With the development of the sixth generation wireless communication, the increasingly scarce spectrum resources limit the further increase in data rate and exacerbate the interference problem among different users and applications. To address this issue, rate-splitting multiple access (RSMA) provides a flexible framework that unifies existing orthogonal and non-orthogonal multiple access schemes. In this work, we analyze the interference scenario of RSMA-based space-terrestrial transmission systems, with multiple satellite users independently and uniformly distributed in the coverage area of the serving satellite. Specifically, the outage performance at the satellite users (resp. terrestrial base station (BS) users) is assessed while considering the interference from the BS users (resp. the satellite and the BSs of other cells). Approximate analytical expressions of the outage probability at each satellite user/BS user are derived, numerically evaluated, and verified through simulation results. The impacts of RSMA power allocation factors, fading parameters, interference severity, and satellite altitude on outage performance are thoroughly analyzed, and the trade-off between outage performance and user fairness is also illustrated.

Spectrum deception has found broad utility across multiple domains, including electronic warfare, tactical counter-measures, and adversarial sensing suppression. However, generating complex time–frequency signatures relies on sophisticated signal processing pipelines, which poses significant challenges for UAV and other IoT platforms with severely constrained onboard computational resources. Moreover, the limited onboard capability further restricts rapid signal synthesis and adaptation, failing to meet the strict rapid-response requirements of the battlefield. To address this dilemma, we propose the Fast-Tactical Signal Deception Framework (FT-SDF), a specialized generative architecture optimized for real-time signal synthesis. We formulate a novel spectro-temporal diffusion dynamics mechanism that innovatively incorporates additional spectral blurring and reverse process variance, jointly optimizing noise prediction and variance, which is necessary to preserve fine-grained spectral structures and key time–frequency signatures across different modulation schemes. Notably, to ensure strict adherence to communication protocols, we introduce a lightweight spectrum-context encoder that employs a dual-domain embedding strategy for physics-aware conditioning. Furthermore, to enable rapid inference, we develop a variance-aware acceleration mechanism that exploits learned spectral uncertainty to guide a dynamic warm-start schedule, thereby drastically compressing the sampling trajectory. Extensive evaluations on a systematically reconstructed RadioML benchmark demonstrate that FT-SDF outperforms state-of-the-art baselines. Specifically, it achieves a 59.3% reduction in sampling iterations (more than 2-fold inference speedup), while maintaining both high statistical fidelity (FID < 15) and industrial-grade precision (EVM ≤ 14%), demonstrating the feasibility of rapid, controllable generative AI in complex electromagnetic environments.

The Integrated Satellite-Ground Network (ISGN) is regarded as a promising technology for future communication systems, which can provide ubiquitous connectivity for various communication scenes, such as remote areas, maritime, and emergency communications. This paper presents a novel downlink transmission framework for the cell-free multi-input multi-output (CFmMIMO) network-based ISGN system. The satellite serves a group of earth stations through multicast technology, while the ground network embraces the cell-free network strategy to serve multiple terrestrial users. Furthermore, the CFmMIMO-based ISGN under consideration operates at millimeter-wave frequencies to satisfy the high data rate demand of the next-generation communication system. In order to enhance the spectral efficiency of the proposed CFmMIMO-based ISGN system, we formulate two joint beamforming design problems, which are denoted as the maximize sum-rate (MSR) problem and the max-min fairness (MMF) problem. Then, to solve this mathematically intractable problem, we initially applied Fractional programming, Alternating optimization, and successive convex approximation to convert the MSR-based joint precoding problem into an equivalent quadratically constrained quadratic program (QCQP) problem. After that, the QCQP optimization problem is solved using an alternating direction method of multipliers based algorithm, where we dramatically simplify the algorithm’s computational complexity with several straightforward variable substitutions. Furthermore, we extend our proposed algorithm to address the MMF-based joint beamforming design problem. At the last, the simulation result demonstrates the superiority of the proposed downlink transmission framework as well as the proposed algorithm.

The Non-Terrestrial Network (NTN) is a promising technology to achieve the ubiquitous, low latency, and high data rate next-generation communication system, however, since the broad application of the NTN, the security problem in the NTN is of paramount importance. In this regard, this paper investigates a novel secure communication strategy within the NTN, where a legitimate user of the LEO satellite-integrated unmanned aerial vehicles network (ISUAVN) is equipped with a high-gain antenna and attempts to cross-layer eavesdrop on the GEO satellite network.In the considered NTN system, the cell-free massive multi-input multi-output (CFmMIMO) network is utilized at the ISUAVN to enhance the spectral efficiency of the terrestrial users. Furthermore, the ISUAVN shares the millimeter wave spectrum with the GEO satellite multicast downlink communication link. We first formulated a multi-objective optimization (MOO) based joint beamforming design problem for the secure communication of the considered CFmMIMO NTN system to achieve a Pareto optimal trade-off between two conflicting and essential objectives: minimizing total transmitted power and maximizing the secret data rate of the satellite network. Thereafter, to address this mathematically complicated optimization problem, we utilized successive convex approximation methods to transform this intractable problem into an equivalent convex optimization problem. Then, the commercial optimization package is employed to achieve the optimal solution of the MOO problem, commencing from a feasible point that is identified by a semi-definite programming initialization algorithm. Ultimately, the numerical simulation results demonstrated that the Pareto optimal trade-offs for the formulated MOO problem were achieved using the proposed algorithm, and the effectiveness of our proposed algorithm was shown through comparisons with existing related methods.

This paper investigates the joint transmit and receive beamforming design for a bistatic integrated sensing and communication (ISAC) system, where a transmit base station (BS) and a receive BS are coordinated to simultaneously serve multiple downlink and uplink users as well as estimate target positions. The closed-form expression for the Cramér-Rao bound (CRB) for target positions and reflection coefficients is derived and minimized subject to constraints of the transmit power budget and communication requirements. To address the considered problem, the closed-form expression for the optimal receive beamforming vectors is derived, facilitating the development of a successive convex approximation (SCA)-based algorithm for optimizing the transmit information beamforming vectors and sensing covariance matrix. In addition, a learning-based approach named ISACNet, trained in an unsupervised manner, is proposed to handle the considered problem. The ISACNet incorporates multi-head self-attention and cross-attention mechanisms to significantly enhance its expressive capability. Simulations validate the effectiveness of the proposed SCA-based algorithm and ISACNet. It is observed that the sensing performance is predominantly affected by the downlink communication more than the uplink communication. Furthermore, our ISACNet generates an effective solution in millisecond-level response times with only a marginal performance degradation compared to the SCA-based algorithm.

In this work, we explore energy-efficient covert underwater acoustic backscatter communications (EC-UABCom) within the framework of the Internet of Underwater Things (IoUT). Specifically, a passive buoy node covertly transmits acoustic information passively to a maritime receiver by reflecting incident acoustic carrier signals from an autonomous underwater vehicle (AUV) transmitter. Simultaneously, the system exploits the uncertainty of underwater noise to mask the covert backscatter information, thereby evading detection by a submarine warden. To optimize the covert strategy, we first derive the warden’s optimal power-detection threshold that minimizes the detection error probability, accounting for underwater noise uncertainty. In response to this optimal detection strategy, we propose an energy-efficient covert policy by jointly optimizing the AUV’s transmit power and the buoy’s reflection coefficient to meet the covertness constraint. We then conduct a performance analysis, providing a closed-form expression for the expected detection error probability at the warden and outage probability at the receiver, thereby revealing their inherent trade-off. Numerical simulations validate the effectiveness of our approach compared to the state-of-the-art methods, demonstrating that higher transmit power and reflection coefficient degrade covertness while improving covert rate performance.

With the rapid development of integrated air-space-ground networks, ensuring secure and covert access for unmanned aerial vehicles (UAVs) to low Earth orbit (LEO) satellite constellations has become a critical challenge. This paper proposes the novel adaptive redundancy contention resolution diversity slotted ALOHA (AR-CRDSA) scheme to provide reliable and covert communication for UAV users. The core of the scheme involves a dynamic pairing strategy that conceals a low-power covert user (CU) transmission under the signal of a high-power legitimate user (LU), thereby shielding it from detection by adversarial reconnaissance satellites. A key innovation is the departure from transmitting identical packet replicas; instead, the AR-CRDSA protocol sends two distinct yet complementary packet versions to adapt to fluctuating channel conditions. This adaptive mechanism, combined with multi-satellite selection diversity and an iterative interference cancellation process, is shown through extensive simulations to significantly enhance the effective covert throughput and overall system reliability when compared to traditional random access protocols.

In recent years, low Earth orbit (LEO) satellite communication has emerged as a focal area of extensive research due to its potential for global broadband connectivity. Multi-carrier direct sequence spread spectrum (MC-DSSS) technology, leveraging the inherent anti-jamming advantages of spread spectrum signals, has shown great promise in enhancing communication reliability. However, the acquisition of MC-DSSS signals in LEO transmission link presents significant challenges, primarily due to the coexistence of extremely low signal-to-noise ratio (SNR) and substantial Doppler effect.To address these issues, this paper proposes an optimal two-dimensional (2D) acquisition framework for MC-DSSS signals. Based on the maximum likelihood (ML) criterion, the proposed framework enables coherent combination of subcarriers with high time resolution, thereby improving the acquisition performance in harsh LEO environments. Furthermore, a two-step low-complexity coherent acquisition algorithm is developed. This algorithm significantly reduces the computational burden while maintaining the performance of fully coherent subcarrier combination, making it more suitable for real-time implementation in resource-constrained LEO communication terminals. Moreover, this paper derives closed-form solutions for the false alarm probability, detection probability, and mean squared error (MSE) in additive white Gaussian noise (AWGN) channel, which are validated through simulations. The results demonstrate that the proposed algorithm achieves 2 dB performance improvement in SNR compared to noncoherent combining method, with a 1024-fold reduction in MSE when the number of subcarriers is 16.

Diverse and complex wireless interference is one of the most critical threats to modern wireless communication systems. The continuous shift of wireless interference toward higher frequency and wider bandwidth imposes sampling-rate limitations on wireless interference identification (WII). Existing compressed sensing-based WII methods designed for undersampling scenarios suffer from high-complexity signal reconstruction and low identification accuracy caused by fixed sampling strategies. To address these challenges, we propose a multi-domain fusion model which employs domain-specific hybrid sampling to extract interference features for multi-attribute WII without signal reconstruction. Given the respective classification advantages of random and uniform undersampling in time-domain sequences and time–frequency images, this paper proposes a dual-branch architecture to exploit their joint benefits. Specifically, we propose a learnable sparse sampler combined with a Transformer to extract time-domain features in the random undersampling branch. We further prove that the Restricted Isometry Property (RIP)-compliant undersampling largely preserves the feature class discriminability, which motivates the introduction of the RIP loss. In parallel, we propose a multi-scale feature extraction module and a cross-fusion module for dimensionality reduction in the uniform undersampling branch. Subsequently, an attribute correlation-driven graph convolution network is introduced to classify the fused features from both branches, further improving WII performance. Finally, we propose an adaptive multi-domain binary cross-entropy loss and prove that the optimal weight of the RIP loss effectively mitigates gradient conflicts. Experimental results show that the proposed model improves precision by 25.1% over the state-of-the-art, while maintaining effective multi-attribute WII performance at undersampling ratios up to 8.

With the rapid development of global satellite Internet and the growing demand for seamless connectivity, the Satellite–Terrestrial Integrated Network (STIN) has become a key architecture for achieving ubiquitous communication coverage. However, STIN faces critical energy efficiency challenges, including the high peak-to-average power ratio (PAPR) that degrades high power amplifier efficiency at the physical layer and frequent satellite handovers that increase network overhead. To address these issues, this paper proposes a mobility-aware model of Multicarrier Direct-Sequence Spread Spectrum (MC-DSSS) STIN. At the physical layer, the Orthogonality-Based Generalized Multicarrier Constant Envelope Multiplexing (CEMIC) technique is adopted, and an energy efficiency maximization problem is formulated under constant-envelope constraints. A joint power allocation algorithm is developed based on the Dinkelbach method and the Alternating Direction Method of Multipliers (ADMM) to solve the non-convex problem efficiently. To overcome the limitations of single-layer optimization, a twolayer collaborative framework is further proposed. At the network layer, an improved binary particle swarm optimization (IBPSO-HO) algorithm is employed to optimize satellite handovers. This joint design enables two-layer energy efficiency optimization. Simulation results demonstrate that the proposed scheme significantly enhances overall energy efficiency across both layers, providing robust theoretical support for the large-scale green deployment of STIN.

Inter-satellite links (ISLs) are pivotal for information interaction and orbit determination in mega-constellation networks. Terahertz (THz) band, with its abundant spectral resources, exhibits significant potential for high-speed ISL establishment. However, due to frequent operations of Low earth orbit (LEO) satellites, the combined impacts of multi-source timevarying platform vibrations and two-dimensional asymmetrical features of each source on THz inter-satellite communication-ranging systems have yet to be revealed. This paper proposes a vibration-constrained THz-ISL system with LEO satellite attitude dynamics and spatial effects. Specifically, we establish a multi-peak frequency model using Semiconductor Inter-Satellite Link Experiment (SILEX) spectra, complemented by an optimized sum-of-sinusoids (SOS) time-domain model to address high dynamics and multi-source vibrations in LEO scenarios. Statistical link gain models are formulated, encompassing steady, sporadic, and universal scenarios. Subsequently, the proposed ISL gain model is applied to assess communication and ranging performance. We derive the signal-to-noise ratio (SNR)-vibration quantitative relationship through Stirling’s approximation, where SNR quantifies communication performance degradation. Ranging precision is evaluated via the Fisher information matrix (FIM), revealing vibration deterioration of Cram´er-Rao bounds (CRB). Under given power constraints, we define truncation distance as the maximum vibration-tolerant link range where performance remains unaffected, and derive its closed-form solution. Simulations utilizing SILEX orbit data indicate that under typical settings: 1) inter-satellite vibrations are on the order of hundreds of μrad and cause a 5% pointing error attenuation, 2) vibrations alter system performance boundaries, with 600 km communication distance reduction and 2.2 dB ranging accuracy degradation with 0.03° deviation, 3) vibration effects can be mitigated via phased array dimensions and beamforming design, achieving capacity-robustness trade-off.

With the proliferation of software-defined radio technology, malicious jamming attacks against wireless communications have become more aggressive and flexible, which could easily create a complex and highly dynamic jamming environment by varying both the jamming parameters and the jamming policies. Such a complex jamming environment makes it challenging for most of deep reinforcement learning (DRL) based anti-jamming schemes in rapidly identifying effective strategies. In this paper, we have developed a dual-tier policy-oriented anti-jamming (DPA) scheme based on DRL to facilitate swift adaptation to the complex jamming environment. Unlike existing works, an upper-tier jamming pattern recognition (JPR) network is introduced to extract underlying jamming policy-related information which serves as a guidance for the lower-tier deep recurrent Q-network on anti-jamming decision-making. The output of the JPR network can enable the sharing of experiences among various jamming patterns originated from the same jamming policy and facilitate more efficient and targeted anti-jamming strategic learning. Extensive experimental results demonstrate that the superiority of our DPA scheme over other DRL-based benchmark schemes in terms of both anti-jamming performance and convergence speed.

The implementation of deep learning-based automatic modulation recognition (AMR) on resource-constrained edge devices calls for efficient networks. Unfortunately, existing AMR networks fail to balance parameter scale, inference speed, and recognition accuracy, which hinders their edge applications. In view of this, we propose an expert-assistant network for efficient AMR with small parameter scale and inference time. To exploit the advantages of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for parameter scale reduction, we build a lightweight AMR network with a CNN-RNN hybrid architecture. Given the speed-and-accuracy dilemma faced by existing CNN-RNN hybrid networks, we introduce an small-scale plug-in, called the assistants, as well as a temporal shuffling scheme to enable fast and accurate AMR with small parameter scales. Besides, techniques like non-recurrent dropout for the gated recurrent unit (GRU) layer, parameter estimator and transformer (PET) and model pruning are applied to further enhance the performance of the networks. Extensive experimental results demonstrate that the proposed Expert-Assistant (E-A) network achieves the best comprehensive performance on lightweight, computational efficiency and recognition accuracy. Our model performs especially well with extremely low parameters, which achieves an average accuracy near 60% and a highest accuracy near 90% with just 3.5K non-zero parameters on RML2016.10a.

