approximation to the probability density function of the signal component is presented, and its accuracy is verified by calculating the bit error rate of a maximal ratio combining Rake receiver.

Optimum detectors for pilot symbol assisted modulation (PSAM) signals in Rayleigh and Rician fading channels are derived. Conventional PSAM as used on Rayleigh fading channels is also employed on Rician fading channels. It is shown that the conventional PSAM receiver is optimal for binary phase shift keying in Rayleigh fading but suboptimal for Rician fading and suboptimal for 16-ary quadrature amplitude modulation in Rayleigh fading. The optimum PSAM signal detector uses knowledge of the specular component and also jointly processes the pilot symbols and the data symbol. The performance of the optimum detector is analyzed and compared with that of the conventional detector. It is concluded that substantial gains can be achieved by exploiting knowledge of the specular component while joint processing of the data symbol with the pilot symbols may offer small benefits.

A maximum a posteriori (MAP) estimator for the Nakagami m parameter in an ultra-wide bandwidth (UWB) indoor channel is proposed. Previous work exclusively studies maximum likelihood (ML) estimation and moment method (MM) estimation of the Nakagami m parameter. This letter derives the MAP estimator for the Nakagami m parameter by using the a priori probabilities of the Nakagami fading parameters in an indoor UWB channel. The performance of the MAP estimator is examined and compared with those of the ML estimator and the MM estimator. Numerical results demonstrate that the new MAP estimator is superior to the ML estimator and the MM estimator in an indoor UWB channel, especially when the sample size in the estimation is small

The problem of estimating two measures of signal-to-noise ratio (SNR) is investigated, both for static and slowly fading channels without memory. Maximum likelihood SNR estimators that use digitally modulated signals are derived for sampled signal processing receivers as well as continuous time signal processing receivers. The performances of the estimators are examined analytically in terms of biases and root-mean-squared errors. Numerical results are presented to show their good performances

The problem of estimating the K parameter and the local average signal-to-noise ratio in a noisy Ricean fading channel is studied. Unlike most previous estimators where independent channel samples are assumed, in this paper, novel estimators that assume correlated channel samples are proposed. Both data-aided and non-data-aided designs are considered. The performances of the new estimators are examined. Several design issues are discussed. Numerical results are presented to show their good performances in a realistic Ricean fading channel

The problem of estimating the signal-to-noise ratio for time-hopping binary pulse position modulated signals, time- hopping binary phase shift keying signals, and direct-sequence binary phase shift keying signals in an ultra-wide bandwidth (UWB) system is studied. Both an additive white Gaussian noise channel and a multipath fading channel are considered. Several new estimators are derived by making use of different properties of the UWB signals. The performances of the estimators are examined and compared in terms of their root-mean-squared errors. Numerical results show that they have excellent performances when operating in an ultra-wide bandwidth system.

Signal-to-noise ratio estimators for binary phase shift keying signals in ultra-wide bandwidth (UWB) systems are proposed. Both time-hopping UWB systems and direct-sequence UWB systems are considered. Several new estimators are derived by making use of different properties of the UWB signals. The performances of the estimators are examined and compared in terms of their root-mean-squared error. Numerical results show that they have excellent performances when operating in an ultra- wide bandwidth system.

The performance of the selection diversity combiner is studied. Unlike most previous works where perfect knowledge of the signal amplitude and the noise power is assumed, in this analysis, knowledge of the signal amplitude and the noise power is obtained by using practical estimators that introduce estimation errors. The average symbol error rate of the combiner is derived for non-coherent M-ary frequency shift keying signals, independent and non-identically distributed diversity branches and unequal noise powers. The effect of estimation errors on the performance of the combiner is evaluated and illustrated by numerical examples. An interesting and useful conclusion is that it is disadvantageous to employ signal-to-noise ratio as a branch selection criterion when the branch noise powers are known a priori to be equal.

A maximum a posteriori (MAP) estimator for the Nakagami m parameter in an ultra-wide bandwidth (UWB) indoor channel is proposed. Previous work exclusively studies maximum likelihood (ML) estimation and moment method (MM) estimation of the Nakagami m parameter. This paper derives the MAP estimator for the Nakagami m parameter by using the a priori probabilities of the Nakgami fading parameters in an indoor UWB channel. The performance of the MAP estimator is examined and compared with those of the ML estimator and the MM estimator. Numerical results demonstrate that the new MAP estimator is superior to the ML estimator and the MM estimator in an indoor UWB channel, especially when the sample size in the estimation is small.