Low Earth Orbit (LEO) satellite systems with sensing capabilities are widely regarded as promoting reliable and efficient communication services globally. This paper proposes a Multi-Satellite Collaborative Security System with Integrated Sensing and Communication (ISAC-MSC). Considering the potential benefits of LEO satellites and ISAC, we exploit sensing performance in the MSC system by jointly optimizing LEO satellite assignments, communication Beamforming (BF) vectors, and sensing BF vectors. Specifically, we design improved Continuous Particle Swarm (CP) optimization and Discrete Particle Swarm (DP) optimization algorithms to maximize the target sensing Signal-to-Noise Ratio (SNR) for LEO satellite assignments. Additionally, with respect to the BF vector optimization, we develop Power Approximation (PA) optimization algorithm, Inner Approximation (IA) optimization algorithm, and Joint Sensing and Communication BF (JSC-BF) optimization algorithm. Multiple algorithms are tightly integrated and alternately iterated. Numerical results show that: 1) the JSC-BF algorithms outperform the PA and IA algorithms in terms of sensing performance and communication secrecy rate; 2) compared to the single satellite case, the ISAC-MSC system performance approximately linear growth, and has strong extensibility; 3) with imperfect MSC synchronization case, the communication secrecy rate appears inflection point and stabilization, but the JSC-BF algorithms still have excellent performance.

This paper studies the beamforming design for jamming assisted multi-user covert communications under the surveillance of an eavesdropper. Different from existing work, the impact of multi-user interference as well as the tradeoff between the jamming and the confidential signal power allocation are taken into consideration. To degrade the eavesdropper's signal detection capability, the power of the jamming signal is often randomized, which introduces uncertainty to the achievable transmission rate. In light of this, we propose to carry out the beamforming design with the concept of quantile transmission rate (QTR) so that the expected covert transmission rate can be achieved with high probability. With QTR, the fairness aware multi-user beamforming design problem is formulated to maximize the minimum QTR among all legitimate users while ensuring the covertness constraints are satisfied. Given the nonconvexity of the constraints, we propose a successive convex approximation (SCA)-based iterative algorithm for effective solution finding. The validity of the obtained covert beamforming scheme is evaluated through various experiments.

Human-centered wireless sensing has been significantly advanced by artificial intelligence (AI) technologies. To enhance AI model performance, signal preprocessing, as a fundamental procedure, is widely employed for improving signal quality. However, existing methods are time-consuming, labor-intensive, and exhibit limited generalization. To address this issue, we first investigate the frequency spectrum of various signals. The results demonstrate that, in human-centered wireless applications, human activities significantly affect the low-frequency components in the signal spectrum. Motivated by this observation, we propose Sense+, a concise and versatile signal preprocessing module that can be seamlessly integrated into existing models to enhance sensing performance. Specifically, we transform the raw signals into a unified frequency domain, apply a learnable filter to process their frequency spectra, and then convert them back to the original signal domain. To accurately extract low-frequency features, we further propose a low-pass weight initialization method for the filter. Extensive experiments are conducted across various sensing tasks and signal types, including IR-UWB signals for person identification, mmWave radar signals for gesture recognition, and Wi-Fi signals for action recognition. The results highlight the effectiveness of Sense+ in enabling preprocessing across diverse wireless signals. Specifically, when equipped with Sense+, the average accuracy improves by 21.84% compared to conventional preprocessing methods. Additionally, Sense+ accelerates convergence and exhibits consistent generalization across different neural network models.

Terahertz (THz) and Non-Orthogonal Multiple Access (NOMA) technologies have illustrated a great potential in the use of large-scale Internet of Things (IoT) applications. However, in most applications, ground sensors (GSs) have poor energy capacity, while THz communication suffers from significant path loss, which leads to a low data transfer rate in air-ground communication system. Fortunately, with the development of unmanned aerial vehicle (UAV) assisted THz-NOMA communication technology, it becomes one of the promising solutions to deal with above challenges. To conserve GSs’ limited energy while enhancing the throughput of THz-NOMA communication system, we utilize a UAV to collect and transmit data from GSs to an aerostat. Then, energy efficiency (EE) of GSs network is maximized by leveraging GSs’ communication resource allocation strategy and UAV trajectory design, while taking into account each GS’s minimum throughput and each GS’s transmission power constraints. The proposed design is a mixed integer non-convex problem, which is generally intractable. To promptly solve the original problem, we divide it into three subproblems: GSs’ communication resource allocation, UAV’s altitude optimization, and horizontal trajectory design. An iterative algorithm is proposed to deal with these subproblems, based on Dinkelbach method, dual decomposition, and convex relaxation techniques. Simulation results show that the proposed iterative algorithm can achieve a higher EE with a shorter convergence time compared with baseline algorithms.

The rapid development of the Internet of Things (IoT) has led to exponential growth in wireless network traffic and the number of connected devices, thereby intensifying the demand for scarce spectrum resources. In this context, Wireless signal identification, a key technology in spectrum sensing, is crucial for enhancing spectrum utilization by mitigating interference and ensuring system security. In this study, we treat wireless signal identification as a time series classification task and propose a novel model based on Fourier-segmented attention. In our proposed model, instead of computing point-level attention, we extract sequence dependencies by computing segment-level attention. Moreover, we introduce a method based on the Fourier transform to determine the segment length, ensuring that each segment captures multi-scale features. Experimental results indicate that the proposed method outperforms existing models, achieving an accuracy of approximately 95% on our dataset and representing an improvement of around 1.6% in accuracy over competing approaches. Furthermore, experiments were conducted to evaluate the model’s effectiveness in detecting fake signals and its potential to enhance system security.

Target speaker extraction (TSE) aims to isolate a specific speaker’s voice from multi-talker environments using enrollment data. While current approaches primarily utilize speaker embeddings from enrollment, they often neglect contextual information and the dynamic interactions between the mixture and enrollment. To address this limitation, we propose a novel DualStream Contextual Fusion Network (DCF-Net) that operates in the time-frequency (T-F) domain. Our framework introduces a DualStream Fusion Block (DSFB) that: 1) captures contextual information, 2) models interactions between contextualized enrollment and mixture representations across spatial and channel dimensions, and 3) employs these enriched representations to guide the extraction process. Comprehensive experiments show that DCF-Net achieves state-of-the-art (SOTA) performance with a 21.6 dB improvement in scale-invariant signal-to-distortion ratio (SI-SDR) on benchmark datasets while demonstrating robustness in noisy and reverberant conditions. Notably, our model significantly reduces the wrong extraction rate to just 0.4% when testing on target confusion problem (TCP), underscoring its practical applicability.

Satellite communication faces a great risk of eavesdropping due to the its broadcasting nature and vast coverage. Conventional beamforming-based physical-layer security technology encounters difficulty in preventing eavesdropping within the main-lobe of the satellite, which causes a large insecure area. In this paper, we propose a cooperative multi-beam transmission framework, which elaborately superimposes multiple beams to shrink the intersected coverage area, and exploits the additional design degrees of freedom to enhance the security. The designs of symbol mapping and beamforming are jointly studied at multiple beams to achieve transparent communication in the intersected area while randomizing the signals otherwise. Information-theoretic performance metric is identified to characterize the information leakage. A tight upper bound of the intractable metric is proved using variational methods, and a deep learning-based approximation and optimization scheme is then proposed to efficiently design the multi-beam transmit signals. To further distort the leaked signal, a convergence-guaranteed cross-entropy algorithm is developed for joint antenna subset selection at multiple beams. Simulations show that the proposed scheme can shrink the insecure area by an order of magnitude compared with the conventional methods.

In this paper, we study uplink covert communication in a space-air system, where an unmanned aerial vehicle (UAV) transmits sensitive data to a Geosynchronous Earth Orbit (GEO) satellite while preventing the transmission action from being discovered by a warden. We derive the optimal decision threshold of the warden. We investigate the 3-dimensional (3D) beamformer and 3D trajectory design for the transmitter UAV against this optimum warden to maximize the covert transmission rate in the presence of imperfect channel state information and uncertain noise. Due to the non-convex structure and dependence between beamforming vectors and locations of the transmitter UAV, we develop a decoupling method that specifies a feasible flight region of the transmitter UAV at each time slot, enabling the decomposition of the original optimization problem into two sub-problems that optimize the trajectory and beamforming vectors individually. We design an iterative algorithm with a new initialization method to solve the sub-problems alternately with the semi-definite relaxation (SDR) and the successive convex approximation (SCA) technique. Numerical results show that the average covert rate of our design approaches the ideal case without the warden and increases by about 102.3% and 19.1% compared with benchmark schemes that do not employ beamforming or design 2D trajectory, respectively.

This paper studies the fundamental limits of covert communications covered by randomly activated overt users in both single-frame and multi-frame transmission scenarios. While traditional covert communications mainly consider concealing signal power characteristics, the existence of overt users provides opportunities such that covert communications can be achieved through the confusion between the users. This benefit is first revealed in single-frame transmission scenario. The major obstacle in analyzing performance limits is that the conventional Kullback-Leibler divergence based covertness measurement becomes infinite. To overcome the intractability, a tighter upper bound of the total variation distance (TVD) is then developed using a novel recursive-iterative approximation. On this basis, the collapse effect of the TVD is derived, which shows that the TVD is strictly less than 1 if the covert user sets the transmit power to be an integer multiple of that of the overt users. Then, we find that$\mathcal {O}(N)$-bit information can be transmitted over N channel uses under the above setting, which breaks the well-known square root law. If the above setting is violated, the TVD instantly approaches 1 as$N\rightarrow \infty $, and only$\mathcal {O}(\sqrt {N})$-bit information can be covertly transmitted. To prove this, the detection method of the warden is modified to cope with the random activation of overt users. These conclusions also hold for the transmission with uncertain powers or in fading channels, which resembles realistic wireless transmissions. In multi-frame transmission scenario, however, the access characteristics of overt users can be exposed from a statistical perspective, such that the rate gain disappears and the covert transmission rate drops to$\mathcal {O}(\sqrt {N})$bits per frame. To obtain a positive covert transmission rate, we propose a rate-splitting based covert transmission scheme that introduces an opportunistic access branch to bring randomness, through which the covert user can transmit up to$\mathcal {O}(NL)$-bit information over L frames.

With the increasing demands of the Industrial Internet of Things (IIoT), highly dynamic multi-unmanned aerial vehicle (UAV) networks are becoming indispensable to IIoT due to their flexibility, cost-effectiveness, robust safety measures, and real-time data collection capabilities. Accurate time synchronization is crucial for coordinated missions of multi-UAV networks, yet the time-varying nature of clock parameters and the rapid movements of UAVs pose significant challenges to achieving precise synchronization. This article introduces new state and observation models for clock and velocity parameters and proposes a Doppler and timestamp-based distributed algorithm for tracking clock parameters using the Kalman filter. To evaluate the performance of the proposed algorithm, we derive the Bayesian Cramér–Rao lower bound and conduct numerical simulations. The results of the simulations demonstrate that our algorithm surpasses existing methods in terms of accuracy in tracking clock parameters.

Concerns surrounding privacy leakages caused by prevalent vision-based person identifications are countless. A promising privacy-preserving solution is to identify the wireless signals reflecting persons, which, however, faces a major challenge of losing efficacy in multi-environments. In this paper, we work on person identification based on wireless signals using transfer learning, toward tackling the performance deterioration across environments. We investigate the feature variations induced by environmental shifts based on data measurements. Lay our foundation on the feature alignment concept, we propose a novel wireless-based person identification framework using transfer learning. In the framework, we integrate a series of signal processing methods including signal selection, pre-processing, and augmentation, where the first includes a reference environment to assist the feature extraction while the latter two respectively reduce the data noise and improve the data diversity. We also propose a model generalization method where a neural network is employed to align features from different environments, which facilitates the extraction of environment-independent features while incorporating both person and environment information. On a real wireless testbed consisting of an Impulse Radio Ultra-WideBand (IR-UWB) radar, we build and publicly release a dataset with 22,264 samples of ten individuals from three environments, varying in testing distance and obstruction condition. Extensive experimental evaluations demonstrate that the proposed framework can improve the identification accuracy across environments, and surpasses state-of-the-art methods by up to 18.06%.

Space-air-ground integration has become paramount in the next generation of wireless communication systems in the era marked by the seamless integration of terrestrial and celestial domains. On the other hand, the age of information (AoI) has recently emerged as a vital metric for evaluating the timeliness and freshness of data in these multi-hop communication systems. This paper focuses on investigating the information freshness of multi-hop satellite IoT systems while considering several automatic repeat request (ARQ) and hybrid ARQ (HARQ) schemes over different hops to promise the reliability of data transmissions. Specifically, a group of remote sensors transmits their data to a terrestrial base station (B) via the code-division multiple access (CDMA) strategy to exploit CDMA’s natural merits, e.g., anti-jamming and simultaneous transmissions. Then, B sends these received data to a data destination (D) via a satellite (R) under the transparent forwarding strategy. We derive the closed form of the outage probability for the CDMA protocol considering multi-user interference (MUI) and the closed form of the end-to-end outage probability for the three kinds of ARQ and HARQ schemes on the dual-hopB-R-Dtransmission, and then finally derive the expression of the corresponding AoI. Finally, numerical results show that simulations match well with the theoretical results, proving the analysis’s correctness and the pros and cons of the different ARQ/HARQ schemes.

Assessing the vulnerability of mega-constellation networks (MCNs) to large-scale failure is challenging, due to the time-varying three-dimensional constellation. In this paper, we propose a geometrical method to assess the vulnerability of the MCNs under large-scale geographical failure based on the standard +Gridinter-satellite connectivity pattern, using hop count as the metric. We first equivalently map the original constellation to a two-dimensional flat torus, offering a simplified and time-invariant representation of the geographical failure. The failure’s properties are then revealed on the torus to identify the blockage on the inter-satellite paths that might increase the hop count between end users. Under the blockage, a closed-form hop count expression is then obtained by deriving the explicit expressions of the optimal detours in the scaling limit. Finally, we propose a low-complexity hop count estimation algorithm that achieves a maximum relative error of 1.5% compared with the network simulation while being$10^{5}$times faster. Evaluations using traffic sourced from the 100 most populous cities show that the geographical failure covering the latitude corresponding to the MCNs’ inclination has the greatest impact on the hop count, whereas the MCNs are generally robust even under extreme scenarios.

Given the time-varying radio environments, legitimate users need to conduct covert communications over multiple time slots with different radio propagation conditions, while a warden performs joint signal detection based on the observations collected during these slots. Unfortunately, existing studies mainly deal with the design of covert communication scheme within a single slot where the radio environment remains unchanged. These scheme might suffer performance degradation when legitimate users make transmission decisions over multiple slots with different propagation conditions and a joint covertness constraint over these slots is imposed. In view of this challenge, we investigate the design of covert communication schemes in time-varying radio environments under a multi-slot covertness constraint. Given the coupling between the transmission decisions in different slots, we formulate the schematic design as a Markov decision process where the multi-slot covertness constraint is characterized with the concept of conditional value at risk to address the non-cumulative growth brought by the unknown eavesdropping channel. Unlike existing works, our scheme allows the legitimate users to adapt their transmission decisions to the current propagation condition and covertness margin. Extensive simulations demonstrate that our scheme can facilitate efficient covert data transmission under a multi-slot covertness constraint.

This paper investigates beamforming design with partial channel state information (CSI) feedback aimed at maximizing the energy efficiency (EE) of a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user system. By leveraging the spatial reciprocity, which means that the path angle information (PAI) and power angular spectrum (PAS) of sparse paths are similar in the uplink and downlink channels, we can acquire the downlink PAI and PAS at the base station (BS) via uplink estimation. Subsequently, we select several paths that contribute to EE maximization from all the cascaded paths and define them as dominant paths. Consequently, only the small-scale fading coefficients (i.e., the normalized path gain information, NPGI) of these selected dominant paths need to be fed back from the user equipments (UEs), thereby significantly reducing the feedback overhead. Moreover, we update the active BS beamformer and passive RIS beamformer by using the feedback of partial NPGI to further improve the EE. Numerical results demonstrate the superiority of our proposed algorithms over conventional schemes.

Low Earth orbit (LEO) Mega-constellations have emerged as a transformative approach to realize enhanced system capacity and improved coverage to satisfy the ever-increasing global demand for data services. Subsequently, the high density of satellites in a confined orbital region poses challenges, including potential interference among neighboring satellites. Further, it is vital to adequately address the impacts of safety distances in satellite communication systems on ensuring proper operation, collision avoidance, and interference management. Inspired by these observations, this work proposes a novel analysis tool, the Poisson arc hardcore point process (PAHPP), by extending the traditional Poisson line hardcore point process to characterize the unique orbiting properties of the satellites in LEO mega-constellations, accounting for factors such as the orbit, the satellite density, and spatial distribution. Specifically, this paper presents the PAHPP by enforcing a minimum separation between satellites operating in the same circular orbit to reflect the practical LEO mega-constellations. The imposed minimum inter-satellite separation in the proposed PAHPP model has also been applied to multi-orbit multi-satellite communication cases. Moreover, the discretization approximation technique is employed to analyze system performance, focusing on serving distance and outage probability. Numerical results provide valuable insights and conclusions for uncovering and recognizing LEO mega-constellations.

Ambient OFDM backscatter communication has attracted considerable research efforts. Yet the prior works focus on point-to-point backscatter from a single tag, leaving behind efficient backscatter networking of multiple tags. In this paper, we design and implement ConcurScatter, the first ambient OFDM backscatter system that scales to concurrent transmission of hundreds of tags. Our key innovation is building and using the subcarrier pattern diversity to distinguish concurrent tags. This would yield linear collision states rather than exponential ones in the prior works based on the IQ domain diversity, supporting more concurrent transmission. We concrete this by designing a suit of techniques including midair frequency synthesis that forms a unique subcarrier pattern for each concurrent tag, non-integer cyclic shift that contributes to support more concurrent tags, and subcarrier pattern reconstruction that creates virtual subcarriers to enable single-symbol parallel decoding. The testbed experiment confirms that ConcurScatter supports seven more concurrent tags with similar BER and 8.4× higher throughput than the point-to-point backscatter RapidRider. The large-scale simulation shows that ConcurScatter supports 200 tags which is 40× more than the state-of-the-art concurrent OFDM backscatter FreeCollision.

The emerging Orthogonal Time Frequency Space (OTFS) technique can achieve stable communication in high-speed mobile scenarios, which combining with multiple-input multiple-output (MIMO) can improve the spectrum and energy efficiency of wireless communication systems. To achieve potential performance gains of OTFS-MIMO systems, this paper considers the precoding design for wideband OTFS-MIMO systems with beam squint effect. We propose a novel precoding structure with compensation modules for eliminating the influence of beam squint and further improving the achievable rate. The simulation results prove that the achievable rate of the wideband OTFS-MIMO system with beam squint has been significantly improved under our proposed scheme.

In this article, we investigate the analog–digital hybrid transceiver optimization for multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems, aiming at maximizing the sum rate of multiple IoT devices in downlink communications. We first derive the downlink–uplink duality for the MIMO communications with analog–digital hybrid structures. Based on this, the intractable MIMO downlink sum-rate maximization is equivalently transferred into an easier-to-handle virtual uplink counterpart. In order to solve the nonconvex virtual uplink problem effectively, we resort to decouple the involved digital and analog matrix variables. On the one hand, we propose two kinds of algorithms for the analog matrices optimizations, namely, the joint design and the separate design. Specifically, the joint design optimizes the analog precoder and equalizer matrices in an alternating manner. In each iteration, an element-wise optimization algorithm is utilized to optimize the analog matrix variables under constant modulus constraints. For the separate design, the analog precoder and equalizer matrices are optimized separately via the elaborately designed space alignments with lower computational complexities. On the other hand, the digital precoders can be computed with fixed analog matrix variables, in which a modified iterative water-filling algorithm is proposed. Finally, numerical results demonstrate the superior performance advantages of the proposed algorithms over several benchmark algorithms.

This article presents a novel noncoherent multisatellite weak signal acquisition scheme by aggregating the observations at multiple low-Earth orbit (LEO) satellites. Existing aggregation schemes rely on exhaustive search over grids on Earth surface to compensate for the differences in the delay and the Doppler frequency shift experienced at different satellites, which have high-computational complexity due to the wide coverage of LEO satellites. Motivated by this observation, we propose to directly work with satellites’ time-frequency (TF) grid and facilitate efficient delay and Doppler compensation by gradually narrowing down the search space over each satellite’s TF grid with multisatellites observations. Our scheme employs geometric search space reduction scheme to reduce the search space for possible Doppler frequency shift and utilizes hierarchical geometric correlation peak matching to eliminate fake correlation peaks based on multisatellite observations. Through extensive performance evaluation, we demonstrate that, in comparison with existing schemes, our proposed multisatellite signal acquisition scheme can achieves significantly better acquisition performance with a much lower computational complexity.

The integrated satellite-terrestrial network (ISTN) system has experienced significant growth, offering seamless communication services in remote areas with limited terrestrial infrastructure. However, designing a routing scheme for ISTN is exceedingly difficult, primarily due to the heightened complexity resulting from the inclusion of additional ground stations, along with the requirement to satisfy various constraints related to satellite service quality. To address these challenges, we study packet routing with ground stations and satellites working jointly to transmit packets, while prioritizing fast communication and meeting energy efficiency and packet loss requirements. Specifically, we formulate the problem of packet routing with constraints as a max-min problem using the Lagrange method. Then we propose a novel constrained Multi-Agent reinforcement learning (MARL) dynamic routing algorithm named CMADR, which efficiently balances objective improvement and constraint satisfaction during the updating of policy and Lagrange multipliers. Finally, we conduct extensive experiments and an ablation study using the OneWeb and Telesat mega-constellations. Results demonstrate that CMADR reduces the packet delay by a minimum of 21% and 15%, while meeting stringent energy consumption and packet loss rate constraints, outperforming several baseline algorithms.

In this study, a joint blind frame synchronization and decoding method is proposed based on the normalized syndrome satisfaction probability (NSSP) provided by the soft Low-Density Parity-Check (LDPC) codes decoder. To reduce complexity, a stopping criterion is introduced into the iterative process through the convergence of NSSP evolution patterns. In addition to achieving an excellent synchronization performance, this method also eliminates the redundancy caused by the pilot sequence. Furthermore, it is compared with the optimal pilot-based and other code-aided frame synchronization algorithms. According to analytical and simulation results, the proposed technique outperforms other advanced code-aided frame synchronization solutions in synchronization, which makes it applicable to achieve comparable performance to the pilot-based methods in specific coding gains. Due to the introduction of a novel stopping criterion, the average number of joint detection & decoding is reduced by up to 90%. Analog probability processing technology integrates sub-threshold region circuits with probability domain-based iterative message-passing algorithms inspired by high energy efficiency and low complexity. Hence, hardware implementation is presented based on the analog LDPC decoder chip as fabricated in a 0.35-μm CMOS technology for our proposed algorithms. The experimental results demonstrate the effectiveness of the proposed method and the realization loss is within 0.2 dB compared with the theoretical circuit simulation results.

Reconfigurable intelligent surface (RIS) has evolved into a promising approach to significantly improve both spectral and energy efficiencies of wireless communications. Different from the traditional fully-passive and fully-active RISs, a novel hybrid RIS composed of both active and passive reflecting elements has recently emerged, which can leverage their combined advantages to effectively mitigate the RIS-induced multiplicative path loss. In this paper, we investigate a hybrid active-passive RIS assisted wireless system from a rate-maximization perspective. Specifically, we firstly consider the multi-antenna multi-user system and aim to maximize the system weighted sum rate (WSR) by jointly optimizing the transmit precoding matrices and the active-passive RIS reflection matrix. The optimal semi-closed-form solution to each subproblem is obtained by jointly exploring the activeness of constraints and leveraging the majorization-minimization (MM) technique. To gain more useful insights into the rate maximization, we also study the special single-antenna single-user scenario, in which it is revealed that both the optimal transmit beamsteering direction and the optimal phase shifts at the hybrid RIS are independent of actual reflection amplitudes of the hybrid RIS. Numerical results demonstrate the lower complexity and superior rate performance of our proposed algorithms as compared to the existing schemes adopting the fully-passive RIS. Moreover, it is revealed that the hybrid RIS can strike a flexible balance between the square-order beamforming gain of the fully-passive RIS and the power amplification gain of the fully-active RIS by adjusting the active/passive element allocation.

This paper studies covert MIMO ambient backscatter communication (AmBC). In contrast to the prior covert AmBC works, we equip all devices with multiple antennas, where a backscatter tag works as a beamformer to hide both his transmission behavior and location from a warden. Technically, we derive two key metrics, namely the Kullback-Leibler divergence (KL divergence) measuring the transmission covertness and the covert rate measuring the transmission efficiency. We reveal that they both increase monotonically w.r.t the number of the tag’s antenna. This tradeoff makes the optimum parameter configuration non-trivial. To this end, we optimize the beamformer of the tag to maximize the covert rate subject to the covertness constraint under perfect knowledge of the warden’s channel state information (WCSI). We use the matrix permutation and semi-definite relaxation (SDR) method to make the formulated non-convex problem convex, and obtain the solution with the barrier method. We also study the impact of imperfect WCSI. To address the uncertain estimate error, we use the S-Procedure to reformulate the constraints linearly and the primal-dual interior-point method for a near-optimal solution. We conduct extensive numerical experiments and confirm that our work outperforms the state-of-the-art ones.

This paper presents a covert ambient backscatter communication (AmBC) system aided by the intelligent reflecting surface (IRS), where an IRS is used as a beamformer to help Tag for covert AmBC. Technically, we derive the expression of the Kullback-Leibler (KL) divergence to measure the detection performance of the warden. To fight against the warden’s detection, we propose a joint IRS’s beamforming and Tag’s reflection coefficient optimization scheme to maximize the covert AmBC rate subject to a key constraint metric of the KL divergence. To solve the non-convex problem, we formulate it as Fractional Programming (FP) for the linear iterations, and use the Majorization-Minimization (MM) algorithm to obtain the optimal parameters of IRS and Tag. Moreover, we investigate the covert performance with the imperfect channel state information of the warden’s link (WCSI) with Tag and IRS. Numerical results show the covert performance of the system, and illustrate the superiority of the IRS’s assistance to the covert backscatter efficiency. The simulations also show that the channel estimated error of Tag-Willie link has a negative impact on the covert backscatter efficiency, while the channel estimated errors of IRS-Willie link have the positive influence on the contrary.

This paper investigates the unsourced random access (URA) problem with a massive multiple-input multiple-output receiver that serves wireless devices in the near-field of radiation. We employ an uncoupled transmission protocol without appending redundancies to the slot-wise encoded messages. To exploit the channel sparsity for block length reduction while facing the collapsed sparse structure in the angular domain of near-field channels, we propose a sparse channel sampling method that divides the angle-distance (polar) domain based on the maximum permissible coherence. Decoding starts with retrieving active codewords and channels from each slot. We address the issue by leveraging the structured channel sparsity in the spatial and polar domains and propose a novel turbo-based recovery algorithm. Furthermore, we investigate an off-grid compressed sensing method to refine discretely estimated channel parameters over the continuum that improves the detection performance. Afterward, without the assistance of redundancies, we recouple the separated messages according to the similarity of the users’ channel information and propose a modifiedK-medoids method to handle the constraints and collisions involved in channel clustering. Simulations reveal that via exploiting the channel sparsity, the proposed URA scheme achieves high spectral efficiency and surpasses existing multi-slot-based schemes. Moreover, with more measurements provided by the overcomplete channel sampling, the near-field-suited scheme outperforms its counterpart of the far-field.

Covert communications studied in prior works typically consider only the average power constraint on the transmit signal. In this paper, we explore the optimal signaling for covert communication under the peak power constraint, in view of the realistic limitation at the transmitter. Our main result is that the rate-optimal transmit signal distribution under the covertness constraint forms finite hyperspheres, on each of which the points distribute uniformly. To prove this, a lemma is first deduced to explore the equivalence between maximizing the achievable rate and minimizing the covertness measured by the Kullback-Leibler divergence. The equivalence property is then exploited to characterize the covert communication as a specific two-user broadcast channel, which simplifies the intractable covertness constraint. After that, by exploiting the spherical symmetry property of Gaussian noise and the identity theorem of holomorphic functions, the main result is derived. Furthermore, the analytical representation of the optimal signaling in low signal-to-noise ratio (SNR) region is studied following Shamai’s approach. For high SNR region, a nonlinear dynamic programming algorithm is developed to generate the optimal signal distribution. For ease of practical implementation, the discrete constellations are optimized based on the sequential quadratic programming algorithm. Simulation results verify the optimality of the proposed hyper-sphere signaling and demonstrate the performance gain of the optimized constellations over typical modulation constellations.

Recent years have witnessed the great success of vision transformer (ViT), which has achieved state-of-the-art performance on multiple computer vision benchmarks. However, ViT models suffer from vast amounts of parameters and high computation cost, leading to difficult deployment on resource-constrained edge devices. Existing solutions mostly compress ViT models to a compact model but still cannot achieve real-time inference. To tackle this issue, we propose to explore the divisibility of transformer structure, and decompose the large ViT into multiple small models for collaborative inference at edge devices. Our objective is to achieve fast and energy-efficient collaborative inference while maintaining comparable accuracy compared with large ViTs. To this end, we first propose a collaborative inference framework termedDeViTto facilitate edge deployment by decomposing large ViTs. Subsequently, we design a decomposition-and-ensemble algorithm based on knowledge distillation, termed DEKD, to fuse multiple small decomposed models while dramatically reducing communication overheads, and handle heterogeneous models by developing a feature matching module to promote the imitations of decomposed models from the large ViT. Extensive experiments for three representative ViT backbones on four widely-used datasets demonstrate our method achieves efficient collaborative inference for ViTs and outperforms existing lightweight ViTs, striking a good trade-off between efficiency and accuracy. For example, our DeViTs improves end-to-end latency by 2.89× with only 1.65% accuracy sacrifice using CIFAR-100 compared to the large ViT, ViT-L/16, on the GPU server. DeDeiTs surpasses the recent efficient ViT, MobileViT-S, by 3.54% in accuracy on ImageNet-1 K, while running 1.72× faster and requiring 55.28% lower energy consumption on the edge device.

Reconfigurable intelligent surface (RIS) has been recently regarded as a disruptive candidate technology for enabling next generation wireless communication. It can establish favorable propagation environment to facilitate low-power and spectrally efficient data transmission, possessing attractive potential to support massive access. However, the required activity detection and channel estimation for RIS-assisted massive access is quite challenging due to the passive nature of the conventional reflecting elements. To this end, this paper considers massive access for RIS-assisted communication systems with semi-passive elements, which can operate in sensing mode for receiving signals. Then, by exploiting the sparsity of the RIS-BS channel in the virtual angular domain as well as the sporadic transmission of massive connectivity, we formulate the joint activity detection and channel estimation as a special bilinear recovery problem, which is a combination of sparse matrix factorization, compressed sensing (CS)-based generalized multiple measurement vector (GMMV) problem and matrix completion. Furthermore, we propose a novel hierarchical message passing-based algorithm to address the problem, in which approximate message passing (AMP)-based approximations are adopted to reduce the computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm and its superior performance compared with state-of-the-art baseline schemes.

Underwater exploration, inspection, and surveillance have become more popular thanks to recent advancements in underwater wireless communication technologies, of which underwater wireless optical communication (UWOC) boasts increased bandwidth, reduced latency, and heightened security. Normally, once underwater sensors collect the data, it is transmitted to remote offices through a relay node stationed on the sea surface. In this study, our focus is on establishing and analyzing the performance of secrecy outage in an uplink hybrid radio frequency (RF)-UWOC space-ocean system that comprises a collection of underwater sensors, a relay floating on the ocean surface, a legitimate satellite receiver, and an eavesdropping satellite. In the considered system, the transmission performance from end-to-end is influenced by several factors. These include path loss, small-scale fading, position randomness of the satellites in RF links, propagation loss, turbulence-induced fading, and random location distribution of the underwater sensors in UWOC links. To enhance our understanding of system design, geometric probability theory is employed to investigate the end-to-end secrecy outage performance of the dual-hop RF-UWOC space-ocean uplink transmission while comprehensively considering the impacts of these factors. Finally, the accuracy of the proposed analysis models is verified through numerical results.

Due to the unique merits of unmanned aerial vehicle (UAV) systems, they have already been harnessed for military, public, and civil applications. Time synchronization is a significant premise of formatting and applying UAV networks. However, the irregular high-speed mobile UAVs pose new challenges to time synchronization, especially when external time references are unavailable in some rigid scenarios. Therefore, in these harsh cases, self-time-synchronization (STS) without any external assistance should be concerned to overcome the relative velocity between UAVs caused by irregular high-speed motion. In this paper, a realistic timestamps model for the multi-UAV networks is established, and then a dynamic topology-based maximum likelihood estimator will be developed to carry out the STS. Furthermore, by introducing the information on frequency offset, a new estimator with the closed-form expression is proposed based on a two-way message exchange framework. After that, a tracking algorithm with the assistance of estimation results will be introduced to compensate for the time-varying change of the clock parameters for the dynamic topology UAV networks. To evaluate the performance of the estimator, both the estimation error and Cramér-Rao lower bound are analyzed. Numerical results show that the proposed algorithm exhibits its superiority in STS performance and computational complexity compared to the existing two-way message exchange algorithm using timestamps only.