The problem of estimating the K parameter and the local average signal-to-noise ratio in a noisy Ricean fading channel is studied. Unlike previous estimators where independent or noiseless channel samples are assumed, in this paper, novel estimators are designed to operate with noisy correlated channel samples. Both data-aided and non-data-aided designs are considered. The performances of the new estimators are examined in detail. Numerical results are presented to show their good performances in a realistic Ricean fading channel.

The performance of the selection diversity combiner in slowly and flatly fading channels is studied. Unlike most previous works where perfect knowledge of the signal amplitude and the noise power is assumed, in this analysis, knowledge of the signal amplitude and the noise power is obtained by using practical estimators that introduce estimation errors. The average symbol error rate of the combiner is derived for noncoherent M-ary frequency shift keying signals, independent and non-identically distributed diversity branches and unequal noise powers. The effect of estimation errors on the performance of the combiner is evaluated and illustrated by numerical examples. An interesting and useful conclusion is that it is disadvantageous to employ signal-to-noise ratio as a branch selection criterion when the branch noise powers are known a priori to be equal

Novel diversity receivers that operate in the presence of Gaussian channel estimation errors are proposed for L independent and identically distributed fading channels. Previous work concerned with channel estimation errors has mainly examined the performance of maximal ratio combining (MRC) with estimation errors. It is shown here that MRC is not optimal when estimation errors occur. Moreover, it is shown that better diversity receivers that operate in the presence of Gaussian channel estimation errors can be obtained by using knowledge of the channel estimate statistics. Numerical results show that the derived new diversity receivers can perform as much as 2.0 dB in signal-to-noise ratio better than the conventional MRC receiver in some cases.

Optimum detectors for pilot symbol assisted modulation signals in a Rician fading channel are derived. Conventional pilot symbol assisted modulation (CPSAM) as used on Rayleigh fading channels has been employed on Rician fading channels. It is shown that the intuitive CPSAM structure is suboptimal for Rician fading. The optimum pilot symbol assisted modulation (OPSAM) signal detector uses knowledge of the specular component power and also jointly processes the pilot symbols and the data symbol. The performance of the optimum detector is analyzed and compared with that of the conventional detector. It is concluded that substantial gains can be achieved by exploiting knowledge of the specular component while joint processing of the data symbol with the pilot symbols may offer only small benefits.

The problem of estimating parameters of two main fading distributions, Ricean and Nakagami-m, is considered. Unlike most previous estimators which are designed to use samples from a noiseless channel, new estimators which use noise-corrupted samples are proposed. Both maximum-likelihood designs and moment-based designs are considered. The performances of the new estimators are examined and compared with those of previous estimators derived in the literature. Numerical results show the superiority of the new estimators for operation in noisy channels.

Optimum diversity receivers that operate in the presence of estimation errors are proposed. Previous work has mainly examined the performance of maximal ratio combining (MRC) with estimation errors. It is shown that MRC is not optimal when estimation errors occur. Moreover, it is shown that the optimum diversity receiver in the presence of estimation errors can be derived by using knowledge of the channel estimate statistics. Numerical results show that the optimum diversity receiver can perform as much as 2.0 dB in signal-to-noise ratio better than the conventional MRC receiver in some cases.

The problem of estimating the Ricean and Nakagami-m distribution parameters in noisy slowly fading channels is studied. Previous published works have mainly examined estimation based on a noiseless sample model. The predicted performances of these estimators can only be achieved by having knowledge of the values of the individual noise samples and subtracting them from the noisy signals, an impractical case. In this paper, a system model which uses samples corrupted by noise is examined. The probability density functions of noisy channel samples are derived. Novel maximum likelihood estimators as well as moment-based estimators for operation in noisy environments are developed based on these density functions. The sample means and sample root mean square errors of the estimators are determined. Numerical results show the new estimators have superior performances over estimators designed for noiseless samples in applications where noise is present.