Unmanned aerial vehicles (UAVs) have found extensive applications across diverse domains owing to their cost-effectiveness, uncomplicated structure, and adaptable takeoff and landing functionalities. Multi-UAV network systems can be coordinated to improve system performance significantly. Network time synchronization is a crucial prerequisite for establishing and operating multi-UAV networks, whereas the high-speed movement of UAVs presents challenges for time synchronization within multi-UAV networks. In this paper, we develop a practical information exchange model with high relative radial velocity and Gaussian distribution random transfer delay. By introducing Doppler information and a novel clock skew and clock offset consensus model, a Doppler and timestamp joint (DATJ) network time synchronization algorithm has been developed. Assessing the performance of the algorithms, we provide rigorous theoretical proof of network time synchronization convergence. Simulation results further validate the theoretical analysis and demonstrate that the proposed algorithm outperforms similar approaches in terms of synchronization performance.

Deep learning has significantly advanced wireless sensing technology by leveraging substantial amounts of high-quality training data. However, collecting wireless sensing data encounters diverse challenges, including unavoidable data noise, limited data scale due to significant collection overhead, and the necessity to reacquire data in new environments. Taking inspiration from the achievements of AI-generated content, this paper introduces a signal generation method that achieves data denoising, augmentation, and synthesis by disentangling distinct attributes within the signal, such as individual and environment. The approach encompasses two pivotal modules: structured signal selection and signal disentanglement generation. Structured signal selection establishes a minimal signal set with the target attributes for subsequent attribute disentanglement. Signal disentanglement generation disentangles the target attributes and reassembles them to generate novel signals. Extensive experimental results demonstrate that the proposed method can generate data that closely resembles real-world data on two wireless sensing datasets, exhibiting state-of-the-art performance. Our approach presents a robust framework for comprehending and manipulating attribute-specific information in wireless sensing.

With the expanding demands for the space-air-ground integrated Internet of Things (IoT), the low-Earth orbit (LEO) satellite can be regarded as an important complement to IoT networks. Due to the transparency of satellite orbit information and the exposing nature of transmitting links, satellite-ground communication is extremely vulnerable to eavesdropping and jamming attacks. To establish a transmission link, the most crucial procedure was signal acquisition. Existing frequency hopping/direct sequence signal acquisition algorithms were either too time costing for the short visibility window of the LEO satellite or too resource costing for the LEO satellite devices. To alleviate this issue, we propose a novel differential coherent accumulation acquisition strategy for the LEO satellite-enabled IoT network to strike a balance between performance and complexity. It is also demonstrated that the proposed acquisition strategy is capable of achieving high-performance signal acquisition in the low-carrier-to-noise ratio and large dynamic regions. Moreover, we derive and simulate false alarm probability, detection probability, computational complexity, and mean square error of both the delay and Doppler factors in the additive white Gaussian noise channel. Numerical simulation results show that the proposed acquisition strategy improves the performance by 1.6 dB over the noncoherent accumulation strategy but at the expense of 0.43% complexity increase.

With the development of the sixth-generation wireless communication technology, the difficulty of covert communication in the satellite communications system (SCS) is further increased. In this paper, a satellite covert communication system based on reconfigurable intelligent surface (RIS) is studied, which consists of a satellite, a terrestrial receiver, RIS, and a warden. Specifically, the ground terminal simultaneously receives the signal transmitted by the satellite and the signal reflected by RIS, while the warden wants to overhear the transmitted signal from the satellite and that reflected by the RIS. The main target of this wort is to maximize the minimum covert rate to improve signal quality while considering the limited onboard resources, the hardware constraints of RIS, and the requirement to implement covert communication. To address the non-convex problem, we propose an effective alternating optimization scheme by jointly optimizing transmitter precoding and RIS to improve the covert communication performance of the considered system. In addition to ideal RIS, we also consider the non-ideal RIS, the angle of which is discrete in the actual case. In addition, several simplified methods are proposed to decrease the computational complexity of the covert communication optimal problem under multiple terminals. Finally, simulation results are provided to verify the superiority of the proposed scheme compared with the benchmark scheme, which suggests that the considered RIS-based satellite-terrestrial covert communication system can significantly improve the achievable masking.

With the emerging concept of satellite mega-constellations (SatCons), connections are about to become faster and broader, building an engine to boost innovation and productivity for every industry. In the following decades, over a hundred of SatCons with enormous application potentials are on the cusp of deployment, and their long-term success hinges on reliable information exchanges. We identify the renascent multicarrier spread spectrum (MCSS) paradigm of sufficient spectral flexibility as the most effective and reasonable candidate for such issues. This article first sheds light on possible use cases for MCSS in SatCons, such as navigation-communication-telecontrol integration, physical-layer security, and intelligent anti-jamming. It also enlightens opportunities by virtue of the many benefits that MCSS may offer, and highlights major challenges encountered during its utilization. Finally, insights into future trends are provided. This article could support system designers in finding the right optimization priorities for future SatCons.

In this work, a reconfigurable intelligent surface (RIS)-based multi-input–multi-output (MIMO) covert communication system is considered and studied for Internet of Things (IoT) networks, while the joint optimization of precoder and RIS reflection phase is carried out to improve the covert communication performance. To solve this problem, we first derive the optimal signal-interference-to-noise ratio under covert communication and transmit power constraints. Then, we simplify the optimized function and use an iterative optimization algorithm to determine the optimal phase shift and precoding in continuous and discrete cases. Simulation results show that the RIS-aided MIMO covert communication system proposed in this article can significantly improve the invisibility of the implementation from Willie. In addition, for covert communication, the performance of the continuous phase shift case outperforms that of the discrete phase shift and fixed phase.

The Internet of Remote Things (IoRT) supported by low-Earth orbit (LEO) satellites is becoming indispensable for remote sensing and it will play an important role in the forthcoming sixth-generation (6G) communication network. In exploring its applications in remote mining and smart grid, etc., it is found that the implementation of IoRT faces challenges, including limited energy supplies, high-mobility, and security vulnerabilities. To address these challenges, we propose employing collaborative LEO satellites to enable the implementation of secure and green IoRT. By combining the uplink signals received at collaborative LEO satellites, the signal-to-noise ratio (SNR) can be significantly improved, so that relieving the transmit power requirement of the energy-limited terminal. Aiming at constructing a collaborative LEO satellite-based IoRT network, this article introduces the system design principles regarding to frequency planning, waveform selection, collaboration strategies, and terminal design. In order to obtain optimal collaboration performance, we propose a signal coherent combining scheme to compensate Doppler shift, propagation delay, and phase differences. Furthermore, we propose a modified SUMPLE algorithm to estimate and compensate phase differences among satellites, which is applicable to direct-sequence spread spectrum (DSSS) signal scheme. Simulation results demonstrate that our proposed algorithm outperforms the traditional SUMPLE algorithm in combining gain.

With the ever-growing communication demands and the unceasing miniaturization of mobile devices, the Internet of Things is expanding the amount of mobile terminals to an enormous level. To deal with such numbers of communication data, plenty of base stations (BSs) need to be deployed. However, denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which could increase network burden and degrade the users experience, especially in traffic hotspot areas. In this article, we develop a unified framework to investigate the handover performance of wireless networks with traffic hotspots. Using the stochastic geometry, we derive the theoretical expressions of average distances and handover metrics in HetNets, where the correlations between users and BSs in hotspots are captured. Specifically, the distributions of macro cells are modeled as independent Poisson point processes (PPPs), and the two tiers of small cells outside and inside the hotspots are modeled as PPP and Poisson cluster process (PCP) separately. A modified random waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon in traditional models and to increase the accuracy of handover decision. By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived. Afterward, we derive the expressions of average distances from a typical terminal to different BSs and reveal that the handover rate, handover failure rate, and ping-pong rate are deduced as the functions of BS density, scattering variance of clustered small cell, user velocity, and threshold of triggered time. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical expressions.

Reconfigurable intelligent surface (RIS) has been envisioned as an innovative technology to assist millimeter wave (mmWave) communications. Thanks to both advantages of low hardware cost and low power consumption, the hybrid transceiver structure also becomes an integral component of mmWave systems. However, due to practical limitations of hardware components, the RIS-assisted mmWave communications usually suffer unavoidable hardware impairments (HWIs). In this paper, we aim to minimize the (sum) MSE and maximize the average rate of the hardware-impaired RIS-assisted point-to-point mmWave MIMO system, respectively, by jointly optimizing the hybrid transceiver and RIS reflection coefficients under the realistic discrete phase shift constraints. We firstly consider the single-antenna user case and propose efficient alternating optimization (AO) algorithms to solve the two intractable problems. A binary-oriented exact penalty (BEP) method is developed for the involved discrete optimization, which is able to strike a good trade-off between performance and complexity. Moreover, we analyze the optimality of AO algorithms under the cascaded line-of-sight (LoS) channel condition, and reveal both the MSE floor effect and average rate saturation effect in the high-SNR regime. The above studies are then extended to the general multi-antenna user case, where a low-complexity two-phase scheme with the aim of creating the favorable RIS-cascaded channel in the first phase and enhancing system performance in the second phase is proposed. This two-phase scheme is also demonstrated to attain the optimal performance in the LoS scenario. Numerical results validate our theoretical analysis and illustrate superior performance of the proposed algorithms over various benchmark schemes.

With the benefits of unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS), they can be combined to further enhance the communication performance. However, the high-quality air-ground channel is more vulnerable to the adversarial eavesdropping. Therefore, in this paper, we propose a covert communication scheme assisted by the UAV-IRS to maximize the covert transmission rate. Specifically, the ground transmitter, Alice, secretly delivers the private message to a legitimate receiver, Bob, via the UAV-IRS, wishing that the transmission will not be observable by the warden, Willie. In addition, Willie is adversarial to Alice and the UAV-IRS, which makes his accurate location difficult to obtain. Given this fact, we first determine an optimal detection threshold and derive the error detection probability at Willie, which is the worst-case situation for the legitimate transmission. Then, we maximize the covert transmission rate by alternatively optimizing the transmit power of Alice, the IRS phase shift and the horizontal location of UAV-IRS subject to the covert requirements. Numerical results are presented to demonstrate the effectiveness of the proposed covert communication scheme assisted by UAV-IRS.

Frame slotted Aloha (FSA) is the de facto MAC layer standard protocol in many ultra-low-power IoT applications, such as Radio Frequency Identification (RFID) and Machine to Machine (M2M) communications. As the age of information (AoI) is an emerging and critical metric for quantifying the freshness of the status update information collected in time-sensitive IoT applications, systematic analysis of AoI for FSA is called for. However, very limited work has been done on this topic despite its both theoretical and practical implications for the operation and optimization of FSA. To fill this void, this paper delivers a comprehensive analysis of AoI for four versions of FSA, namely synchronous and asynchronous FSA with and without retransmission. The core technique of our analysis is to model the AoI for FSA as Markov chains to derive statistics on the delay and inter-delivery time. Our central results consist of the lower bounds of AoI, the exact AoI expressions in the four FSA protocols and the optimum frame length for the AoI of FSA. Our analysis reveals the impact of the arrival rate and the protocol parameters on AoI, and also shows that the retransmission would improve AoI when the arrival rate is small.

Recently, deploying deep neural network (DNN) models via collaborative inference, which splits a pre-trained model into two parts and executes them on user equipment (UE) and edge server respectively, becomes attractive. However, the large intermediate feature of DNN impedes flexible decoupling, and existing approaches either focus on the single UE scenario or simply define tasks considering the required CPU cycles, but ignore the indivisibility of a single DNN layer. In this article, we study the multi-agent collaborative inference scenario, where a single edge server coordinates the inference of multiple UEs. Our goal is to achieve fast and energy-efficient inference for all UEs. To achieve this goal, we design a lightweight autoencoder-based method to compress the large intermediate feature at first. Then we define tasks according to the inference overhead of DNNs and formulate the problem as a Markov decision process (MDP). Finally, we propose a multi-agent hybrid proximal policy optimization (MAHPPO) algorithm to solve the optimization problem with a hybrid action space. We conduct extensive experiments with different types of networks, and the results show that our method can reduce up to 56% of inference latency and save up to 72% of energy consumption.

Large low earth orbit (LEO) mega-constellation systems have been designed and deployed as a global backbone to provide ubiquitous connectivity across the world. However, due to the high traffic load/congestion arisen from the required numerous information relay and forwarding, it is a challenge for LEO mega-constellation systems to set up long-distance connections between two remote terrestrial users for real-time communications, which requires strict low latency. Other than inter-LEO satellite links (ILSL), introducing third-party relays, such as terrestrial, aerial, and satellite relays, is an alternative way to improve the latency performance for wide-area deliveries of real-time traffic. However, the reliability of the transmitted signal will unavoidably degrade, because of the increased path-loss attributed to long-distance relaying transmissions. Then, to reveal the principle that the third-party relays affect the latency and reliability, in this work, an LEO satellite-terrestrial communication scenario is considered, in which two remote terrestrial users communicate with each other via an LEO mega-constellation system. Analysis models are built up to investigate the end-to-end time delay and outage performance while considering different ILSL, terrestrial, aerial, and satellite relay assisted transmission scenarios. More specifically, by applying geometrical probability theory, exact/approximated closed-form analytical expressions have been derived for average time delay

This paper presents a unicast beamforming network for covert ambient backscatter communications (AmBC). Different from the prior covert backscatter works using artificial noise or a power-variable RF source, our work achieves covertness by arming the backscatter tag with multiple antennas. The tag uses a subset of its antennas to modulate covert information on backscattered RF signals while using the other antennas to modulate overt message as a cover to hide the covert transmission from the warden. We first derive the warden’s optimum detection threshold to minimize the detection error probability. To fight against the optimum warden, the tag changes impedance matching on each antenna to vary the reflected power and make the reflected overt and covert signals beamforming at space. We derive the optimum beamforming vectors by solving the problems of maximizing the covert rate at the receiver and the detection error probability at the warden, respectively. To address the non-convex constraints, we use semi-definite relaxation (SDR) and obtain near optimal parameters by designing binarySearch algorithms. Numerical results confirm the superiority of our work to the state-of-the-art one, and the existence of tradeoffs between the covert rate and the detection error probability.

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.

In this paper, we propose a general framework on the hybrid analog-digital transceiver design for multi-hop communications. For the inclusive purpose, a transceiver model unifying both linear and nonlinear transceivers has been taken into account. Various performance metrics, including the most representative capacity and weighted mean-squared error (MSE), have been investigated in a unified manner. In particular, to meet practical needs for the quality of services (QoS), a general eigenvalue power constraint model is introduced, which contains a sum power constraint and box eigenvalue constraints as special cases. Specifically, by carefully designing the auxiliary analog and digital beamformers, the multi-hop transceiver optimization is decomposed into a series of independent sub-problems, where the analog beamformers for different hops are completely decoupled. Based on that, this framework establishes a majorization-minimization (MM) based analog beamformer design algorithm, which is able to handle the complicated weighted unit-modulus matrix optimizations by finding their semi-closed-form solutions. Furthermore, an efficient waterfilling algorithm is proposed for the digital beamformer designs to deal with the difficulties of optimizations subject to the multiple eigenvalue power constraints. The numerical results are provided to demonstrate the performance advantages of the proposed framework.

This paper investigates joint channel estimation and device activity detection in the LEO satellite-enabled grant-free random access systems with large differential delay and Doppler shift. In addition, the multiple-input multiple-output (MIMO) with orthogonal time-frequency space modulation (OTFS) is utilized to combat the dynamics of the terrestrial-satellite link. To simplify the computation process, we estimate the channel tensor in parallel along the delay dimension. Then, the deep learning and expectation-maximization approach are integrated into the generalized approximate message passing with cross-correlation-based Gaussian prior to capture the channel sparsity in the delay-Doppler-angle domain and learn the hyperparameters. Finally, active devices are detected by computing energy of the estimated channel. Simulation results demonstrate that the proposed algorithms outperform conventional methods.

In order to reduce computational complexity and hardware cost for reconfigurable intelligent surface (RIS)-aided multiple-input–multiple-output (MIMO) systems, in this article, the subarray partition algorithm designs at RIS are investigated. Without instantaneous channel state information (CSI) of the RIS-related links, the subarray partition algorithms aim at minimizing the number of subarrays while keeping a minimum sum rate requirement. In nature, the subarray partition optimization problem is a combinatorial optimization and NP-hard because of many discrete optimization variables. Three kinds of subarray partition algorithms are proposed. The first one is named as a fixed pattern subarray partition algorithm, in which subarray is arranged in a predefined manner. This algorithm is easy to implement but its performance is far from optimal. To reap the benefits of RIS as much as possible, two dynamic pattern subarray partition algorithms are given as well. The first dynamic pattern algorithm is the greedy dynamic pattern subarray partition algorithm that is more complicated than the fixed pattern one but benefits much better performance. To reduce complexity, the relaxation-based dynamic pattern algorithm is given, which has almost the same performance as the greedy dynamic algorithm but has a much lower complexity. At the end of the whole work, numerical results are given to access the performance of the proposed algorithms.

In this article, we investigate the training optimization for multiple-input–multiple-output (MIMO)-aided Internet of Things (IoTs) systems that employ subarray-based intelligent reflecting surface (IRS). In order to overcome the nonlinear relationship between two cascaded channel matrices, the IRS can be divided into a series of subarrays, for which only an equivalent cascaded channel matrix should be estimated in each subarray. Correspondingly, the training sequence should be divided into multiple segments. By sufficiently utilizing the available statistical channel state information (CSI), either mean-square error (MSE) minimization or mutual information (MUI) maximization can be taken as the performance metric for optimizing the training sequence. A variety of fairnesses among different subarray channel estimations has been taken into account. Furthermore, in order to reduce the hardware cost of the power amplifier, we propose a two-stage training sequence structure, including a fully digital filter and a constant modulus sequence. To further reduce computational complexity, various low-complexity water-filling solutions are proposed. Numerical results demonstrate the accuracy and efficiency of the proposed solutions.

The machine-type communication (MTC) enables a broad range of applications from mission-critical services to massive deployment of autonomous devices in the Internet of Things (IoT) networks. To release more spectrum resources for facilitating the explosive traffic of MTC in ultradense Cellular-IoT (CIoT) networks, full-duplex (FD) technology has been considered as a candidate mechanic due to its respective advantages in terms of the spectral efficiency (SE). Compared with the traditional half-duplex (HD) technology, the FD technology can (in theory) attain twice the SE gain. Nevertheless, the performance of FD technology is severely limited by both the self-interference (SI) and the mutual interference (MI) in the presence of multiple users. What is more serious is that when FD technology is used in a multiuser communication system, the system performance is very sensitive to the service load of the entire network, and it may even appear fragile in an ultrahigh load environment. In this article, we will delve into investigating the performance of the FD technique in the CIoT networks by considering the impacts of a variety of aspects, including the SI cancelation capability (SICC) of the FD-mode devices, the network’s workload, as well as the devices’ distribution density (DDD), the purpose of which is to maximize both the SE and the sum throughput (ST) of the network by optimizing those critical parameters. It is shown that the FD mode is capable of improving the ST of the CIoT networks in either the low-traffic-volume or low-device-density regime, provided that the devices’ SICC could be up to 100dB. At the same time, research results show that further increasing both the devices’ density and the traffic load, even without compromising the FD devices’ SICC, will still help improve the performance superiority of FD technology. Numerical results show that by implementing an appropriate workload-driven mode-selection scheme, we can sufficiently exploit the FD/HD gain according to the instantaneous radio frequency environment.

Reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems relying on hybrid beamforming structures are capable of achieving high spectral efficiency at a low hardware complexity and low power consumption. In this article, we propose an RIS-assisted mmWave point-to-point system relying on dynamically configured subarray connected hybrid beamforming structures. More explicitly, an energy-efficient analog beamformer relying on the twin-resolution phase shifters is proposed. Then, we conceive a successive interference cancelation (SIC)-based method for jointly designing the hybrid beamforming matrix of the base station (BS) and the passive beamforming matrix of the RIS. Specifically, the associated bandwidth-efficiency maximization problem is transformed into a series of subproblems, where the subarray of phase shifters and RIS elements is jointly optimized for maximizing each subarray’s rate. Furthermore, a greedy method is proposed for determining the phase shifter configuration of each subarray. We then propose to update the RIS elements relying on a complex circle manifold (CCM)-based method. The proposed dynamic subconnected structure as well as the proposed joint hybrid and passive beamforming method strike an attractive tradeoff between the bandwidth efficiency and power consumption. Our simulation results demonstrate the superiority of the proposed method compared to its traditional counterparts.

In wireless-powered device-to-device (D2D) networks with backscatter-aided passive relays, the cooperation among D2D pairs is of special necessity. With the cooperation among D2D pairs, the devices can act as passive relays to enhance the signal reception, and share their active transmission time to improve the resource utilization. Therefore, how the D2D pairs form coalitions and how to reallocate the active transmission time to the D2D pairs after forming coalitions should be studied. In this article, we develop a hierarchical cooperative game framework for the network to investigate how the D2D pairs cooperate with each other. Specifically, in the upper layer of the game, we employ the coalition game to formulate how the D2D pairs form coalitions and analyze the stability of the coalition structure and the complexity of the proposed strategy. In the lower layer of the game, we employ the bargaining game to investigate how the D2D pairs share the active transmission time, and analytically evaluate the complexity of deriving the Nash bargaining solution. Simulation results demonstrate the effectiveness of our proposed scheme. The results also show that the computational complexity of our proposed scheme is only a small fraction of that of the exclusive search-based scheme. Nevertheless, compared with the exclusive search-based scheme, the total effective amount of the transmitted data in the proposed cooperative scheme reduces no more than 2%, which is very minor.

Space–air–ground Internet of Vehicle networks is a promising network paradigm to support diverse vehicular services. Exploiting the unique advantages of spatial, aerial, and terrestrial network segments, the Quality of Service (QoS) of different services can be met by smart access network selection. However, the integrated network inevitably has to face many challenges, such as dynamic network topology, heterogeneous resources, and long propagation latency, which can seriously degrade the QoS performance. In order to provide QoS guarantee to vehicles, we propose a novel architecture, called QoS-guaranteed access assistance (QA2) serve vehicles with access assistance. To be specific, a virtualized layer is established in the network, which contains the logical resources of ground network infrastructures. Using that layer, Access Assistants are flexibly deployed at a dynamic set of ground infrastructures, and then help vehicles to acquire network services with satisfied QoS requirements. Furthermore, to accommodate temporal–spatial-varying network demands of vehicles, QA2 adopts a cost-effective deployment scheme, which updates the locations of Assistants based on the real-time traffic flow with the minimum operation cost. The evaluation results demonstrate the effectiveness of QA2, which increases request success rate by more than 50%. The results also prove that the cost-effective deployment scheme can efficiently adapt to the change of traffic flow and achieve a good balance between QoS guarantee and cost saving. The operation cost is saved by more than 25% and the QoS performance is improved by more than 22%.

Missing item event detection is one of the most important radio-frequency identification (RFID)-enabled functions. Yet it is largely unaddressed how to fast and reliably detect missing item event in multitagged RFID systems where multiple tags are tagged on one item. The canonical methods can only solve tag-level detection problem where each item is associated with one tag, and applying them to detect the missing multitagged items would falsely alarm and is time inefficient. To bridge the gap, this article formulates and analyzes the missing multitagged item detection problem. Our key idea is to search the proper seeds so that the reader only needs to probe a subset of the tags each being selected from different items instead of the entire tag set for the missing item detection. By employing the computation-communication tradeoffs, we design two protocols named M2ID and M2ID+ that classifies the tags before the segmentation compared to the former to improve time efficiency. With the derived optimum parameters, our protocols can achieve up to$4\times$performance gain in terms of time efficiency compared with the state-of-the-art solution.

Industrial Internet of Things (IIoT) brings opportunities for innovation and upgrading to industrial production, but meanwhile, it poses unprecedented challenges on the physical layer of wireless communications. In this article, we propose a novel frequency hopping transmission scheme for IIoT, which owns much high efficiency and strong anti-jamming ability. This new scheme is built based on the combination of coherent fast frequency hopping (CFFH) and cyclic code shift key (CCSK), named phase cyclic shift-keying-based CFFH (PCSK-CFFH). For the proposed PCSK-CFFH system, the shift of orthogonal base sequence is adopted to modulate the phases among interhop, instead of transmitting each hop with the same phase as conventional CFFH. This scheme is able to further exploit the extra dimensions of CFFH signals and enables the CFFH system to transmit additional information at no extra cost on either bandwidth or power. Moreover, the bit error rate performance of the proposed PCSK-CFFH system is analyzed theoretically. To improve its ability against severe narrow-band jamming, the selection combining is introduced in our scheme. Furthermore, the anti-jamming performance of the proposed PCSK-CFFH system is also demonstrated. Finally, the simulation results are shown to demonstrate that: by introducing the interhop dimension to CFFH, PCSK-CFFH improves the power efficiency and spectrum efficiency by multiple times and simultaneously inherits the excellent anti-jamming performance of the conventional CFFH system. It can be concluded that the proposed PCSK-CFFH scheme has a great potential to be applied to low-power and highly reliable IIoT communications.

The satellite–air–ground networks (SAGNs) are experiencing unprecedented deployment for the advantages of global coverage and flexibility. However, the open light-of-sight communication links from satellites to the unmanned aerial vehicles (S2U) in SAGNs are vulnerable to malicious jamming and inadvertent interferences from other communication satellites, challenging the reliability of SAGNs. Despite the great importance, the anti-jamming S2U communication problem in SAGNs has been largely overlooked. To address this problem, we propose a two-stage anti-jamming scheme that embraces the cooperation among the unmanned aerial vehicles (UAVs) within the same group and jointly designs the hovering altitude of UAVs and the beamformers of satellites and cooperative UAVs to combat the malicious jamming and the cross-tier interferences. Specifically, following the proposed scheme, we formulate the problem of maximizing the minimum rate of the UAVs with the presence of the malicious jamming and inadvertent interferences into a nonconvex optimization problem. We then transform the problem into two tractable convex subproblems with feasible point pursuit successive convex approximation (FPP-SCA) method and provide the solutions to configure the hovering altitudes and the beamformers. We also conduct extensive simulations and the results show that the proposed anti-jamming scheme is efficient in terms of the UAVs’ capacity and the anti-jamming ability compared with the ones without cooperative UAVs and with fixed cooperators (FixCo).

In this paper, we investigate a joint device activity detection (DAD), channel estimation (CE), and data decoding (DD) algorithm for multiple-input multiple-output (MIMO) massive unsourced random access (URA). Different from the state-of-the-art slotted transmission scheme, the data in the proposed framework is split into only two parts. A portion of the data is coded by compressed sensing (CS) and the rest is low-density-parity-check (LDPC) coded. In addition to being part of the data, information bits in the CS phase also undertake the task of interleaving pattern design and CE. The principle of interleave-division multiple access (IDMA) is exploited to reduce the interference among devices in the LDPC phase. Based on the belief propagation (BP) algorithm, a low-complexity iterative message passing (MP) algorithm is utilized to decode the data embedded in these two phases separately. Moreover, combined with successive interference cancellation (SIC), the proposed joint DAD-CE-DD algorithm is performed to further improve performance by utilizing the belief of each other. Additionally, based on the energy detection (ED) and sliding window protocol (SWP), we develop a collision resolution protocol to handle the codeword collision, a common issue in the URA system. In addition to the complexity reduction, the proposed algorithm exhibits a substantial performance enhancement compared to the state-of-the-art in terms of efficiency and accuracy.

This paper considers the joint channel estimation and device activity detection in the grant-free random access systems, where a large number of Internet-of-Things devices intend to communicate with a low-earth orbit satellite in a sporadic way. In addition, the massive multiple-input multiple-output (MIMO) with orthogonal time-frequency space (OTFS) modulation is adopted to combat the dynamics of the terrestrial-satellite link. We first analyze the input-output relationship of the single-input single-output OTFS when the large delay and Doppler shift both exist, and then extend it to the grant-free random access with massive MIMO-OTFS. Next, by exploring the sparsity of channel in the delay-Doppler-angle domain, a two-dimensional pattern coupled hierarchical prior with the sparse Bayesian learning and covariance-free method (TDSBL-FM) is developed for the channel estimation. Then, the active devices are detected by computing the energy of the estimated channel. Finally, the generalized approximate message passing algorithm combined with the sparse Bayesian learning and two-dimensional convolution (ConvSBL-GAMP) is proposed to decrease the computations of the TDSBL-FM algorithm. Simulation results demonstrate that the proposed algorithms outperform conventional methods.

In this work, we study the covert communications in ambient backscatter systems (ABS) with uncontrollable RF source on AWGN channels. In contrast to the prior works impractically assuming the existence of acontrollableRF excitation source, our work arms the receiver of covert information with the full-duplex ability. The covert receiver can emit artificial noise (AN) with variable power to cover up the modulation action of a tag on the excitation signals while receiving the backscattered information. Specifically, we first derive the warden’s optimum power-detection threshold that minimizes the detection error probability. To against the optimal warden, we design the covert backscatter communication policy that determines the feasible region of the AN power at the covert receiver depending on the transmission power of the RF source and the reflection coefficient of the tag and guarantees the covertness constraint. We analyze the performance of the policy and provide the closed-form maximum covert rate and maximum detection error probability at the warden, revealing their tradeoff. The numerical analysis shows the increasing transmission power of the RF source and tag’s reflection coefficient would degrade the covertness of ABS when the covert receiver has to increase the AN power.

Lower earth orbit (LEO) satellites play an important role in the integration of space and terrestrial communication networks, which typically encounter high-mobility scenarios. It has been shown that orthogonal time frequency space (OTFS) modulation performs well in such high-mobility scenarios by transforming the time-varying channels into the delay-Doppler domain. In this paper, we develop a joint channel estimation and data detection algorithm for OTFS-based LEO satellite communications. Firstly, we adopt the powerful variational Bayesian inference (VBI) method for estimating the delay-Doppler channel vector, which contains the channel gain, the delay and the Doppler. Secondly, we exploit the unknown data symbols in an OTFS frame as ‘virtual pilots’ for improving the accuracy of channel estimation and detect them simultaneously. Our simulation results demonstrate that the proposed algorithm achieves improved channel estimation mean square error and bit error rate performance than its conventional counterparts.

This paper investigates the unsourced random access (URA) scheme to accommodate numerous machine-type users communicating to a base station equipped with multiple antennas. Existing works adopt a slotted transmission strategy to reduce system complexity; they operate under the framework of coupled compressed sensing (CCS) which concatenates an outer tree code to an inner compressed sensing code for slot-wise message stitching. We suggest that by exploiting the MIMO channel information in the angular domain, redundancies required by the tree encoder/decoder in CCS can be removed to improve spectral efficiency, thereby an uncoupled transmission protocol is devised. To perform activity detection and channel estimation, we propose an expectation-maximization-aided generalized approximate message passing algorithm with a Markov random field support structure, which captures the inherent clustered sparsity structure of the angular domain channel. Then, message reconstruction in the form of a clustering decoder is performed by recognizing slot-distributed channels of each active user based on similarity. We put forward the slot-balanced$ K $-means algorithm as the kernel of the clustering decoder, resolving constraints and collisions specific to the application scene. Extensive simulations reveal that the proposed scheme achieves a better error performance at high spectral efficiency compared to the CCS-based URA schemes.

We propose a cluster-based multi-carrier beam division multiple access (MC-BDMA) scheme to enable massive device terahertz (THz) communications with the dynamic-subarray hybrid beamforming (HBF) architecture. This scheme is motivated by a limitation of conventional HBF architectures, i.e., the number of users cannot exceed the number of radio frequency (RF) chains. By exploiting the unique properties of THz channels, such as high distance-and-frequency dependence, high sparsity, and small angular spread, we propose multiple users in the same cluster to be supported by a single RF chain with distance-aware multi-carrier modulation. Moreover, we propose different clusters to be divided by BDMA that is accomplished through HBF design. In our proposed scheme, we first design a novel antenna selection and subarray partition algorithm for the dynamic-subarray HBF architecture to compensate for performance loss caused by diverse channels among users. We then design an algorithm for multi-carrier HBF which maximizes the achievable throughput and eliminates the inter-beam interference. Numerical results show that our proposed cluster-based MC-BDMA scheme with the dynamic-subarray HBF architecture provides an almost 100% spectral efficiency gain and a 60% energy efficiency gain over the conventional non-cluster HBF scheme in massive device communications.

Satellite-aerial communication (SAC) is the most necessary and reliable way to bridge the aerial terminals with their remote command and control center when terrestrial relay infrastructure is unavailable. However, as an inescapable obstacle, adjacent space interference from neighboring satellites unavoidably leads to the loss of the performance of SACs. To uncover the impacts of space interference, in this paper, a SAC system including a satellite (S), an aerial platform (D), and a space interference (I) is considered. Specifically, S operates in a circular orbit and I, which is randomly distributed in the neighboring space around S, produces a jamming signal to interfere with the information delivery between S and D. Considering the randomness of S and I and employing geometric probability theory, an approximate and asymptotic outage probabilities of the considered SAC system are studied. Moreover, the multi-interference scenario is investigated accordingly. Finally, numerical results are presented to validate our proposed analysis and some insightful conclusions are achieved to reveal the effects of spatially random space interference on SAC.

Due to the considerable fading arising from the long-distance transmission between satellites and terrestrial terminals as well as the complex geographic environments on the earth, aerial relays have been designed to facilitate the information delivery between the satellites and terrestrial terminals when reliable direct data transmissions cannot be achieved. In this paper, a cooperative satellite-aerial-terrestrial network (CSATN) system including a satellite (S), an aerial relay (R), and a terrestrial receiver (D) is considered, while decode-and-forward relay scheme is adopted. Specifically, inspired by the beam shape of the transmission signal from S, we model the operation space of R as a circular truncated cone to accurately reflect the practical large-scale transmission scenarios, the height of which is decided by the upper and lower limitations of the operation height of R. Thus, considering the randomness of R and D and employing geometric probability theory, the exact and asymptotic outage probability (OP) of S-R, S-D, and R-D links is studied, and then the end-to-end outage performance of CSATN is investigated accordingly by using approximation method. Finally, numerical results are presented to validate our proposed analysis.

Recently, the compression and deployment of powerful deep neural networks (DNNs) on resource-limited edge devices to provide intelligent services have become attractive tasks. Although knowledge distillation (KD) is a feasible solution for compression, its requirement on the original dataset raises privacy concerns. In addition, it is common to integrate multiple pretrained models to achieve satisfactory performance. How to compress multiple models into a tiny model is challenging, especially when the original data are unavailable. To tackle this challenge, we propose a framework termed collaborative data-free knowledge distillation via multi-level feature sharing (CDFKD-MFS), which consists of a multi-header student module, an asymmetric adversarial data-free KD module, and an attention-based aggregation module. In this framework, the student model equipped with a multi-level feature-sharing structure learns from multiple teacher models and is trained together with a generator in an asymmetric adversarial manner. When some real samples are available, the attention module adaptively aggregates predictions of the student headers, which can further improve performance. We conduct extensive experiments on three popular computer visual datasets. In particular, compared with the most competitive alternative, the accuracy of the proposed framework is 1.18% higher on the CIFAR-100 dataset, 1.67% higher on the Caltech-101 dataset, and 2.99% higher on the mini-ImageNet dataset.

Deep neural networks (DNNs) are shown to be excellent solutions to staggering and sophisticated problems in machine learning. A key reason for their success is due to the strong expressive power of function representation. For piecewise linear neural networks (PLNNs), the number of linear regions is a natural measure of their expressive power since it characterizes the number of linear pieces available to model complex patterns. In this article, we theoretically analyze the expressive power of PLNNs by counting and bounding the number of linear regions. We first refine the existing upper and lower bounds on the number of linear regions of PLNNs with rectified linear units (ReLU PLNNs). Next, we extend the analysis to PLNNs with general piecewise linear (PWL) activation functions and derive the exact maximum number of linear regions of single-layer PLNNs. Moreover, the upper and lower bounds on the number of linear regions of multilayer PLNNs are obtained, both of which scale polynomially with the number of neurons at each layer and pieces of PWL activation function but exponentially with the number of layers. This key property enables deep PLNNs with complex activation functions to outperform their shallow counterparts when computing highly complex and structured functions, which, to some extent, explains the performance improvement of deep PLNNs in classification and function fitting.

The cell-free MIMO concept relying on hybrid precoding constitutes an innovative technique capable of dramatically increasing the network capacity of millimeter-wave (mmWave) communication systems. It dispenses with the cell boundary of conventional multi-cell MIMO systems, while drastically reducing the power consumption by limiting the number of radio frequency (RF) chains at the access points (APs). In this paper, we aim for maximizing the weighted sum rate (WSR) of mmWave cell-free MIMO systems by conceiving a low-complexity hybrid precoding algorithm. We formulate the WSR optimization problem subject to the transmit power constraint for each AP and the constant-modulus constraint for the phase shifters of the analog precoders. A block coordinate descent (BCD) algorithm is proposed for iteratively solving the problem. In each iteration, the classic Lagrangian multiplier method and the penalty dual decomposition (PDD) method are combined for obtaining near-optimal hybrid analog/digital precoding matrices. Furthermore, we extend our proposed algorithm for deriving closed-form expressions for the precoders of fully digital cell-free MIMO systems. Moreover, we present the convergency analysis and complexity analysis of our proposed method. Finally, our simulation results demonstrate the superiority of the algorithms proposed for both fully digital and hybrid precoding matrices.

Training beam design for channel estimation with infinite-resolution and low-resolution phase shifters (PSs) in hybrid analog-digital milimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems is considered in this paper. By exploiting the sparsity of mmWave channels, the optimization of the sensing matrices (corresponding to training beams) is formulated according to the compressive sensing (CS) theory. Under the condition of infinite-resolution PSs, we propose relevant algorithms to construct the sensing matrix, where the theory of convex optimization and the gradient descent in Riemannian manifold is used to design the digital and analog part, respectively. Furthermore, a block-wise alternating hybrid analog-digital algorithm is proposed to tackle the design of training beams with low-resolution PSs, where the performance degeneration caused by non-convex constant modulus and discrete phase constraints is effectively compensated to some extent thanks to the iterations among blocks. Finally, the orthogonal matching pursuit (OMP) based estimator is adopted for achieving an effective recovery of the sparse mmWave channel. Simulation results demonstrate the performance advantages of proposed algorithms compared with some existing schemes.

Hybrid energy supply composed of batteries and radio frequency (RF) signals has been anticipated to be a prominent solution for balancing the reliability and self-sustainability of future IoT networks. The newly emerging reconfigurable intelligent surface (RIS) is also capable of greatly enhancing spectral and energy efficiencies. In this paper, by considering an asynchronous transmission protocol among all energy receivers (ERs) and assuming the perfect self-interference cancellation (SIC) at the hybrid access point (HAP), we aim to maximize sum throughput in the RIS-aided hybrid powered communication networks (HPCNs) by jointly optimizing the transmit covariance matrices of the HAP and all ERs, the RIS reflection matrix and the downlink/uplink (DL/UL) time allocation. Generally, this optimization problem is intractable to solve due to strongly coupled variables and nonconvex unit-modulus constraints. To draw more insights into this joint design, we firstly carry out feasibility analysis on this problem, and then develop a 2-block alternating optimization algorithm, which consists of the semi-closed-form solution based iterative algorithm for deriving the optimal MIMO transceivers together with the DL/UL time allocation and the alternating direction method of multipliers (ADMM) based algorithm for the RIS design. To avoid the potential high complexity of alternating optimization, we also propose a two-stage scheme, where the RIS design is independent of the others and aims to create favorable DL/UL channels. The extension of our proposed algorithms to the practical imperfect SIC case is then discussed. Numerical results illustrate the superior performance of our proposed algorithms over the baselines in terms of the achievable sum throughput, and their time effectiveness in solving large-scale problems.

Wireless powered sensor networks (WPSNs) have evolved as a promising paradigm for energy-efficient communications. Recently, the proliferation of reconfigurable intelligent surface (RIS) has further been envisioned as a cost-effective solution for improving wireless power transfer (WPT) efficiency. In this paper, from the practical perspective of balancing the network sustainability and reliability, we consider a RIS-enhanced partially WPSN that composed of wireless-powered energy receivers (ERs) and battery-powered information receivers (IRs). Assuming the partially WPSN operates in time division multiple access (TDMA) mode, the joint optimization of covariance matrices, downlink/uplink (DL/UL) time allocation and RIS reflecting coefficients are investigated under the minimum DL rate constraint among all IRs for maximizing the achievable UL sum rate. Specifically, the single-IR single-ER (SISE) case is first studied based on the assumption of separate DL/UL RIS reflecting coefficients, in which an alternating optimization algorithm is proposed with semi-closed-form optimal solutions. In order to reduce the hardware overhead and signal processing complexity, we also investigate the case of identical DL/UL RIS reflecting coefficients, in which an iterative optimization algorithm is developed to tackle the coupled DL/UL transmissions. Then, we extend our work to the multiple-IRs multiple-ERs (MIME) case, where both the optimization problems corresponding to separate and identical DL/UL RIS reflecting schemes become more challenging to solve. To circumvent this intractability, we propose a successive convex relaxation (SCA) based alternating optimization algorithm and a low-complexity two-step algorithm. Finally, numerical results demonstrate the superior UL sum rate performance of our proposed algorithms over the adopted benchmarks.

The large bandwidth at terahertz (THz) and the large number of antennas in massive MIMO result in the non-negligible spatial wideband effect in time domain or the corresponding beam squint issue in frequency domain. For a phased array based hybrid transceiver, beam squint makes the accurate beamforming an enormous challenge since an analog beamformer/combiner cannot generate frequency-dependent phase shift constitutionally. In this paper, we propose a virtual sub-array based wideband hybrid beamforming approach to eliminate the impact of beam squint. By dividing the whole array into several virtual sub-arrays, a wider beam is generated and provides an evenly distributed array gain across the whole operating frequency band. Analytical and numerical results demonstrate the effectiveness of the proposed wideband beamforming approach.

Existing deep learning (DL) based downlink channel prediction algorithms for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems mainly utilize single-source sensing information, e.g., the uplink channels, to predict the downlink channels. With the aid of multi-source sensing information (MSI) in communication systems, this paper explores deep multimodal learning (DML) technologies to improve the accuracy of downlink channel prediction. By leveraging various modality combinations and fusion levels, we design several DML based architectures for downlink channel prediction, which can also be easily extended to other communication problems like beam prediction. Simulation results demonstrate that the proposed DML based architectures can effectively exploit the constructive and complementary information of multimodal sensory data, thus achieving better performance than existing works.

Model compression without the original data for fine-tuning is challenging for deploying large-size models on resource constrained edge devices. To this end, we propose a novel data-free model compression framework based on knowledge distillation (KD), where multiple teachers are utilized in a collaborative manner to enable reliable distillation. It mainly consists of three components: adversarial data generation, multi-teacher KD, and adaptive outputs aggregation. In particular, some synthesized data are generated in an adversarial manner to mimic the original data for model compression. Then a multi-header module is developed to simultaneously leverage diverse knowledge from multiple teachers. The distillation outputs are adaptively aggregated for final prediction. The experimental results demonstrate that our framework outperforms the data-free counterpart significantly (4.48% on MNIST and 2.96% on CIFAR-10). Effectiveness of different components of our method is also verified via carefully designed ablation study.

Recent advances in deep learning bring impressive performance for multimedia applications. Hence, compressing and deploying these applications on resource-limited edge devices via model compression becomes attractive. Knowledge distillation (KD) is one of the most popular model compression techniques. However, most well-behaved KD approaches require the original dataset, which is usually unavailable due to privacy issues, while existing data-free KD methods perform much worse than data-required counterparts. In this paper, we analyze previous data-free KD methods from the data perspective and point out that using a single pre-trained model limits the performance of these approaches. We then propose a Data-Free Ensemble knowledge Distillation (DFED) framework, which contains a student network, a generator network, and multiple pre-trained teacher networks. During training, the student mimics behaviors of the ensemble of teachers using samples synthesized by a generator, which aims to enlarge the prediction discrepancy between the student and teachers. A moment matching loss term assists the generator training by minimizing the distance between activations of synthesized samples and real samples. We evaluate DFED on three popular image classification datasets. Results demonstrate that our method achieves significant performance improvements compared with previous works. We also design an ablation study to verify the effectiveness of each component of the proposed framework.

Intelligent reflecting surface (IRS) is capable of constructing the favorable wireless propagation environment by leveraging massive low-cost reconfigurable reflect array elements. In this article, we investigate the IRS-aided multiple-input-multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) for Internet-of-Things (IoT) networks, where the active base station (BS) transmits beamforming and the passive IRS reflection coefficients are jointly optimized for maximizing the minimum signal-to-interference-plus-noise ratio (SINR) among all information decoders (IDs), while maintaining the minimum total harvested energy at all energy receivers (ERs). Moreover, the IRS with practical discrete phase shifts is considered, and thereby the max-min SINR problem becomes an NP-hard combinatorial optimization problem with a strong coupling among optimization variables. To explore the insights and generality of this max-min design, both the single-ID single-ER (SISE) scenario and the multiple-IDs multiple-ERs (MIME) scenario are studied. In the SISE scenario, the classical combinatorial optimization techniques, namely, the special ordered set of type 1 (SOS1) and the reformulation-linearization (RL) technique, are applied to overcome the difficulty of this max-min design imposed by discrete optimization variables. Then, the optimal branch-and-bound algorithm and suboptimal alternating optimization algorithm are, respectively, proposed. We further extend the idea of alternating optimization to the MIME scenario. Moreover, to reduce the iteration complexity, a two-stage scheme is considered aiming to separately optimize the BS transmit beamforming and the IRS reflection coefficients. Finally, numerical simulations demonstrate the superior performance of the proposed algorithms over the benchmarks in both the two scenarios.

In this article, we investigate the analog–digital hybrid transceiver optimization for distributed Internet-of-Things (IoT) sensing networks consisting of a multiantenna fusion center (FC) and several multiantenna sensor nodes. Analog–digital hybrid transceiver is an economic way to realize tradeoffs between hardware cost and performance for multiantenna communications. Under the nonconvex unit modulus constraints and transmit power constraint at each sensor, two synchronization schemes are considered for the hybrid linear minimum mean-square error (LMMSE) transceiver optimization. First, a centralized algorithm is proposed, in which the hybrid transceivers are computed at the FC. Based on the framework of alternating direction method of multipliers (ADMMs), the unit modulus constraints can be satisfied by projecting the elements of analog transceivers onto the unit modulus circle. However, the centralized algorithm usually suffers from strict synchronous requirements and high communication overhead. In order to accommodate the inevitable computing and communication delays in distributed IoT sensing networks, an asynchronous distributed ADMM (AD-ADMM) algorithm is proposed. By using the aged information, the hybrid transceivers are computed at the sensors without the coordination of the FC. Thus, the AD-ADMM algorithm can greatly reduce the computation overhead of the FC and improve the scalability of IoT sensing networks. Simulation results are presented to show that both the centralized ADMM and AD-ADMM algorithms perform closely to the fully digital counterpart.

The combination of large bandwidth at terahertz (THz) and the large number of antennas in massive MIMO results in the non-negligible spatial wideband effect in time domain or the corresponding beam squint issue in frequency domain, which will cause severe performance degradation if not properly treated. In particular, for a phased array based hybrid transceiver, there exists a contradiction between the requirement of mitigating the beam squint issue and the hardware implementation of the analog beamformer/combiner, which makes the accurate beamforming an enormous challenge. In this paper, we propose two wideband hybrid beamforming approaches, based on the virtual sub-array and the true-time-delay (TTD) lines, respectively, to eliminate the impact of beam squint. The former one divides the whole array into several virtual sub-arrays to generate a wider beam and provides an evenly distributed array gain across the whole operating frequency band. To further enhance the beamforming performance and thoroughly address the aforementioned contradiction, the latter one introduces the TTD lines and propose a new hardware implementation of analog beamformer/combiner. This TTD-aided hybrid implementation enables the wideband beamforming and achieves the nearoptimal performance close to full-digital transceivers. Analytical and numerical results demonstrate the effectiveness of two proposed wideband beamforming approaches.

Existing work in intelligent communications has recently made preliminary attempts to utilize multi-source sensing information (MSI) to improve the system performance. However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML). In this paper, we first present complete descriptions and heuristic understandings on the framework of DML based wireless communications, where core design choices are analyzed in the view of communications. Then, we develop several DML based architectures for channel prediction in massive multiple-input multiple-output (MIMO) systems that leverage various modality combinations and fusion levels. The case study of massive MIMO channel prediction offers an important example that can be followed in developing other DML based communication technologies. Simulation results demonstrate that the proposed DML framework can effectively exploit the constructive and complementary information of multimodal sensory data to assist the current wireless communications.

The explosive deployment of the Internet of Things (IoT) brings a massive number of light-weight and energy-limited IoT devices, challenging stable wireless access. Energy-efficient, Frame Slotted Aloha (FSA) recently emerged as a promising MAC protocol for large-scale IoT systems such as Machine to Machine (M2M) and Radio Frequency Identification (RFID). Yet the stability of FSA and how to stabilize it, despite of its fundamental importance on the effective operation in practical systems, have not been systematically addressed. In order to bridge this gap, we devote this paper to designing stable FSA-based access protocol (SFP) to stabilize IoT systems. We first design an additive active node population estimation scheme and use the estimate to set frame size and participation probability for throughput optimization. We then carry out theoretical analysis demonstrating the stability of SFP in the sense of geometric ergodicity of Markov chain derived from dynamics of the active node population and its estimate. Our central theoretical result is a set of closed-form conditions on the stability of SFP. We further conduct extensive simulations whose results confirm our theoretical analysis and demonstrate the effectiveness of SFP.

Hybrid analog/digital precoding in millimeter-wave (mmWave) multi-input multi-ouput (MIMO) systems is capable of achieving the near-optimal full-digital performance at reduced hardware cost and power consumption compared to its full-RF digital counterpart. However, having numerous phase shifters is still costly, especially when the phase shifters are of high resolution. In this paper, we propose a novel twin-resolution phase-shifter network for mmWave MIMO systems, which reduces the power consumption of an entirely high-resolution network, whilst mitigating the severe array gain reduction of an entirely low-resolution network. The connections between the twin phase shifters having different resolutions and the antennas are either fixed or dynamically configured. In the latter, we jointly design the phase-shifter network and the hybrid precoding matrix, where the phase of each entry in the analog precoding matrix can be dynamically designed according to the required resolution. This method is slightly modified for the fixed network's hybrid precoding matrix. Furthermore, we extend the proposed method to multi-user MIMO systems and provide its performance analysis. Our simulation results show that the proposed dynamic hybrid precoding method strikes an attractive performance vs. power consumption trade-off.

Intelligent reflecting surfaces (IRSs) improve both the bandwidth and energy efficiency of wideband communication systems by using low-cost passive elements for reflecting the impinging signals with adjustable phase shifts. To realize the full potential of IRS-aided systems, having accurate channel state information (CSI) is indispensable, but it is challenging to acquire, since these passive devices cannot carry out transmit/receive signal processing. The existing channel estimation methods conceived for wideband IRS-aided communication systems only consider the channel’s frequency selectivity, but ignore the effect of beam squint, despite its severe performance degradation. Hence we fill this gap and conceive wideband channel estimation for IRS-aided communication systems by explicitly taking the effect of beam squint into consideration. We demonstrate that the mutual correlation function between the spatial steering vectors and the cascaded two-hop channel reflected by the IRS has two peaks, which leads to a pair of estimated angles for a single propagation path, due to the effect of beam squint. One of these two estimated angles is the frequency-independent ‘actual angle’, while the other one is the frequency-dependent ‘false angle’. To reduce the influence of false angles on channel estimation, we propose a twin-stage orthogonal matching pursuit (TS-OMP) algorithm, where the path angles of the cascaded two-hop channel reflected by the IRS are obtained in the first stage, while the propagation gains and delays are obtained in the second stage. Moreover, we propose a bespoke pilot design by exploiting the specific the characteristics of the mutual correlation function and cross-entropy theory for achieving an improved channel estimation performance. Our simulation results demonstrate the superiority of the proposed channel estimation algorithm and pilot design over their conventional counterparts.

A digital adaptive blind calibration technique for a two-channel frequency-interleaved analog-to-digital converter (2 FI-ADC) is proposed. It simultaneously estimates and corrects the inherent circuit errors, including spectral leakage, harmonic folding, jitters, I/Q and aliasing images, in a cyclic manner. The effectiveness of this novel architecture is validated via extensive simulations.

With the rapid development of the backscatter technology, backscatter-aided passive relays can be employed to improve the performance of wireless-powered device-to-device (D2D) networks. In this paper, we investigate the strategy of how the D2D pairs cooperate with each other in the network, and formulate the problem as a coalition game. In the coalition game, the players are the D2D pairs, the payoff of a D2D pair is the amount of its transmitted data, and the action is that each D2D pair can choose to stay or switch its coalition. We then develop the preference relation and the switch rule, and further design the switch algorithm for the D2D pairs to form coalitions. Moreover, we analyse the stability of the coalition game and the complexity of the proposed strategy. Simulation results demonstrate that compared with the non-cooperative strategy, our developed cooperative strategy can efficiently improve the amount of the transmitted data.

We propose a novel hybrid beamforming (BF) scheme with distance-aware multi-carrier (DAMC) modulation and beam division multiple access (BDMA) to enable massive connections in terahertz (THz) micro-scale networks. This scheme breaks a fundamental limitation in hybrid BF, i.e., the number of users that are simultaneously supported cannot exceed the number of RF chains. Some unique properties of THz channels, such as high distance-and-frequency dependence, high sparsity, and small angular spread, are exploited in this scheme. First, we propose a user grouping scheme with rough beam pre-scanning and a DAMC spectrum allocation scheme to eliminate intragroup interference. Then, we propose a wideband hybrid BF designing algorithm using the principles of BDMA to control inter-group interference. Furthermore, we propose an iterative power allocation strategy to maximize the achievable sum-rate of the network. Simulation results are presented to show that our proposed hybrid BF DAMC-BDMA scheme achieves higher sum-rate than the fully digital BF scheme in the high transmit power regime, due to the high sparsity of THz channels. Simulation results also demonstrate that our iterative power allocation strategy has strong robustness against uncertain interferences.

With the rapid development of radio-frequency identification (RFID) technology, the ever-increasing research effort has been dedicated to devising various RFID-enabled services. The missing event detection, the functionality of detecting missing objects, is one of the most important services in many Internet-of-Things applications such as inventory management. Prior detection protocols only work in single-tagged RFID systems and would waste much time on repeated checks on one object in the emerging multitagged systems where each object is attached by multiple tags, leaving efficient detection in the new scenario unaddressed. To bridge the gap, this article is devoted to detecting missing multitagged objects. The key technicality is to build a filter from a subset of tags instead of whole in prior works to avoid repeated detections of one object and reduce detection time. Specifically, we first provide a basic solution based on the Bloom filter which can specify only tags in the chosen subset to participate in the final detection. To further improve time efficiency, we propose an advanced protocol that exploits tag ID knowledge and sparsity of slots mapped by only tags in the chosen subset to build a more compact compressive filter. Moreover, a composite vector is used to efficiently coordinate tags to report its presence. We conduct theoretical analysis on optimum protocol parameters and extensive simulations to verify the feasibility of the protocols. The results show that the advanced protocol achieves more than$2\times $performance gain in terms of time efficiency over the Bloom filter-based basic protocol.

To achieve maximum throughput in named data networking (NDN) with consideration of fairness among each source-sink pair, we model the routing problem in NDN as a mixed-integer maximum concurrent flow problem. Taking advantage of the smart forwarding framework in NDN, we obtain an approximate optimal routing and forwarding solution with our improved Fleischer and random rounding (FRR) algorithm. We analyze the complexity of the Fleischer algorithm more comprehensively and give the best scaling method to achieve the least iterations. In addition, we further reduce the complexity of the Fleischer algorithm so that it iterates only once, thus getting a heuristic algorithm which is called multicommodity multipath (MCMP). Using ndnSIM-based simulation, we compare our proposed routing algorithms with the existing routing algorithms, including flooding, k-shortest path, and best single path routing, and the simulation results prove that FRR and MCMP can outperform other algorithms both in throughput and latency, and it can also be concluded that the best number of paths for k-shortest path routing should be close to and less than the average node degree of the network. For studying routing optimization with caching, we conduct simulations with different in-network cache capacity and different caching policies. The simulation results show that although the LRU can achieve the best performance under the same cache capacity, the cache capacity plays a decisive role. All routing algorithms produce better performance with caching, but the FRR still achieves the best performance, and follows by MCMP.

In this paper, we study the resource allocation and trajectory design for secure unmanned aerial vehicle (UAV)-enabled communication systems, where multiple multi-purpose UAV base stations are dispatched to provide secure communications to multiple legitimate ground users (GUs) in the existence of multiple eavesdroppers (Eves). Specifically, by leveraging orthogonal frequency division multiple access (OFDMA), active UAV base stations can communicate to their desired ground users via the assigned subcarriers while idle UAV base stations can serve as jammer simultaneously for communication security provisioning. To achieve fairness in secure communication, we maximize the average minimum secrecy rate per user by jointly optimizing the communication/jamming subcarrier allocation policy and the trajectory of UAVs, while taking into account the constraints on the minimum safety distance among multiple UAVs, the maximum cruising speed, the initial/final locations, and the existence of cylindrical no-fly zones (NFZs). The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm can significantly improve the average minimum secrecy rate compared to various baseline schemes.

Angle of Arrival (AoA) estimation with localized hybrid arrays is challenging in millimeter-wave (mmWave) communication systems. Most existing solutions quantize AoAs into limited values with relatively low accuracy. This paper presents a multi-AoA estimation scheme which is capable of estimating multiple AoAs from multiple users with low complexity. Specifically, we design a path filter via combining the received signals for each subarray. Each path filter enables a certain range of AoAs to pass through while suppressing the rest. Then we can use low-complexity cross-correlation operations to obtain continuous AoA estimates. Association of paths to users is further achieved by a follow-up pseudo-random codes based correlation operation. The scheme is first presented for a narrowband system and then extended to wideband with frequency selectivity. We also introduce new metrics and derive the lower bound of mean square error for evaluating the accuracy of AoA estimates, as conventional metrics face difficulties in the presence of multiple closely located AoAs. Extensive simulation results are provided and validate the effectiveness of the proposed multi-AoA estimation scheme.

We propose novel hybrid beamforming schemes for the terahertz (THz) wireless system where a multi-antenna base station (BS) communicates with a multi-antenna user over frequency selective fading. Here, we assume that the BS employs sub-connected hybrid beamforming and multi-carrier modulation to deliver ultra high data rate. We consider a three-dimensional wideband THz channel by incorporating the joint effect of molecular absorption, high sparsity, and multi-path fading, and consider the carrier frequency offset in multi-carrier systems. With this model, we first propose a two-stage wideband hybrid beamforming scheme which includes a beamsteering codebook searching algorithm for analog beamforming and a regularized channel inversion method for digital beamforming. We then propose a novel wideband hybrid beamforming scheme with two digital beamformers. In this scheme, an additional digital beamformer is developed to compensate for the performance loss caused by the constant-amplitude hardware constraints and the difference of channel matrices among subcarriers. Furthermore, we consider imperfect channel state information (CSI) and propose a probabilistic robust hybrid beamforming scheme to combat channel estimation errors. Numerical results demonstrate the benefits of our proposed schemes for the sake of practical implementation, especially considering its high spectral efficiency, low complexity, and robustness against imperfect CSI.

This paper investigates the joint base station (BS) association and beamforming for energy efficiency maximization in coordinated multi-cell multiuser downlink systems. In particular, we assume that only the channel distribution information is known to the BSs. The considered problem is difficult to be solved optimally due to the non-smooth and non-convex functions in the formulation. Therefore, we propose an iterative suboptimal algorithm to solve the problem efficiently based on the successive convex approximation (SCA). More specifically, the convex approximation of the original problem at each iteration can be solved efficiently by the second-order cone programming and the solution obtained by the proposed algorithm satisfies the generalized Karush-Kuhn-Tucker (KKT) conditions. To facilitate the implementation of decentralized beamforming, we transform the convex approximation problem at each iteration of the SCA into an equivalent form, which is amenable to applying the alternating direction method of multipliers (ADMM). By combining the SCA and the ADMM, a decentralized energy-efficient beamforming algorithm is proposed. Numerical results are presented to show the performance of the proposed algorithms.

By observing the fact that moving in a straight line is a common flying behavior of unmanned aerial vehicles (UAVs) in normal applications, e.g., power line inspections, and air patrols along with highway/streets/borders, in this paper we investigate the secrecy outage performance of a UAV system with linear trajectory, where a UAV (S) flies in a straight line and transmits its information over the downlink to a legitimate receiver (D) on the ground while an eavesdropping UAV (E) trying to overhear the information delivery between S and D. Meanwhile, some information is delivered to S over the uplink from D, such as commanding messages to control S's detecting operations, which can also be eavesdropped by E. The locations of S, D, and E are randomly distributed. We first characterize the statistical characteristics (including cumulative distribution functions and probability density function) of the received signal-to-noise ratio over both downlink and uplink, and then the closed form analytical expressions for the lower boundary of the secrecy outage probability of both downlink and uplink have also been derived accordingly. Finally, Monte-Carlo simulations are given to testify our proposed analytical models.

In this paper, a cooperative hybrid visible light communication (VLC)-radio frequency (RF) system with spatially random terminals is considered. Especially, a source node ($S$) with a group of light-emitting diode (LED) lamps transmits information bits to a destination ($D$), which is located out of$S$’s coverage area, via a relay node ($R$).$R$is equipped with a photodetector and an antenna to set up a VLC link between$S$and$R$, and a radio frequency (RF) link between$R$and$D$, respectively. Meanwhile, an eavesdropper ($E$) equipped with a photodetector and an antenna tries to overhear the information delivery over VLC and RF links. Also, diversity receiving scheme is considered at$E$, while the same modulation scheme is considered over both VLC and RF links. Furthermore, decode-and-forward scheme is adopted at$R$to process and forward the received VLC signals. By employing stochastic geometry, we first characterize the probability density function and cumulative distribution function of the received signal-to-noise-ratio over both VLC and RF links, while considering the randomness of the locations of both$R$,$E$, and$D$. Then, the secrecy performance of the target system is studied by deriving the approximated expressions for the secrecy outage probability under various cases. Finally, the proposed analytical models are verified via Monte-Carlo simulations.

To guarantee quality of experience (QoE) of video streaming for ground users with large obstacles that deteriorate the quality of links, unmanned aerial vehicle (UAV) is introduced in this paper as a relay to serve these users for HTTP adaptive streaming. By introducing the QoE utility model for users, we study the average QoE maximization problem in UAV-enabled relaying system by optimizing the UAV position along with the bandwidth and transmit power allocation for ground users, subject to the information causality constraint at the UAV relay. The optimization problem is formulated with a non-convex programming which is difficult to solve in general. By applying the successive convex approximation and block coordinate descent techniques, an efficient iterative algorithm is proposed to simultaneously update the UAV's position, transmit power and bandwidth allocation at each iteration, where the convergence is analyzed. Extensive simulations are conducted to show that the proposed solution can achieve significant gains for QoE in terms of the average streaming utility.

This paper investigates the time scheduling for a backscatter-aided radio-frequency-powered cognitive radio network, where multiple secondary transmitters transmit data to the same secondary gateway in the backscatter mode and the harvest-then-transmit mode. With many secondary transmitters connected to the network, the total transmission demand of the secondary transmitters may frequently exceed the transmission capacity of the secondary network. As such, the secondary gateway is more likely to assign the time resource, i.e., the backscattering time in the backscatter mode and the transmission time in the harvest-then-transmit mode, to the secondary transmitters with higher transmission valuations. Therefore, according to a variety of demand requirements from secondary transmitters, we design two auction-based time scheduling mechanisms for the time resource assignment. In the auctions, the secondary gateway acts as the seller as well as the auctioneer, and the secondary transmitters act as the buyers to bid for the time resource. We design the winner determination, the time scheduling, and the pricing schemes for both the proposed auction-based mechanisms. Furthermore, the economic properties, such as individual rationality and truthfulness, and the computational efficiency of our proposed mechanisms are analytically evaluated. The simulation results demonstrate the effectiveness of our proposed mechanisms.

We propose a novel hybrid beamforming (BF) scheme for the Terahertz (THz) wireless communication system over frequency selective channels. In the system, a multi-antenna base station which employs the sub-connected architecture adopts orthogonal frequency division multiplexing to serve a multi- antenna user. By building a wideband THz channel model, we design a beamsteering codebook searching algorithm for analog BF in which the channel state information of all subcarriers in the radio frequency domain is considered. We then design the digital BF by using the regularized channel inversion method for eliminating inter-band interference at the baseband. Numerical results demonstrate that our proposed hybrid BF scheme achieves a significant spectral efficiency advantage over the existing hybrid BF scheme which adopted the zero-forcing digital beamformer. The results also demonstrate that the spectral efficiency achieved by our proposed low-complexity scheme is very close to that achieved by the high- complexity fully digital BF scheme, especially when the average received power at the user is low.

As the ϵ4 allele of apolipoprotein E (APOE4) is proved a high risk factor of Alzheimer's disease (AD), numerous studies have used modalities of neuroimaging data to investigate the alterations of brain caused by APOE4. A recent study has shown that APOE4-related pathological changes of cortical networks during rest exist in APOE4 carriers. However, the interrelationship among the resting intrinsic networks (ICNs) has not been adequately explored. In the present study, seven ICNs were detected in both of APOE4 carriers and APOE4 non-carriers with spatial independent component analysis (ICA). Then the effective connectivity patterns of the two groups were acquired using multivariate Granger causality analysis (mGCA) the results of which reflect the information flow among ICNs. Compared with APOE4 non-carriers, significant difference in activity was found within default mode network in APOE4 carriers. Moreover, the significantly reduced intensity of causal interaction from auditory network to cerebellum network was found in APOE4 carriers. And we found the significantly causal relationship from cerebellum network to default mode network only existed in the connectivity pattern of APOE4 carriers but not in that of APOE4 non-carriers. In addition, the subcortical network and cognitive control network in APOE4 carriers were found less influenced by other ICNs. The findings in our study suggest that APOE4 indeed modulates the activities and interactions of ICNs, which might be the genetic cause of the dysfunctional process in APOE4 carriers. Our findings may shed new light on the ways how APOE4 affects the functions of nervous systems and provide a perspective to understand genetic basis of pathogenesis of AD.

In this paper, we propose an energy-efficient joint resource allocation and power control scheme for downlink multi-cell orthogonal frequency division multiple access (OFDMA) networks with imperfect channel state information (CSI). Considering the maximum allowed transmit power, the tolerable outage probability, and the fact that each resource block is allocated to at most one user in each cell, we formulate the energy-efficient joint resource allocation and power control problem as a probabilistic mixed non-convex fractional programming problem, which is hard to be tackled. In order to solve the problem efficiently, we first substitute the probabilistic constraints into the objective function and then introduce new auxiliary variables to transform the original problem into a difference of two convex functions (D.C.) programming problem. As a result, we resort to D.C. algorithm to obtain a solution satisfying Karush-Kuhn-Tucker conditions of the D.C. problem. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

Radio Tomographic Imaging (RTI) is an attractive technique for imaging the nonmetallic targets within wireless sensor network. RTI has been used in many challenging environments and situations. Due to the accuracy of Radio Tomographic Imaging system model and the interference between the wireless signals of sensors, the image obtained from the RTI system is a degraded target image, which cannot offer sufficient details to distinguish different targets. In this paper, we treat the RTI system as an image degraded process, and we propose an estimation model based on mixture Gaussian distribution to derive the degradation function from the shadowing-based RTI model. Then we use this degradation function to recover an original image by a method called constrained least squares filtering. So far, many imaging models have been proposed for localization; however, they do not have a satisfied imaging accuracy. Simulated and experimental results show that the imaging accuracy of our proposed method is improved, and the proposed method can be used in the real-time circumstances.

We propose a new linear physical-layer network coding and information combining scheme for the K-user fading multiple-access relay network (MARN), which consists of K users, one relay and one destination. The relay and the destination are connected via a rate-constraint backhaul link. In the proposed scheme, the K users transmit signals simultaneously. The relay and the destination receive the superimposed signals distorted by fading and noise. The relay reconstructs L linear combinations of the K users' messages, referred to as L network coded (NC) messages, and forwards them to the destination. The destination then attempts to recover all K users' messages by combining its received signals and the NC messages obtained from the relay. We develop an explicit expression on the selection of the coefficients of the NC messages at the relay that minimizes the end-to-end error probability at a high signal to noise ratio. We demonstrate that our proposed scheme outperforms the benchmark scheme significantly in an MARN.

In this paper, the problem of joint transmit power and bandwidth allocation over multiple channels is investigated for a secondary user in underlay mode, when partial information of interference channel is known. The target is to maximize the capacity of a secondary user under a probabilistic constraint of the interference to the primary user. A robust optimization problem is formulated, which is nondeterministic and cannot be solved directly. We then transform the original optimization problem into an equivalent convex optimization problem. For general case, an optimal solving algorithm, which is a combination of analytical and bisection-search methods, is given. For some special cases, simple and optimal solving algorithms are also devised. Numerical results are presented to show that our proposed optimal algorithm has low computation complexity when the number of channels is not large and can achieve global optimal utility in general case and our proposed simple algorithms have much lower computation complexity and can achieve global optimal utility in special cases.

In this paper, we consider the wire-tap channel model consisting of three users, namely, a transmitter, a legal receiver, and an eavesdropper, where the eavesdropper has possible unlimited centralized or distributed multiple antennas, while the legal user has just a single antenna. With this model, we study the realization of strict positive secrecy rate through Rayleigh flat fading channels using M-ary phase shift keying (PSK) and orthogonal space-time block code (OSTBC) based on channel reciprocal principle. We demonstrate that the information rate at the eavesdropper can be reduced to zero and a positive communication rate at the legal receiver can be realized if the transmitter employs a proper phase precoder based on the channel reciprocal, and uses OSTBC and M-ary PSK modulations for data transmissions. Application examples of space-time codes for positive secrecy rate are illustrated. As we have given the eavesdropper more capabilities than the legal receiver and thus captured a worst case scenario, any positive secrecy rate that is achieved serves as a lower bound on the secrecy capacity.

We propose a new linear physical-layer network coding (LPNC) and information combining scheme for the K-user fading multiple-access relay network (MARN), which consists of K users, one relay, and one destination. The relay and the destination are connected by a rate-constraint wired or wireless backhaul. In the proposed scheme, the K users transmit signals simultaneously. The relay and the destination receive the superimposed signals distorted by fading and noise. The relay reconstructs L linear combinations of the K users' messages, referred to as L network-coded (NC) messages, and forwards them to the destination. The destination then attempts to recover all K users' messages by combining its received signals and the NC messages obtained from the relay. We develop an explicit expression on the selection of the coefficients of the NC messages at the relay that minimizes the end-to-end error probability at a high signal-to-noise ratio. We develop a channel-coded LPNC scheme by using an irregular repeat-accumulate modulation code over GF(q). An iterative belief-propagation algorithm is employed to compute the NC messages at the relay, while a new algorithm is proposed for the information combining decoding at the destination. We demonstrate that our proposed scheme outperforms benchmark schemes significantly in both un-channel-coded and channel-coded MARNs.

Abstract This paper investigates the channel selection and slot time configuration in a cognitive radio network with a number of potential channels. Each channel alternates between ON state (i.e., the primary user is using the channel) and OFF state (i.e., the primary user does not use the channel), and the state evolution process is modeled as a continuous‐time Markov process. The traffic parameters (the transition rates) of the Markov process also evolve with time, modeled as a discrete‐time Markov process. A secondary user adopts a slotted structure with dynamic slot length. At each slot, the secondary user needs to determine which channel to sense and, if the channel is sensed idle, how long the slot length should be. Considering both the amount of data that the secondary user can transmit and the duration when the secondary user interferes with primary activities, a reward definition is given. Based on the reward definition, an adaptive channel selection and slot length configuration method is proposed, which includes a reward maximization procedure to maximize the achieved reward and an update procedure for the channel state belief vector and traffic parameter state belief vector. Numerical results are given to demonstrate the effectiveness and features of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

Device-free localization (DFL) based on received signal strength (RSS) measurements functions by measuring RSS variation due to the presence of the target. The accuracy of a certain localization method closely depends on the accuracy of the measurement model itself. Existing models have been found not accurate enough under certain circumstances as they cannot explain some phenomena observed in DFL practices. In light of this, we propose a new model to characterize the RSS variation, which invokes diffraction theory and regards the target as a cylinder instead of a point mass. It is observed that the proposed model agrees well with experimental measurements, particularly when the target crosses the link or is in the vicinity of the link. Since the proposed measurement model is highly nonlinear, a particle filter-based tracking method is used to generate the approximate Bayesian estimate of the target position. As a performance benchmark, we have also derived the posterior Cramér-Rao lower bound of DFL for a diffraction model. A field test has shown that the proposed diffraction model may improve the tracking accuracy at least by 45% in a single-target case and by 27% in a double-target case.

Abstract Because of the broadcast nature of the wireless communication medium, their security issues are receiving extensive concern. In this paper, we propose a spectrum‐sparse signal based on multi‐carrier modulation for secure wireless communication. The proposed signal is a summation of multiple sub‐band signals over several equally spaced carriers. The power spectrum density of the proposed signal can be reduced to a level even lower than the background noise, provided the number of sub‐bands is sufficiently large. Thus, the proposed signal possesses a favorable low probability detection property similar to that of spread spectrum signals. At the intended receiver, an under‐sampling method based on active aliasing is proposed. In this way, the intended receiver can coherently collect the transmitted signal power over all sub‐bands and extract the information reliably. We also derive a closed‐form expression for bit error rate performance of the proposed scheme in additive white Gaussian channel with binary phase shift keying modulation. Finally, numerical and simulation results to testify the effectiveness of the proposed scheme are generated for both binary phase shift keying and eight‐phase shift keying modulations in additive white Gaussian channel. Copyright © 2015 John Wiley & Sons, Ltd.

We propose and design a practical modulation-coded (MC) physical-layer network coding (PNC) scheme to approach the capacity limits of Gaussian and fading two-way relay channels (TWRCs). In the proposed scheme, an irregular repeat-accumulate (IRA) MC over GF(q) with the same random coset is employed at two users, which directly maps the message sequences into coded PAM or QAM symbol sequences. The relay chooses appropriate network coding coefficients and computes the associated finite-field linear combinations of the two users' message sequences using an iterative belief propagation algorithm. For a symmetric Gaussian TWRC, we show that, by introducing the same random coset vector at the two users and a time-varying accumulator in the IRA code, the MC-PNC scheme exhibits symmetry and permutation-invariant properties for the soft information distribution of the network-coded message sequence (NCMS). We explore these properties in analyzing the convergence behavior of the scheme and optimizing the MC to approach the capacity limit of a TWRC. For a block fading TWRC, we present a new MC linear PNC scheme and an algorithm used at the relay for computing the NCMS. We demonstrate that our developed schemes achieve near-capacity performance in both Gaussian and Rayleigh fading TWRCs. For example, our designed codes over GF(7) and GF(3) with a code rate of 3/4 are within 1 and 1.2 dB of the TWRC capacity, respectively. Our method can be regarded as a practical embodiment of the notion of compute-and-forward with a good nested lattice code, and it can be applied to a wide range of network configurations.

It is well known that conventional rate-compatible RC codes, such as Raptor codes, only perform well at long code lengths. However, we propose a class of RC codes with short code lengths in this paper. Particularly, we develop a computational approach to design online-generated RC low-density parity-check LDPC codes available on noisy channels. We first propose a diagonal-tailed encoding to generate Quasi-regular low-density generator matrix codes. Then, an optimal encoding profile for RC codes is achieved with a linear interpolation approach that is based on the fixed-rate quasi-regular LDPC codes. Finally, we evaluate the rateless and fixed-rate performances of the proposed RC codes by extensive simulation results on various code rates with different modulations. Copyright © 2013 John Wiley & Sons, Ltd.

Joint Timing and Channel Estimation (JTCE) for bandlimited long-code-aided Multi-Carrier Direct-Sequence Code Division Multiple Access (MC-DS-CDMA) systems is investigated. We establish the optimal multiuser timing and channel estimates for the uplink MC-DS-CDMA receiver by minimising a weighted least squares cost function with respect to K independent parameters, where K is the number of active users. A guided random search procedure known as Repeated Weighted Boosting Search (RWBS) is invoked for numerically solving this challenging multivariate optimisation problem, and thereby for producing near-optimal timing and channel estimates. The Cramer-Rao Lower Bound (CRLB) for the JTCE problem of interest is derived to benchmark the performance of the proposed RWBS based estimator. Quantitatively, for the scenario of K=10 users, E_b/N_0≥3 dB where E_b is the energy per bit and N_0 the single-sided noise power spectral density, and for a near-far ratio of 10 dB, the RWBS based estimator using an observation window of 20 symbols is shown to approach the CRLB at a complexity 10 orders of magnitude lower in comparison to its full maximum likelihood search based counterpart. The proposed algorithm does not require the transmission of known pilots, yet it is capable of handling time-variant channel states.

This paper considers pilot-based parameter estimation for bandlimited MC-DS-CDMA systems relying on long spreading codes. Three different schemes are proposed and compared. The two so-called unstructured algorithms, namely the Least Squares Estimator (LS-E) and the Least Absolute Shrinkage and Selection Operator Estimator (LASSOE) first estimate the composite channel impulse response, and then extract the propagation delay, amplitude and phase. By contrast, the third algorithm namely the Structured LS Search Estimator (SLSS-E) exploits the a priori knowledge of the chip waveform and directly estimates the channel parameters. Parallel interference cancelation (PIC) is incorporated in the SLSS-E for the sake of mitigating the effect of multiple access interference and hence to further improve the performance. The complexity of PIC assisted SLSS-E and LS-E only increases linearly with the number of users, with the number of subcarriers and with the length of the pilot sequence. Simulation results indicate that the PIC assisted structured estimator outperforms its unstructured counterparts.

The code timing acquisition problem of bandlimited long-code based DS-CDMA systems operating in doubly-selective time-varying multipath channels is considered. We extend the unstructured schemes proposed by Buzzi to long-code spreading, which first estimates the Symbol Weighted Composite Channel Impulse Response (SW-CCIR) vectors and then extracts the timing informations. We also propose novel structured acquisition schemes, which directly estimate the multipath propagation delays by exploiting the a priori knowledge of the chip waveform. Both the structured and unstructured schemes are blind, since they require no other a priori information but the aperiodic spreading code of the desired user. Our numerical results demonstrate that the structured acquisition significantly outperforms both its unstructured counterpart and the classic correlator-based acquisition at a reasonable complexity increase.

Abstract Soft‐decision decoding of Reed—Solomon (RS) codes consists of three key steps: multiplicity calculation, bivariate interpolation, and factorization. In this work, we investigate the first step, with the aim of reducing the complexity of multiplicity calculation. It is observed that the objective value of multiplicity calculation in Koetter and Vardy's (KV) algorithm can be expressed as an increasing function of independent parameter. Based on this fact, we propose to use bisection or golden section methods in multiplicity calculation. Simulation study demonstrates that our proposed approach can significantly reduce the computational complexity of RS codes decoding. Copyright © 2010 John Wiley & Sons, Ltd.

To improve the throughput performance without feedback channel, a partial cooperation scheme based on fountain codes is proposed in this paper. Compared with the incremental relaying where the signal is retransmitted in fixed or selective relay and fed back to the source from the destination, the proposed scheme only requires the partner to offer partial encoded packets in collaborative phase. Simulation results show that throughput performance of the proposed scheme outperforms that of incremental relaying, which has ever been considered as the optimal scheme in three-terminal cooperative communications system with the partial cooperation parameter around 0.1 in our simulation. In particular, the proposed scheme allows for an easy extension to multiple partners.

In many communication systems, it is necessary to use unequal error protection (UEP) techniques. The design issues of UEP fountain codes over the binary erasure channel (BEC) have been extensively studied in recent years. In this paper, we investigate the performance of two main classes of UEP fountain codes (i.e., UEP-LT codes and Expanding Window Fountain (EWF) codes) over the binary input additive white Gaussian noise (BIAWGN) channel. We derive analysis formulas of the UEP fountain codes under belief propagation (BP) decoding by using the "semi-Gaussian" approximation technique, and compare our results with the results proved over the BEC.

Location of a source is of considerable interest in wireless sensor networks. A novel algorithm for source location by utilizing the time-of-arrival (TOA) measurements of a signal received at spatially separated sensors under non-line-of-sight (NLOS) environments is proposed. The algorithm is based on quadratic programming, which is a special type of mathematical optimization problem. Comparisons of performance with other algorithms are made, and Monte Carlo simulations are performed. Simulation results show that the proposed algorithm gives better results.

We present a robust threshold setting strategy with modest computational complexity for frequency domain interference excision in direct sequence spread-spectrum (DSSS) commu-nication systems. The proposed strategy calculates the threshold by multiplying the minimum value of the averaged squared magnitude with a predefined scaling factor. An analytical framework for choosing the scaling factor is developed based on the principle of constant false-alarm rate (CFAR). Numerical results indicate that the new strategy outperforms existing ones in a wide range of partial-band jamming scenarios.

One of the main implementation disadvantages of a multicarrier communication system is the possibly high peak to average power ratio of the transmitted signals which cause the requirement of highly cost linear amplifiers with large dynamic range. One proposed solution is given by Haiming Wang [1] which is based on the algorithm of Hopfield neural network (HNN). Also, in our previous work [2],we demonstrated the solution based on the immune clonal selection algorithm (ICSA) which has a better performance than [1]. However, a important disadvantage of the ICSA is the need of high number of iteration. In this paper, we will show a hybrid solution which adopts both the concept of ICSA and HNN. According to the simulation results, this solution maintained the good performance of PAPR reduction, meanwhile, the number of iteration is significantly reduced.

In this paper, we present multiuser detection (MUD) technique based on a hybrid immune clonal selection algorithm (ICSA) with Hopfield neural network (HNN), for Multi-Carrier Code Division Multiple Access (MC-CDMA) communications systems. The ICSA is an effective approach for the issue of multiuser detection, however, it needs relative high iterated time to convergence. The performances of the ICSA with different parameters are studied, and the effect of parameter changing is analyzed, however adjusting these parameters cannot significantly accelerate the convergence. Then, Hopfield Neural Networks are embedded into the ICSA to improve further the affinity of the antibodies at each generation. Such a hybridization of the ICSA with the HNNs reduces its computational complexity by providing faster convergence. Simulation results are provided to show that the proposed approach can achieve near-optimal bit error rate (BER) performance with reasonable computational complexity.

Channel estimation is critical in designing broadband multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, a parametric channel estimation method based on least square (LS) criteria is proposed in this paper, and the MSE performance using optimal training pilots is also given here, which proves this estimation method can improve the estimation precision greatly in sparse channel. Since such method needs the multi-path time delays information of the channel, the probabilistic data association (PDA) method was employed to estimate the multi-path time delays. Simulation results show that both the bit error rate (BER) and the mean square error (MSE) performance of the proposed method are better than the traditional LS channel estimation method.